ALBERT

Description: Background: Graph-based modularity analysis has emerged as an important tool to study the functional organization of biological networks. However, few methods are available to study state-dependent changes in network modularity using biological activity data. We develop a weighting scheme, based on metabolic flux data, to adjust the interaction distances in a reaction-centric graph model of a metabolic network. The weighting scheme was combined with a hierarchical module assignment algorithm featuring the preservation of metabolic cycles to examine the effects of cellular differentiation and enzyme inhibitions on the functional organization of adipocyte metabolism. Results: Our analysis found that the differences between various metabolic states primarily involved the assignment of two specific reactions in fatty acid synthesis and glycerogenesis. Our analysis also identified cyclical interactions between reactions that are robust with respect to metabolic state, suggesting possible co-regulation. Comparisons based on cyclical interaction distances between reaction pairs suggest that the modular organization of adipocyte metabolism is stable with respect to the inhibition of an enzyme, whereas a major physiological change such as cellular differentiation leads to a more substantial reorganization. Conclusion: Taken together, our results support the notion that network modularity is influenced by both the connectivity of the network's components as well as the relative engagements of the connections.

Description: Background: Estrogen receptors alpha (ER) are implicated in many types of female cancers, and are the common target for anti-cancer therapy using selective estrogen receptor modulators (SERMs, such as tamoxifen). However, cell-type specific and patient-to-patient variability in response to SERMs (from suppression to stimulation of cancer growth), as well as frequent emergence of drug resistance, represents a serious problem. The molecular processes behind mixed effects of SERMs remain poorly understood, and this strongly motivates application of systems approaches. In this work, we aimed to establish a mathematical model of ER-dependent gene expression to explore potential mechanisms underlying the variable actions of SERMs. Results: We developed an equilibrium model of ER binding with 17beta-estradiol, tamoxifen and DNA, and linked it to a simple ODE model of ER-induced gene expression. The model was parameterised on the broad range of literature available experimental data, and provided a plausible mechanistic explanation for the dual agonism/antagonism action of tamoxifen in the reference cell line used for model calibration. To extend our conclusions to other cell types we ran global sensitivity analysis and explored model behaviour in the wide range of biologically plausible parameter values, including those found in cancer cells. Our findings suggest that transcriptional response to tamoxifen is controlled in a complex non-linear way by several key parameters, including ER expression level, hormone concentration, amount of ER-responsive genes and the capacity of ER-tamoxifen complexes to stimulate transcription (e.g. by recruiting co-regulators of transcription). The model revealed non-monotonic dependence of ER-induced transcriptional response on the expression level of ER, that was confirmed experimentally in four variants of the MCF-7 breast cancer cell line. Conclusions: We established a minimal mechanistic model of ER-dependent gene expression, that predicts complex non-linear effects in transcriptional response to tamoxifen in the broad range of biologically plausible parameter values. Our findings suggest that the outcome of a SERM's action is defined by several key components of cellular micro-environment, that may contribute to cell-type-specific effects of SERMs and justify the need for the development of combinatorial biomarkers for more accurate prediction of the efficacy of SERMs in specific cell types.

Description: Background: Experimental datasets are becoming larger and increasingly complex, spanning different data domains, thereby expanding the requirements for respective tool support for their analysis. Networks provide a basis for the integration, analysis and visualization of multi-omics experimental datasets. Results: Here we present VANTED (version 2), a framework for systems biology applications, which comprises a comprehensive set of seven main tasks. These range from network reconstruction, data visualization, integration of various data types, network simulation to data exploration combined with a manifold support of systems biology standards for visualization and data exchange. The offered set of functionalities is instantiated by combining several tasks in order to enable users to view and explore a comprehensive dataset from different perspectives. We describe the system as well as an exemplary workflow. Conclusions: VANTED is a stand-alone framework which supports scientists during the data analysis and interpretation phase. It is available as a Java open source tool from http://www.vanted.org.

Description: Background: Temperature strongly affects microbial growth, and many microorganisms have to deal with temperature fluctuations in their natural environment. To understand regulation strategies that underlie microbial temperature responses and adaptation, we studied glycolytic pathway kinetics in Saccharomyces cerevisiae during temperature changes. Results: Saccharomyces cerevisiae was grown under different temperature regimes and glucose availability conditions. These included glucose-excess batch cultures at different temperatures and glucose-limited chemostat cultures, subjected to fast linear temperature shifts and circadian sinoidal temperature cycles. An observed temperature-independent relation between intracellular levels of glycolytic metabolites and residual glucose concentration for all experimental conditions revealed that it is the substrate availability rather than temperature that determines intracellular metabolite profiles. This observation corresponded with predictions generated in silico with a kinetic model of yeast glycolysis, when the catalytic capacities of all glycolytic enzymes were set to share the same normalized temperature dependency. Conclusions: From an evolutionary perspective, such similar temperature dependencies allow cells to adapt more rapidly to temperature changes, because they result in minimal perturbations of intracellular metabolite levels, thus circumventing the need for extensive modification of enzyme levels.

Description: Background: Torcetrapib, a cholesteryl ester transfer protein (CETP) inhibitor which raises high-density lipoprotein (HDL) cholesterol and reduces low-density lipoprotein (LDL) cholesterol level, has been documented to increase mortality and cardiac events associated with adverse effects. However, it is still unclear the underlying mechanisms of the off-target effects of torcetrapib. Results: In the present study, we developed a systems biology approach by combining a human reassembled signaling network with the publicly available microarray gene expression data to provide unique insights into the off-target adverse effects for torcetrapib. Cytoscape with three plugins including BisoGenet, NetworkAnalyzer and ClusterONE was utilized to establish a context-specific drug-gene interaction network. The DAVID functional annotation tool was applied for gene ontology (GO) analysis, while pathway enrichment analysis was clustered by ToppFun. Furthermore, potential off-targets of torcetrapib were predicted by a reverse docking approach. In general, 10503 nodes were retrieved from the integrative signaling network and 47660 inter-connected relations were obtained from the BisoGenet plugin. In addition, 388 significantly up-regulated genes were detected by Significance Analysis of Microarray (SAM) in adrenal carcinoma cells treated with torcetrapib. After constructing the human signaling network, the over-expressed microarray genes were mapped to illustrate the context-specific network. Subsequently, three conspicuous gene regulatory networks (GRNs) modules were unearthed, which contributed to the off-target effects of torcetrapib. GO analysis reflected dramatically over-represented biological processes associated with torcetrapib including activation of cell death, apoptosis and regulation of RNA metabolic process. Enriched signaling pathways uncovered that IL-2 Receptor Beta Chain in T cell Activation, Platelet-Derived Growth Factor Receptor (PDGFR) beta signaling pathway, IL2-mediated signaling events, ErbB signaling pathway and signaling events mediated by Hepatocyte Growth Factor Receptor (HGFR, c-Met) might play decisive characters in the adverse cardiovascular effects associated with torcetrapib. Finally, a reverse docking algorithm in silico between torcetrapib and transmembrane receptors was conducted to identify the potential off-targets. This screening was carried out based on the enriched signaling network analysis. Conclusions: Our study provided unique insights into the biological processes of torcetrapib-associated off-target adverse effects in a systems biology visual angle. In particular, we highlighted the importance of PDGFR, HGFR, IL-2 Receptor and ErbB1tyrosine kinase might be direct off-targets, which were highly related to the unfavorable adverse effects of torcetrapib and worthy of being further experimental validation.

Description: Background: Lineage specific differentiation of human embryonic stem cells (hESCs) is largely mediated by specific growth factors and extracellular matrix molecules. Growth factors initiate a cascade of signals which control gene transcription and cell fate specification. There is a lot of interest in inducing hESCs to an endoderm fate which serves as a pathway towards more functional cell types like the pancreatic cells. Research over the past decade has established several robust pathways for deriving endoderm from hESCs, with the capability of further maturation. However, in our experience, the functional maturity of these endoderm derivatives, specifically to pancreatic lineage, largely depends on specific pathway of endoderm induction. Hence it will be of interest to understand the underlying mechanism mediating such induction and how it is translated to further maturation. In this work we analyze the regulatory interactions mediating different pathways of endoderm induction by identifying co-regulated transcription factors. Results: hESCs were induced towards endoderm using activin A and 4 different growth factors (FGF2 (F), BMP4 (B), PI3KI (P), and WNT3A (W)) and their combinations thereof, resulting in 15 total experimental conditions. At the end of differentiation each condition was analyzed by qRT-PCR for 12 relevant endoderm related transcription factors (TFs). As a first approach, we used hierarchical clustering to identify which growth factor combinations favor up-regulation of different genes. In the next step we identified sets of co-regulated transcription factors using a biclustering algorithm. The high variability of experimental data was addressed by integrating the biclustering formulation with bootstrap re-sampling to identify robust networks of co-regulated transcription factors. Our results show that the transition from early to late endoderm is favored by FGF2 as well as WNT3A treatments under high activin. However, induction of late endoderm markers is relatively favored by WNT3A under high activin. Conclusions: Use of FGF2, WNT3A or PI3K inhibition with high activin A may serve well in definitive endoderm induction followed by WNT3A specific signaling to direct the definitive endoderm into late endodermal lineages. Other combinations, though still feasible for endoderm induction, appear less promising for pancreatic endoderm specification in our experiments.

Description: Background: A hub protein is one that interacts with many functional partners. The annotation of hub proteins, or more generally the protein-protein interaction "degree" of each gene, requires quality genome-wide data. Data obtained using yeast two-hybrid methods contain many false positive interactions between proteins that rarely encounter each other in living cells, and such data have fallen out of favor. Results: We find that protein "stickiness", measured as network degree in ostensibly low quality yeast two-hybrid data, is a more predictive genomic metric than the number of functional protein-protein interactions, as assessed by supposedly higher quality high throughput affinity capture mass spectrometry data. In the yeast Saccharomyces cerevisiae, a protein's high stickiness, but not its high number of functional interactions, predicts low stochastic noise in gene expression, low plasticity of gene expression across different environments, and high probability of forming a homo-oligomer. Our results are robust to a multiple regression analysis correcting for other known predictors including protein abundance, presence of a TATA box and whether a gene is essential. Once the higher stickiness of homo-oligomers is controlled for, we find that homo-oligomers have noisier and more plastic gene expression than other proteins, consistent with a role for homo-oligomerization in mediating robustness. Conclusions: Our work validates use of the number of yeast two-hybrid interactions as a metric for protein stickiness. Sticky proteins exhibit low stochastic noise in gene expression, and low plasticity in expression across different environments.

Description: Background: Cell cycle process of budding yeast (Saccharomyces cerevisiae) consists of four phases: G1, S, G2 and M. Initiated by stimulation of the G1 phase, cell cycle returns to the G1 stationary phase through a sequence of the S, G2 and M phases. During the cell cycle, a cell verifies whether necessary conditions are satisfied at the end of each phase (i.e., checkpoint) since damages of any phase can cause severe cell cycle defect. The cell cycle can proceed to the next phase properly only if checkpoint conditions are met. Over the last decade, there have been several studies to construct Boolean models that capture checkpoint conditions. However, they mostly focused on robustness to network perturbations, and the timing robustness has not been much addressed. Only recently, some studies suggested extension of such models towards timing-robust models, but they have not considered checkpoint conditions. Results: To construct a timing-robust Boolean model that preserves checkpoint conditions of the budding yeast cell cycle, we used a model verification technique, 'model checking'. By utilizing automatic and exhaustive verification of model checking, we found that previous models cannot properly capture essential checkpoint conditions in the presence of timing variations. In particular, such models violate the M phase checkpoint condition so that it allows a division of a budding yeast cell into two before the completion of its full DNA replication and synthesis. In this paper, we present a timing-robust model that preserves all the essential checkpoint conditions properly against timing variations. Our simulation results show that the proposed timing-robust model is more robust even against network perturbations and can better represent the nature of cell cycle than previous models. Conclusions: To our knowledge this is the first work that rigorously examined the timing robustness of the cell cycle process of budding yeast with respect to checkpoint conditions using Boolean models. The proposed timing-robust model is the complete state-of-the-art model that guarantees no violation in terms of checkpoints known to date.

Description: Background: MicroRNAs, post-transcriptional repressors of gene expression, play a pivotal role in gene regulatorynetworks. They are involved in core cellular processes and their dysregulation is associated to a broad range ofhuman diseases. This paper focus on a minimal microRNA-mediated regulatory circuit, in which aprotein-coding gene (host gene) is targeted by a microRNA located inside one of its introns. Results: Autoregulation via intronic microRNAs is widespread in the human regulatory network, as confirmed by ourbioinformatic analysis, and can perform several regulatory tasks despite its simple topology. Our analysis,based on analytical calculations and simulations, indicates that this circuitry alters the dynamics of the hostgene expression, can induce complex responses implementing adaptation and Weber's law, and efficientlyfilters fluctuations propagating from the upstream network to the host gene. A fine-tuning of the circuitparameters can optimize each of these functions. Interestingly, they are all related to gene expressionhomeostasis, in agreement with the increasing evidence suggesting a role of microRNA regulation inconferring robustness to biological processes. In addition to model analysis, we present a list ofbioinformatically predicted candidate circuits in human for future experimental tests. Conclusions: The results presented here suggest a potentially relevant functional role for negative self-regulation via intronicmicroRNAs, in particular as a homeostatic control mechanism of gene expression. Moreover, the map ofcircuit functions in terms of experimentally measurable parameters, resulting from our analysis, can be auseful guideline for possible applications in synthetic biology.

Description: Background: Colon crypts, a single sheet of epithelia cells, consist of a periodic pattern of stem cells, transit-amplifying cells, and terminally differentiated cells that constantly renew and turnover. Experimental evidence suggests that Wnt signaling promotes and regulates stem cell division, differentiation, and possible cell migrations while intestinal BMP signaling inhibits stem cell self-renewal and repression in crypt formation. As more molecular details on Wnt and BMP in crypts are being discovered, little is still known about how complex interactions among Wnt, BMP, and different types of cells, and surrounding environments may lead to de novo formation of multiple crypts or how such interactions affect regeneration and stability of crypts. Results: We present a mathematical model that contains Wnt and BMP, a cell lineage, and their feedback regulations to study formation, regeneration, and stability of multiple crypts. The computational explorations and linear stability analysis of the model suggest a reaction-diffusion mechanism, which exhibits a short-range activation of Wnt plus a long-range inhibition with modulation of BMP signals in a growing tissue of cell lineage, can account for spontaneous formation of multiple crypts with the spatial and temporal pattern observed in experiments. Through this mechanism, the model can recapitulate some distinctive and important experimental findings such as crypt regeneration and crypt multiplication. BMP is important in maintaining stability of crypts and loss of BMP usually leads to crypt multiplication with a fingering pattern. Conclusions: The study provides a mechanism for de novo formation of multiple intestinal crypts and demonstrates a synergetic role of Wnt and BMP in regeneration and stability of intestinal crypts. The proposed model presents a robust framework for studying spatial and temporal dynamics of cell lineages in growing tissues driven by multiple signaling molecules.

Description: RNA sequencing (RNA-Seq) is rapidly replacing microarrays for profiling gene expression with much improved accuracy and sensitivity. One of the most common questions in a typical gene profiling experiment is how to identify a set of transcripts that are differentially expressed between different experimental conditions. Some of the statistical methods developed for microarray data analysis can be applied to RNA-Seq data with or without modifications. Recently several additional methods have been developed specifically for RNA-Seq data sets. This review attempts to give an in-depth review of these statistical methods, with the goal of providing a comprehensive guide when choosing appropriate metrics for RNA-Seq statistical analyses.

Description: Background: Concurrent with the efforts currently underway in mapping microbial genomes using high-throughput sequencing methods, systems biologists are building metabolic models to characterize and predict cell metabolisms. One of the key steps in building a metabolic model is using multiple databases to collect and assemble essential information about genome-annotations and the architecture of the metabolic network for a specific organism. To speed up metabolic model development for a large number of microorganisms, we need a user-friendly platform to construct metabolic networks and perform constraint-based flux balance analysis based on genome databases and experimental results. Results: We have developed a semi-automatic, web-based platform (MicrobesFlux) for generating and reconstructing metabolic models for annotated microorganisms. MicrobesFlux is able to automatically download the metabolic network (including enzymatic reactions and metabolites) of ~1,200 species from the KEGG database (Kyoto Encyclopedia of Genes and Genomes) and then convert it to a metabolic model draft. The platform also provides diverse customized tools, such as gene knockouts and the introduction of heterologous pathways, for users to reconstruct the model network. The reconstructed metabolic network can be formulated to a constraint-based flux model to predict and analyze the carbon fluxes in microbial metabolisms. The simulation results can be exported in the SBML format (The Systems Biology Markup Language). Furthermore, we also demonstrated the platform functionalities by developing a FBA model (including 229 reactions) for a recent annotated bioethanol producer, Thermoanaerobacter sp. strain X514, to predict its biomass growth and ethanol production. Conclusion: MicrobesFlux is an installation-free and open-source platform that enables biologists without prior programming knowledge to develop metabolic models for annotated microorganisms in the KEGG database. Our system facilitates users to reconstruct metabolic networks of organisms based on experimental information. Through human-computer interaction, MicrobesFlux provides users with reasonable predictions of microbial metabolism via flux balance analysis. This prototype platform can be a springboard for advanced and broad-scope modeling of complex biological systems by integrating other "omics" data or 13C- metabolic flux analysis results. MicrobesFlux is available at http://tanglab.engineering.wustl.edu/static/MicrobesFlux.html and will be continuously improved based on feedback from users.

Description: Background: In order to reduce time and efforts to develop microbial strains with better capability of producing desired bioproducts, genome-scale metabolic simulations have proven useful in identifying gene knockout and amplification targets. Constraints-based flux analysis has successfully been employed for such simulation, but is limited in its ability to properly describe the complex nature of biological systems. Gene knockout simulations are relatively straightforward to implement, simply by constraining the flux values of the target reaction to zero, but the identification of reliable gene amplification targets is rather difficult. Here, we report a new algorithm which incorporates physiological data into a model to improve the model's prediction capabilities and to capitalize on the relationships between genes and metabolic fluxes. Results: We developed an algorithm, flux variability scanning based on enforced objective flux (FVSEOF) with grouping reaction (GR) constraints, in an effort to identify gene amplification targets by considering reactions that co-carry flux values based on physiological omics data via "GR constraints". This method scans changes in the variabilities of metabolic fluxes in response to an artificially enforced objective flux of product formation. The gene amplification targets predicted using this method were validated by comparing the predicted effects with the previous experimental results obtained for the production of shikimic acid and putrescine in Escherichia coli. Moreover, new gene amplification targets for further enhancing putrescine production were validated through experiments involving the overexpression of each identified targeted gene under condition-controlled batch cultivation. Conclusions: FVSEOF with GR constraints allows identification of gene amplification targets for metabolic engineering of microbial strains in order to enhance the production of desired bioproducts. The algorithm was validated through the experiments on the enhanced production of putrescine in E. coli, in addition to the comparison with the previously reported experimental data. The FVSEOF strategy with GR constraints will be generally useful for developing industrially important microbial strains having enhanced capabilities of producing chemicals of interest.

Description: Background: Starch serves as a temporal storage of carbohydrates in plant leaves during the day/night cycles. To study the transcriptional regulatory modules of this dynamic metabolic process, we constructed a gene regulation network model using small-sample inference of graphical Gaussian model (GGM). Results: Time-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF). A starch-clock-TF gene regulation network model comprising of 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that the gene encoding for beta-amylase 3 (b-AMY3: At4g17090), which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene). The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, INDETERMINATE DOMAIN5 (AtIDD5: At2g02070) and CONSTANS-LIKE (COL: At2g21320), were observed as positive regulators of starch synthase 4 (SS4: At4g18240). The inference model of AtIDD5-dependent positive regulation of SS4 gene expression was experimentally supported by decreased SS4 mRNA accumulation in Atidd5 mutant plants during the light period of both short and long day conditions. COL was also shown to positively control SS4 mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to the deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which was significantly increased in both knockout mutant lines. Conclusions: In this study, we utilized a systematic approach of microarray analysis to discover the transcriptional regulatory network of starch metabolism in Arabidopsis leaves. With this inference method, the starch regulatory network of Arabidopsis was found to be strongly associated with clock genes and TFs, of which AtIDD5 and COL were evidenced to control SS4 gene expression and starch granule formation in chloroplast.

Description: Background: Eosinophil cationic protein is a clinical asthma biomarker that would be released into blood, especially gathered in bronchia. The signal peptide of eosinophil cationic protein (ECPsp) plays an important role in translocating ECP to the extracellular space. We previously reported that ECPsp inhibits microbial growth and regulates the expression of mammalian genes encoding tumor growth factor-alpha (TGF-alpha) and epidermal growth factor receptor (EGFR). Results: In the present study, we first generated a DNA microarray dataset, which showed that ECPsp upregulated proinflammatory molecules, including chemokines, interferon-induced molecules, and Toll-like receptors. The levels of mRNAs encoding CCL5, CXCL10, CXCL11, CXCL16, STAT1, and STAT2 were increased in the presence of ECPsp by 2.07-, 4.21-, 7.52-, 2.6-, 3.58-, and 1.67-fold, respectively. We then constructed a functional linkage network by integrating the microarray dataset with the pathway database of Kyoto Encyclopedia of Genes and Genomes (KEGG). Follow-up analysis revealed that STAT1 and STAT2, important transcriptional factors that regulate cytokine expression and release, served as hubs to connect the pathways of cytokine stimulation (TGF-alpha and EGFR pathways) and inflammatory responses. Furthermore, integrating TGF-alpha and EGFR with the functional linkage network indicated that STAT1 and STAT2 served as hubs that connect two functional clusters, including (1) cell proliferation and survival, and (2) inflammation. Finally, we found that conditioned medium in which cells that express ECPsp had been cultured could chemoattract macrophages. Experimentally, we also demonstrated that the migration of macrophage could be inhibited by the individual treatment of siRNAs of STAT1 or STAT2. Therefore, we hypothesize that ECPsp may function as a regulator for enhancing the migration of macrophages through the upregualtion of the transcriptional factors STAT1 and STAT2. Conclusion: The increased expression and release of various cytokines triggered by ECPsp may attract macrophages to bronchia to purge damaged cells. Our approach, involving experimental and computational systems biology, predicts pathways and potential biological functions for further characterization of this novel function of ECPsp under inflammatory conditions.

Description: Background: Transcription factor knockout microarrays (TFKMs) provide useful information about gene regulation. By using statistical methods for detecting differentially expressed genes between the gene expression microarray data of the mutant and wild type strains, the TF knockout targets of the knocked-out TF can be identified. However, the identified TF knockout targets may contain a certain amount of false positives due to the experimental noises inherent in the high-throughput microarray technology. Even if the identified TF knockout targets are true, the molecular mechanisms of how a TF regulates its TF knockout targets remain unknown by this kind of statistical approaches. Results: To solve these two problems, we developed a method to filter out the false positives in the original TF knockout targets (identified by statistical approaches) so that the biologically interpretable TF knockout targets can be extracted. Our method can further generate experimentally testable hypotheses of the molecular mechanisms of how a TF regulates its biologically interpretable TF knockout targets. The details of our method are as follows. First, a TF binding network was constructed using the ChIP-chip data deposited in the YEASTRACT database. Then for each original TF knockout target, it is said to be biologically interpretable if a path (in the TF binding network) from the knocked-out TF to this target could be identified by our path search algorithm. The identified path explains how the TF may regulate this target either directly by binding to its promoter or indirectly through intermediate TFs. After checking all the original TF knockout targets, the biologically interpretable ones could be extracted and the false positives could be filtered out. We validated the biological significance of our refined (i.e., biologically interpretable) TF knockout targets by assessing their functional enrichment, expression coherence, and the prevalence of protein-protein interactions. Our refined TF knockout targets outperform the original TF knockout targets across all measures. Conclusions: By jointly analyzing the TFKM and ChIP-chip data, our method can extract the biologically interpretable TF knockout targets by identifying paths (in the TF binding network) from the knocked-out TF to these targets. The identified paths form experimentally testable hypotheses regarding the molecular mechanisms of how a TF may regulate its knockout targets. About seven hundred hypotheses generated by our methods have been experimentally validated in the literature. Our work demonstrates that integrating different data sources is a powerful approach to study complex biological systems.

Description: Background: Elementary mode (EM) analysis is ideally suited for metabolic engineering as it allows for an unbiased decomposition of metabolic networks in biologically meaningful pathways. Recently, constrained minimal cut sets (cMCS) have been introduced to derive optimal design strategies for strain improvement by using the full potential of EM analysis. However, this approach does not allow for the inclusion of regulatory information. Results: Here we present an alternative, novel and simple method for the prediction of cMCS, which allows to account for boolean transcriptional regulation. We use binary linear programming and show that the design of a regulated, optimal metabolic network of minimal functionality can be formulated as a standard optimization problem, where EM and regulation show up as constraints. We validated our tool by optimizing ethanol production in E. coli. Our study showed that up to 70% of the predicted cMCS contained non-enzymatic, non-annotated reactions, which are difficult to engineer. These cMCS are automatically excluded by our approach utilizing simple weight functions. Finally, due to efficient preprocessing, the binary program remains computationally feasible. Conclusions: We used integer programming to predict efficient deletion strategies to metabolically engineer a production organism. Our formulation utilizes the full potential of cMCS but adds additional flexibility to the design process. In particular our method allows to integrate regulatory information into the metabolic design process and explicitly favors experimentally feasible deletions. Our method remains manageable even if millions or potentially billions of EM enter the analysis. We demonstrated that our approach is able to correctly predict the most efficient designs for ethanol production in E. coli.

Description: Cell and Bioscience is on track to receive its first Impact Factor in mid-2013. What is the role of the Impact Factor as a measure of a journal's success?

Description: Background: Merkel cell carcinoma (MCC) is a relatively new addition to the expanding category of oncovirus-induced cancers. Although still comparably rare, the number of cases has risen dramatically in recent years. Further complicating this trend is that MCC is an extremely aggressive neoplasm with poor patient prognosis and limited treatment options for advanced disease. The causative agent of MCC has been identified as the merkel cell polyomavirus (MCPyV). The MCPyV-encoded large T (LT) antigen is an oncoprotein that is theorized to be essential for virus-mediated tumorigenesis and is therefore, an excellent MCC antigen for the generation of antitumor immune response. As a foreign antigen, the LT oncoprotein avoids the obstacle of immune tolerance, which normally impedes the development of antitumor immunity. Ergo, it is an excellent target for anti-MCC immunotherapy. Since tumor-specific CD8+ T cells lead to better prognosis for both MCC and numerous other cancers, we have generated a DNA vaccine that is capable of eliciting LT-specific CD8+ T cells. The DNA vaccine (pcDNA3-CRT/LT) encodes the LT antigen linked to a damage-associated molecular pattern, calreticulin (CRT), as it has been demonstrated that the linkage of CRT to antigens promotes the induction of antigen-specific CD8+ T cells. Results: The present study shows that DNA vaccine-induced generation of LT-specific CD8+ T cells is augmented by linking CRT to the LT antigen. This is relevant since the therapeutic effects of the pcDNA3-CRT/LT DNA vaccine is mediated by LT-specific CD8+ T cells. Mice vaccinated with the DNA vaccine produced demonstrably more LT-specific CD8+ T cells. The DNA vaccine was also able to confer LT-specific CD8+ T cell-mediated protective and therapeutic effects to prolong the survival of mice with LT-expressing tumors. In the interest of determining the LT epitope against which most MCC-specific CD8+ T cells recognize, we identified the amino acid sequence of the immunodominant LT epitope as aa19-27 (IAPNCYGNI) and that it is H-2kb-restricted. Conclusion: The results of this study can facilitate the development of other modes of MCC treatment such as peptide-based vaccines and adoptive transfer of LT-specific CD8+ T cells. Likewise, the MCC DNA vaccine has great potential for clinical translation as the immunologic specificity is high and the treatment strategy can be exported to address other virus-induced tumors.

Description: Background: A well known example of oscillatory phenomena is the transient oscillations of glycolytic intermediates in Saccharomyces cerevisiae, their regulation being predominantly investigated by mathematical modeling. To our knowledge there has not been a genetic approach to elucidate the regulatory role of the different enzymes of the glycolytic pathway. Results: We report that the laboratory strain BY4743 could also be used to investigate this oscillatory phenomenon, which traditionally has been studied using S. cerevisiae X2180. This has enabled us to employ existing isogenic deletion mutants and dissect the roles of isoforms, or subunits of key glycolytic enzymes in glycolytic oscillations. We demonstrate that deletion of TDH3 but not TDH2 and TDH1 (encoding glyceraldehyde-3-phosphate dehydrogenase: GAPDH) abolishes NADH oscillations. While deletion of each of the hexokinase (HK) encoding genes (HXK1 and HXK2) leads to oscillations that are longer lasting with lower amplitude, the effect of HXK2 deletion on the duration of the oscillations is stronger than that of HXK1. Most importantly our results show that the presence of beta (Pfk2) but not that of alpha subunits (Pfk1) of the hetero-octameric enzyme phosphofructokinase (PFK) is necessary to achieve these oscillations. Furthermore, we report that the cAMP-mediated PKA pathway (via some of its components responsible for feedback down-regulation) modulates the activity of glycoytic enzymes thus affecting oscillations. Deletion of both PDE2 (encoding a high affinity cAMP-phosphodiesterase) and IRA2 (encoding a GTPase activating protein- Ras-GAP, responsible for inactivating Ras-GTP) abolished glycolytic oscillations. Conclusions: The genetic approach to characterising the glycolytic oscillations in yeast has demonstrated differential roles of the two types of subunits of PFK, and the isoforms of GAPDH and HK. Furthermore, it has shown that PDE2 and IRA2, encoding components of the cAMP pathway responsible for negative feedback regulation of PKA, are required for glycolytic oscillations, suggesting an enticing link between these cAMP pathway components and the glycolysis pathway enzymes shown to have the greatest role in glycolytic oscillation. This study suggests that a systematic genetic approach combined with mathematical modelling can advance the study of oscillatory phenomena.

Description: Background: Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs). As a logical model, probabilistic Boolean networks (PBNs) consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics of GRNs. This will ultimately lead to advances in developing therapeutic methods that intervene in the process of disease development and progression. The applications of PBNs, however, are hindered by the complexities involved in the computation of the state transition matrix and the steady-state distribution of a PBN. For a PBN with n genes and N Boolean networks, the complexity to compute the state transition matrix is O(nN22n) or O(nN2n) for a sparse matrix. Results: This paper presents a novel implementation of PBNs based on the notions of stochastic logic and stochastic computation. This stochastic implementation of a PBN is referred to as a stochastic Boolean network (SBN). An SBN provides an accurate and efficient simulation of a PBN without and with random gene perturbation. The state transition matrix is computed in an SBN with a complexity of O(nL2n), where L is a factor related to the stochastic sequence length. Since the minimum sequence length required for obtaining an evaluation accuracy approximately increases in a polynomial order with the number of genes, n, and the number of Boolean networks, N, usually increases exponentially with n, L is typically smaller than N, especially in a network with a large number of genes. Hence, the computational complexity of an SBN is primarily limited by the number of genes, but not directly by the total possible number of Boolean networks. Furthermore, a time-frame expanded SBN enables an efficient analysis of the steady-state distribution of a PBN. These findings are supported by the simulation results of a simplified p53 network, several randomly generated networks and a network inferred from a T cell immune response dataset. An SBN can also implement the function of an asynchronous PBN and is potentially useful in a hybrid approach in combination with a continuous or single-molecule level stochastic model. Conclusions: Stochastic Boolean networks (SBNs) are proposed as an efficient approach to modelling gene regulatory networks (GRNs). The SBN approach is able to recover biologically-proven regulatory behaviours, such as the oscillatory dynamics of the p53-Mdm2 network and the dynamic attractors in a T cell immune response network. The proposed approach can further predict the network dynamics when the genes are under perturbation, thus providing biologically meaningful insights for a better understanding of the dynamics of GRNs. The algorithms and methods described in this paper have been implemented in Matlab packages, which are attached as Additional files.

Description: Background: In the field of drug discovery, assessing the potential of multidrug therapies is a difficult task because of the combinatorial complexity (both theoretical and experimental) and because of the requirements on the selectivity of the therapy.To cope with this problem, we have developed a novel method for the systematic in silico investigation of synergistic effects of currently available drugs on genome-scale metabolic networks. The algorithm finds the optimal combination of drugs which guarantees the inhibition of an objective function, while minimizing the side effect on the overall network. Results: Two different applications are considered: finding drug synergisms for human metabolic diseases (like diabetes, obesity and hypertension) and finding antitumoral drug combinations with minimal side effect on the normal human metabolism.The results we obtain are consistent with some of the available therapeutic indications and predict some new multiple drug treatments.A cluster analysis on all possible interactions among the currently available drugs indicates a limited variety on the metabolic targets for the approved drugs. Conclusion: The in silico prediction of drug synergism can represent an important tool for the repurposing of drug in a realistic perspective which considers also the selectivty of the therapy. Moreover, for a more profitable exploitation of drug-drug interactions, also drugs which show a too low efficacy but which have a non-common mechanism of action, can be reconsider as potential ingredients of new multicompound therapeutic indications.Needless to say the clues provided by a computational study like ours need in any case to be thoroughly evaluated experimentally.

Description: Background: The use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study. This could provide biomarkers or therapeutic targets relevant for prostate cancer treatment. Results: Our findings demonstrate that a miRNA's functional role can be explained by its target protein connectivity within a physical and functional interaction network. To detect miRNAs with high influence on target protein modules, we integrated miRNA and mRNA expression profiles with a sequence based miRNA-target network and human functional and physical protein interactions (FPI). miRNAs with high influence on target protein complexes play a role in prostate cancer progression and are promising diagnostic or prognostic biomarkers. We uncovered several miRNA-regulated protein modules which were enriched in focal adhesion and prostate cancer genes. Several miRNAs such as miR-96, miR-182, and miR-143 demonstrated high influence on their target protein complexes and could explain most of the gene expression changes in our analyzed prostate cancer data set. Conclusions: We describe a novel method to identify active miRNA-target modules relevant to prostate cancer progression and outcome. miRNAs with high influence on protein networks are valuable biomarkers that can be used in clinical investigations for prostate cancer treatment.

Description: Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time. Background: There exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values.Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature. Results: Here, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions.We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) on the Boolean state space. This software, parallelized and optimized, computes the temporal evolution of probability distributions and estimates stationary distributions. Conclusions: Applications of the Boolean Kinetic Monte-Carlo are demonstrated for three qualitative models: a toy model, a published model of p53/Mdm2 interaction and a published model of the mammalian cell cycle. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations.

Description: Background: Availability of oxygen and nutrients in the coronary circulation is a crucial determinant of cardiac performance. Nutrient composition of coronary blood may significantly vary in specific physiological and pathological conditions, for example, administration of special diets, long-term starvation, physical exercise or diabetes. Quantitative analysis of cardiac metabolism from a systems biology perspective may help to a better understanding of the relationship between nutrient supply and efficiency of metabolic processes required for an adequate cardiac output. Results: Here we present CardioNet, the first large-scale reconstruction of the metabolic network of the human cardiomyocyte comprising 1793 metabolic reactions,including 560 transport processes in six compartments. We use flux-balance analysis to demonstrate the capability of the network to accomplish a set of 368 metabolic functions required for maintaining the structural and functional integrity of the cell.Taking the maintenance of ATP, biosynthesis of ceramide, cardiolipin and further important phospholipids as examples, we analyse how a changed supply of glucose, lactate, fatty acids and ketone bodies may influence the efficiency of these essential processes. Conclusions: CardioNet is a functionally validated metabolic network of the human cardiomyocyte that enables theorectical studies of cellular metabolic processescrucial for the accomplishment of an adequate cardiac output.

Description: Background: Annually, influenza A viruses circulate the world causing wide-spread sickness, economic loss, and death. One way to better defend against influenza virus-induced disease may be to develop novel host-based therapies, targeted at mitigating viral pathogenesis through the management of virus-dysregulated host functions. However, mechanisms that govern aberrant host responses to influenza virus infection remain incompletely understood. We previously showed that the pandemic H1N1 virus influenza A/California/04/2009 (H1N1; CA04) has enhanced pathogenicity in the lungs of cynomolgus macaques relative to a seasonal influenza virus isolate (A/Kawasaki/UTK-4/2009 (H1N1; KUTK4)). Results: Here, we used microarrays to identify host gene sequences that were highly differentially expressed (DE) in CA04-infected macaque lungs, and we employed a novel strategy -- combining functional and pathway enrichment analyses, transcription factor binding site enrichment analysis and protein-protein interaction data -- to create a CA04 differentially regulated host response network. This network describes enhanced viral RNA sensing, immune cell signaling and cell cycle arrest in CA04-infected lungs, and highlights a novel, putative role for the MYC-associated zinc finger (MAZ) transcription factor in regulating these processes. Conclusions: Our findings suggest that the enhanced pathology is the result of a prolonged immune response, despite successful virus clearance. Most interesting, we identify a mechanism which normally suppresses immune cell signaling and inflammation is ineffective in the pH1N1 virus infection; a dyregulatory event also associated with arthritis. This dysregulation offers several opportunities for developing strain-independent, immunomodulatory therapies to protect against future pandemics.

Description: Background: Cell-to-cell variability in protein expression can be large, and its propagation through signaling networks affects biological outcomes. Here, we apply deterministic and probabilistic models and biochemical measurements to study how network topologies and cell-to-cell protein abundance variations interact to shape signaling responses. Results: We observe bimodal distributions of extracellular signal-regulated kinase (ERK) responses to epidermal growth factor (EGF) stimulation, which are generally thought to indicate bistable or ultrasensitive signaling behavior in single cells. Surprisingly, we find that a simple MAPK/ERK-cascade model with negative feedback that displays graded, analog ERK responses at a single cell level can explain the experimentally observed bimodality at the cell population level. Model analysis suggests that a conversion of graded input--output responses in single cells to digital responses at the population level is caused by a broad distribution of ERK pathway activation thresholds brought about by cell-to-cell variability in protein expression. Conclusions: Our results show that bimodal signaling response distributions do not necessarily imply digital (ultrasensitive or bistable) single cell signaling, and the interplay between protein expression noise and network topologies can bring about digital population responses from analog single cell dose responses. Thus, cells can retain the benefits of robustness arising from negative feedback, while simultaneously generating population-level on/off responses that are thought to be critical for regulating cell fate decisions.

Description: Background: The search for new drug targets for antibiotics against Plasmodium falciparum, a majorcause of human deaths, is a pressing scientific issue, as multiple resistance strains spreadrapidly. Metabolic network-based analyses may help to identify those parasite's essentialenzymes whose homologous counterparts in the human host cells are either absent,non-essential or relatively less essential. Results: Using the well-curated metabolic networks PlasmoNet of the parasite Plasmodiumfalciparum and HepatoNet1 of the human hepatocyte, the selectivity of 48 experimentalantimalarial drug targets was analyzed. Applying in silico gene deletions, 24 of these drugtargets were found to be perfectly selective, in that they were essential for the parasite butnon-essential for the human cell. The selectivity of a subset of enzymes, that were essentialin both models, was evaluated with the reduced fitness concept. It was, then, possible toquantify the reduction in functional fitness of the two networks under the progressiveinhibition of the same enzymatic activity. Overall, this in silico analysis provided aselectivity ranking that was in line with numerous in vivo and in vitro observations. Conclusions: Genome-scale models can be useful to depict and quantify the effects of enzymaticinhibitions on the impaired production of biomass components. From the perspective of ahost-pathogen metabolic interaction, an estimation of the drug targets-inducedconsequences can be beneficial for the development of a selective anti-parasitic drug.

Description: Background: Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results: We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in-silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters. Furthermore, in both the in silico and experimental case studies, the predicted gene expression profiles are in very close agreement with the dynamics of the input data. Conclusions: Our integer programming algorithm effectively utilizes bootstrapping to identify robust gene regulatory networks from noisy, non-linear time-series gene expression data. With significant noise and non-linearities being inherent to biological systems, the present formulism, with the incorporation of network sparsity, is extremely relevant to gene regulatory networks, and while the formulation has been validated against in silico and E. Coli data, it can be applied to any biological system.

Description: Background: Predicting a system's behavior based on a mathematical model is a primary task in Systems Biology. If the model parameters are estimated from experimental data, the parameter uncertainty has to be translated into confidence intervals for model predictions. For dynamic models of biochemical networks, the nonlinearity in combination with the large number of parameters hampers the calculation of prediction confidence intervals and renders classical approaches as hardly feasible. Results: In this article reliable confidence intervals are calculated based on the prediction profile likelihood. Such prediction confidence intervals of the dynamic states can be utilized for a data-based observability analysis. The method is also applicable if there are non-identifiable parameters yielding to some insufficiently specified modelpredictions that can be interpreted as non-observability. Moreover, a validation profile likelihood is introduced that should be applied when noisy validation experiments are to be interpreted. Conclusions: The presented methodology allows the propagation of uncertainty from experimental to model pre-dictions. Although presented in the context of ordinary differential equations, the concept is general and also applicable to other types of models. Matlab code which can be used as a template to implement the method is provided at http://www.fdmold.uni-freiburg.de/~ckreutz/PPL .

Description: The topological analysis of biological networks has been a prolific topic in network science during the last decade. A persistent problem with this approach is the inherent uncertainty and noisy nature of the data. One of the cases in which this situation is more marked is that of transcriptional regulatory networks (TRNs) in bacteria. The datasets are incomplete because regulatory pathways associated to a relevant fraction of bacterial genes remain unknown. Furthermore, direction, strengths and signs of the links are sometimes unknown or simply overlooked. Finally, the experimental approaches to infer the regulations are highly heterogeneous, in a way that induces the appearance of systematic experimental-topological correlations. And yet, the quality of the available data increases constantly. In this work we capitalize on these advances to point out the influence of data (in)completeness and quality on some classical results on topological analysis of TRNs, specially regarding modularity at different levels. In doing so, we identify the most relevant factors affecting the validity of previous findings, highlighting important caveats to future prokaryotic TRNs topological analysis.

Description: Background: Extracellular feedback is an abundant module of intercellular communication networks, yet a detailed understanding of its role is still lacking. Here, we study interactions between polyclonal activated T cells that are mediated by IL-2 extracellular feedback as a model system. Results: Using mathematical modeling we show that extracellular feedback can give rise to opposite outcomes: competition or cooperation between interacting T cells, depending on their relative levels of activation. Furthermore, the outcome of the interaction also depends on the relative timing of activation of the cells. A critical time window exists after which a cell that has been more strongly activated nevertheless cannot exclude an inferior competitor. Conclusions: In a number of experimental studies of polyclonal T-cell systems, outcomes ranging from cooperation to competition as well as time dependent competition were observed. Our model suggests that extracellular feedback can contribute to these observed behaviors as it translates quantitative differences in T cells' activation strength and in their relative activation time into qualitatively different outcomes. We propose extracellular feedback as a general mechanism that can balance speed and accuracy -- choosing the most suitable responders out of a polyclonal population under the clock of an escalating threat.

Description: Background: Hypoxia is associated with many disease conditions in humans, such as cancer, stroke and traumatic injuries. Hypoxia elicits broad molecular and cellular changes in diverse eukaryotes. Our recent studies suggest that one likely mechanism mediating such broad changes is through changes in the cellular localization of important regulatory proteins. Particularly, we have found that over 120 nuclear proteins with important functions ranging from transcriptional regulation to RNA processing exhibit altered cellular locations under hypoxia. In this report, we describe further experiments to identify and evaluate the role of nuclear protein relocalization in mediating hypoxia responses in yeast. Results: To identify regulatory proteins that play a causal role in mediating hypoxia responses, we characterized the time courses of relocalization of hypoxia-altered nuclear proteins in response to hypoxia and reoxygenation. We found that 17 nuclear proteins relocalized in a significantly shorter time period in response to both hypoxia and reoxygenation. Particularly, several components of the SWI/SNF complex were fast responders, and analysis of gene expression data show that many targets of the SWI/SNF proteins are oxygen regulated. Furthermore, confocal fluorescent live cell imaging showed that over 95% of hypoxia-altered SWI/SNF proteins accumulated in the cytosol in hypoxic cells, while over 95% of the proteins were nuclear in normoxic cells, as expected. Conclusions: SWI/SNF proteins relocalize in response to hypoxia and reoxygenation in a quick manner, and their relocalization likely accounts for, in part or in whole, oxygen regulation of many SWI/SNF target genes.

Description: No description available

Description: Background: Stochastic fluctuations in molecular numbers have been in many cases shown to be crucialfor the understanding of biochemical systems. However, the systematic study of thesefluctuations is severely hindered by the high computational demand of stochasticsimulation algorithms. This is particularly problematic when, as is often the case, some ormany model parameters are not well known. Here, we propose a solution to this problem,namely a combination of the linear noise approximation with optimisation methods. Thelinear noise approximation is used to efficiently estimate the covariances of particlenumbers in the system. Combining it with optimisation methods in a closed-loop to findextrema of covariances within a possibly high-dimensional parameter space allows us toanswer various questions. Examples are, what is the lowest amplitude of stochasticfluctuations possible within given parameter ranges? Or, which specific changes ofparameter values lead to the increase of the correlation between certain chemical species?Unlike stochastic simulation methods, this has no requirement for small numbers ofmolecules and thus can be applied to cases where stochastic simulation is prohibitive. Results: We implemented our strategy in the software COPASI and show its applicability on twodifferent models of mitogen-activated kinases (MAPK) signalling--one generic model ofextracellular signal-regulated kinases (ERK) and one model of signalling via p38 MAPK.Using our method we were able to quickly find local maxima of covariances betweenparticle numbers in the ERK model depending on the activities of phospho-MKKK and itscorresponding phosphatase. With the p38 MAPK model our method was able toefficiently find conditions under which the coefficient of variation of the output of thesignalling system, namely the particle number of Hsp27, could be minimised. We alsoinvestigated correlations between the two parallel signalling branches (MKK3 andMKK6) in this model. Conclusions: Our strategy is a practical method for the efficient investigation of fluctuations inbiochemical models even when some or many of the model parameters have not yet beenfully characterised.

Description: The skeleton of complex systems can be represented as networks where vertices represent entities, and edges represent the relations between these entities. Often it is impossible, or expensive, to determine the network structure by experimental validation of the binary interactions between every vertex pair. It is usually more practical to infer the network from surrogate observations. Network inference is the process by which an underlying network of relations between entities is determined from indirect evidence. While many algorithms have been developed to infer networks from quantitative data, less attention has been paid to methods which infer networks from repeated observations of related sets. This type of data is ubiquitous in the field of systems biology and in other areas of complex systems research, hence such methods would be of great utility and value.Here we present a general method for network inference from repeated observations of sets of related entities. Given experimental observations of such sets, we infer the underlying network connecting these entities by generating an ensemble of networks consistent with the data. The frequency of occurrence of a given link throughout this ensemble is interpreted as the probability that the link is present in the underlying real network conditioned on the data. Exponential random graphs are used to generate and sample the ensemble of consistent networks, and we take an algorithmic approach to numerically executing the inference method. The effectiveness of the method is demonstrated on synthetic data before employing this inference approach to problems in systems biology and systems pharmacology, as well as to construct a co-authorship collaboration network. We predict direct protein-protein interactions from high-throughput mass-spectrometry proteomics; build networks that connect pluripotency regulators based on ChIP-seq and loss-of-function/gain-of-function followed by expression data; extract a network that connects 53 cancer drugs to each other and to 34 severe adverse events by mining the FDA's Adverse Events Reporting Systems (AERS); and construct a co-authorship network that connects Mount Sinai School of Medicine investigators. The predicted networks and online software to create networks from entity-set libraries are provided online at http://www.maayanlab.net/S2N. As empirical data about sets of related entities accrues, there are more constraints on possible network realizations that can fit the data; in the language of statistical mechanics, the size of the microstate ensemble shrinks, until the underlying network resolves. The network inference method presented here can be applied to resolve different types of networks in current systems biology and systems pharmacology as well as in other fields of research.

Description: Background: Mathematical modelling has become a standard technique to improve our understanding of complex biological systems. As models become larger and more complex, simulations and analyses require increasing amounts of computational power. Clusters of computers in a high-throughput computing environment can help to provide the resources required for computationally expensive model analysis. However, exploiting such a system can be difficult for users without the necessary expertise. Results: We present Condor-COPASI, a server-based software tool that integrates COPASI, a biological pathway simulation tool, with Condor, a high-throughput computing environment. Condor-COPASI provides a web-based interface, which makes it extremely easy for a user to run a number of model simulation and analysis tasks in parallel. Tasks are transparently split into smaller parts, and and submitted for execution on a Condor pool. Result output is presented to the user in a number of formats, including tables and interactive graphical displays. Conclusions: Condor-COPASI can effectively use a Condor high-throughput computing environment to provide significant gains in performance for a number of model simulation and analysis tasks. Condor-COPASI is free, open source software, released under the Artistic License 2.0, and is suitable for use by any institution with access to a Condor pool. Source code is freely available for download at http://code.google.com/p/condor-copasi/, along with full instructions on deployment and usage.

Description: Background: Genomic datasets generated by new technologies are increasingly prevalent in disparate areas of biological research. While many studies have sought to characterize relationships among genomic features, commensurate efforts to characterize relationships among biological samples have been less common. Consequently, the full extent of sample variation in genomic studies is often under-appreciated, complicating downstream analytical tasks such as gene co-expression network analysis. Results: Here we demonstrate the use of network methods for characterizing sample relationships in microarray data generated from human brain tissue. We describe an approach for identifying outlying samples that does not depend on the choice or use of clustering algorithms. We introduce a battery of measures for quantifying the consistency and integrity of sample relationships, which can be compared across disparate studies, technology platforms, andbiological systems. Among these measures, we provide evidence that the correlation between the connectivity and the clustering coefficient (two important network concepts) is a sensitive indicator of homogeneity among biological samples. We also show that this measure, which we refer to as cor(K,C), can distinguish biologically meaningful relationships among subgroups of samples. Specifically, we find that cor(K,C) reveals the profound effect of Huntington's disease on samples from the caudate nucleus relative to other brain regions. Furthermore, we find that this effect is concentrated in specific modules of genes that are 2 naturally co-expressed in human caudate nucleus, highlighting a new strategy for exploring the effects of disease on sets of genes. Conclusions: These results underscore the importance of systematically exploring sample relationships in large genomic datasets before seeking to analyze genomic feature activity. We introduce a standardized platform for this purpose using freely available R software that has been designed to enable iterative and interactive exploration of sample networks.

Description: Background: Integration of metabolic pathways resources and metabolic network models, and deploying new tools on the integrated platform can help perform more effective and more efficient systems biology research on understanding the regulation of metabolic networks. Therefore, the tasks of (a) integrating under a single database environment regulatory metabolic networks and existing models, and (b) building tools to help with modeling and analysis aredesirable and intellectually challenging computational tasks. Results: PathCase Systems Biology (PathCase-SB) is built and released. This paper describes PathCase-SB user interfaces developed to date. The current PathCase-SB system provides a database-enabled framework and web-based computational tools towards facilitating the development of kinetic models for biological systems. PathCase-SB aims to integrate systems biology models data and metabolic network data of selected biological data sources on the web (currently, BioModels Database and KEGG, respectively), and to provide more powerful and/or new capabilities via the new web-based integrative framework. Conclusions: Each of the current four PathCase-SB interfaces, namely, Browser, Visualization, Querying, and Simulation interfaces, have expanded and new capabilities as compared with the original data sources. PathCase-SB is already available on the web and being used by researchers across the globe.

Description: Background: CD4+T cells have several subsets of functional phenotypes, which play critical yet diverse roles in the immune system. Pathogen-driven differentiation of these subsets of cells is often heterogeneous in terms of the induced phenotypic diversity. In vitro recapitulation of heterogeneous differentiation under homogeneous experimental conditions indicates some highly regulated mechanisms by which multiple phenotypes of CD4+T cells can be generated from a single population of naive CD4+T cells. Therefore, conceptual understanding of induced heterogeneous differentiation will shed light on the mechanisms controlling the response of populations of CD4+T cells under physiological conditions. Results: We present a simple theoretical framework to show how heterogeneous differentiation in a two-master-regulator paradigm can be governed by a signaling network motif common to all subsets of CD4+T cells. With this motif, a population of naive CD4+T cells can integrate the signals from their environment to generate a functionally diverse population with robust commitment of individual cells. Notably, two positive feedback loops in this network motif govern three bistable switches, which in turn, give rise to three types of heterogeneous differentiated states, depending upon particular combinations of input signals. We provide three prototype models illustrating how to use this framework to explain experimental observations and make specific testable predictions. Conclusions: The process in which several types of T helper cells are generated simultaneously to mount complex immune responses upon pathogenic challenges can be highly regulated, and a simple signaling network motif can be responsible for generating all possible types of heterogeneous populations with respect to a pair of master regulators controlling CD4+T cell differentiation. The framework provides a mathematical basis for understanding the decisionmaking mechanisms of CD4+T cells, and it can be helpful for interpreting experimental results. Mathematical models based on the framework make specific testable predictions that may improve our understanding of this differentiation system.

Description: Background: Statistical approaches to describing the behaviour, including the complex relationships between input parameters and model outputs, of nonlinear dynamic models (referred to as metamodelling) are gaining more and more acceptance as a means for sensitivity analysis and to reduce computational demand. Understanding such input-output maps is necessary for efficient model construction and validation. Multi-way metamodelling provides the opportunity to retain the block-wise structure of the temporal data typically generated by dynamic models throughout the analysis. Furthermore, a cluster-based approach to regional metamodelling allows description of highly nonlinear input-output relationships, revealing additional patterns of covariation. Results: By presenting the N-way Hierarchical Cluster-based Partial Least Squares Regression (N-way HC-PLSR) method, we here combine multi-way analysis with regional cluster-based metamodelling, together making a powerful methodology for extensive exploration of the input-output maps of complex dynamic models. We illustrate the potential of the N-way HC-PLSR by applying it both to predict model outputs as functions of the input parameters, and in the inverse direction (predicting input parameters from the model outputs), to analyse the behaviour of a dynamic model of the mammalian circadian clock. Our results display a more complete cartography of how variation in input parameters is reflected in the temporal behaviour of multiple model outputs than has been previously reported. Conclusions: Our results indicated that the N-way HC-PLSR metamodelling provides a gain in insight into which parameters that are related to a specific model output behaviour, as well as variations in the model sensitivity to certain input parameters across the model output space. Moreover, the N-way approach allows a more transparent and detailed exploration of the temporal dimension of complex dynamic models, compared to alternative 2-way methods.

Description: Background: Thyroid hormone (T3) is important for adult organ function and vertebrate development. Amphibian metamorphosis is totally dependent on T3 and offers a unique opportunity to study how T3 controls postembryonic development in vertebrates. Earlier studies have demonstrated that TR mediates the metamorphic effects of T3 in Xenopus laevis. Liganded TR recruits histone modifying coactivator complexes to target genes during metamorphosis. This leads to nucleosomal removal and histone modifications, including methylation of histone H3 lysine (K) 79, in the promoter regions, and the activation of T3-inducible genes. Results: We show that Dot1L, the only histone methyltransferase capable of methylating H3K79, is directly regulated by TR via binding to a T3 response element in the promoter region during metamorphosis in Xenopus tropicalis, a highly related species of Xenopus laevis. We further show that Dot1L expression in both the intestine and tail correlates with the transformation of the organs. Conclusions: Our findings suggest that TR activates Dot1L, which in turn participates in metamorphosis through a positive feedback to enhance H3K79 methylation and gene activation by liganded TR.

Description: Background: Transcription factors (TFs) have long been known to be principally activators of transcription in eukaryotes and prokaryotes. The growing awareness of the ubiquity of microRNAs (miRNAs) as suppressive regulators in eukaryotes, suggests the possibility of a mutual, preferential, self-regulatory connectivity between miRNAs and TFs. Here we investigate the connectivity from TFs and miRNAs to other genes and each other using text-mining, TF promoter binding site and 6 different miRNA binding site prediction methods. Results: In the first approach text-mining of PubMed abstracts reveal statistically significant associations between miRNAs and both TFs and signal transduction gene classes. Secondly, prediction of miRNA targets in human and mouse 3'UTRs show enrichment only for TFs but not consistently across prediction methods for signal transduction or other gene classes. Furthermore, a random sample of 986 TarBase entries was scored for experimental evidence by manual inspection of the original papers, and enrichment for TFs was observed to increase with score. Low-scoring Tarbase entries, where experimental evidence is anticorrelated miRNA:mRNA expression with predicted miRNA targets, appear not to select for real miRNA targets to any degree. Our manually validated text-mining results also suggests that miRNAs may be activated by more TFs than other classes of genes, as 7% of miRNA:TF co-occurrences in the literature were TFs activating miRNAs. This was confirmed when thirdly, we found enrichment for predicted, conserved TF binding sites in miRNA and TF genes compared to other gene classes. Conclusions: We see enrichment of connections between miRNAs and TFs using several independent methods, suggestive of a network of mutual activating and suppressive regulation. We have also built regulatory networks (containing 2- and 3-loop motifs) for mouse and human using predicted miRNA and TF binding sites and we have developed a web server to search and display these loops, available for the community at http://rth.dk/resources/tfmirloop.

Description: Background: Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed for predicting essential proteins by using the topological features of protein-protein interaction (PPI) networks. However, most of these methods ignored intrinsic biological meaning of proteins. Moreover, PPI data contains many false positives and false negatives. To overcome these limitations, recently many research groups have started to focus on identification of essential proteins by integrating PPI networks with other biological information. However, none of their methods has widely been acknowledged. Results: By considering the facts that essential proteins are more evolutionarily conserved than nonessential proteins and essential proteins frequently bind each other, we propose an iteration method for predicting essential proteins by integrating the orthology with PPI networks, named by ION. Differently from other methods, ION identifies essential proteins depending on not only the connections between proteins but also their orthologous properties and features of their neighbors. ION is implemented to predict essential proteins in S. cerevisiae. Experimental results show that ION can achieve higher identification accuracy than eight other existing centrality methods in terms of area under the curve (AUC). Moreover, ION identifies a large amount of essential proteins which have been ignored by eight other existing centrality methods because of their low-connectivity. Many proteins ranked in top 100 by ION are both essential and belong to the complexes with certain biological functions. Furthermore, no matter how many reference organisms were selected, ION outperforms all eight other existing centrality methods. While using as many as possible reference organisms can improve the performance of ION. Additionally, ION also shows good prediction performance in E.Coli K-12. Conclusions: The accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks.

Description: Human Immunodeficiency Virus Type 1 (HIV-1) protease inhibitors (PIs) are the most potent class of drugs in antiretroviral therapies. However, viral drug resistance to PIs could emerge rapidly thus reducing the effectiveness of those drugs. Of note, all current FDA-approved PIs are competitive inhibitors, i.e., inhibitors that compete with substrates for the active enzymatic site. This common inhibitory approach increases the likelihood of developing drug resistant HIV-1 strains that are resistant to many or all current PIs. Hence, new PIs that move away from the current target of the active enzymatic site are needed. Specifically, allosteric inhibitors, inhibitors that block HIV-1 protease active site, should be sought. Another common feature of current PIs is they were all developed based on the structure-based design. Drugs derived from a structure-based strategy may generate target specific and potent inhibitors. However, this type of drug design can only target one site at a time and drugs discovered by this method are often associated with strong side effects such as cellular toxicity, limiting its number of target choices, efficacy, and applicability. In contrast, a cell-based system may provide a useful alternative strategy that can overcome many of the inherited shortcomings associated with structure-based drug designs. For example, allosteric PIs can be sought using a cell-based system without considering the site or mechanism of inhibition. In addition, a cell-based system can eliminate those PIs that have strong cytotoxic effect. Most importantly, a simple, economical, and easy-to-maintained eukaryotic cellular system such as yeast will allow us to search for potential PIs in a large-scaled high throughput screening (HTS) system, thus increasing the chance of success. Based on our many years of experience in using fission yeast as a model system to study HIV-1 Vpr, we propose the use of fission yeast as a possible surrogate system to study the effects of HIV-1 protease on cellular functions and to explore its utility as a HTS system to search for new PIs to battle HIV-1 strains resistant to the current PI drugs.

Description: Background: In the functional genomics analysis domain, various methodologies are available for interpreting the results produced by high-throughput biological experiments. These methods commonly use a list of genes as an analysis input, and most of them produce a more complicated list of genes or pathways as the results of the analysis. Although there are several network-based methods, which detect key nodes in the network, the results tend to include well-studied, major hub genes. Results: To mine the molecules that have biological meaning but to fewer degrees than major hubs, we propose, in this study, a new network-based method for selecting these hidden key molecules based on virtual information flows circulating among the input list of genes. The human biomolecular network was constructed from the Pathway Commons database, and a calculation method based on betweenness centrality was newly developed. We validated the method with the ErbB pathway and applied it to practical cancer research data. We were able to confirm that the output genes, despite having fewer edges than major hubs, have biological meanings that were able to be invoked by the input list of genes. Conclusions: The developed method, named NetHiKe (Network-based Hidden Key molecule miner), was able to detect potential key molecules by utilizing the human biomolecular network as a knowledge base. Thus, it is hoped that this method will enhance the progress of biological data analysis in the whole-genome research era.

Description: Background: Site-specific Transcription Factors (TFs) are proteins that bind to specific sites on the DNA and control the activity of a target gene by enhancing or decreasing the rate at which the gene is transcribed by RNA polymerase. The process by which TF molecules locate their target sites is a key component of transcriptional regulation. Therefore it is essential to gain insight into the mechanisms by which TFs search for the target sites.Research in this area uses experimental and analytical approaches, but also stochastic simulations of the search process. Previous work based on stochastic simulations focussed only on short sequences, primarily for reasons of technical feasibility. Many of these studies had to disregard possible biases introduced by reducing a genome-wide system to a smaller subsystem. In particular, we identified crucial parameters that require adjustment, which were not adequately changed in these previous studies. Results: We investigated several methods that adequately adapt the parameters of stochastic simulations of the facilitated diffusion, when the full sequence space is reduced to smaller regions of interest. We found two methods that scale the system accordingly: the copy number model and the association rate model. We systematically compared the results produced by simulations of the subsystem with respect to the original system. Our results confirmed that the copy number model is adequate only for high abundance TFs, while for low abundance TFs the association rate model is the only one that reproduces with high accuracy the results of the full system. Conclusions: We propose a strategy to reduce the size of the system that adequately adapts important parameters to capture the behaviour of the full system. This enables correct simulations of a smaller sequence space (which can be as small as 100 Kbp) and, thus, provides independence from computationally intensive genome-wide simulations of the facilitated diffusion mechanism.Research in this area uses experimental and analytical approaches, but also stochastic simulations of the search process. Previous work based on stochastic simulations focussed only on short sequences, primarily for reasons of technical feasibility. Specifically, many of these studies had to disregard possible biases introduced by reducing a genome-wide system to a smaller subsystem. In addition, we identified crucial parameters that require adjustment, which were not adequately changed in these previous studies. Results: We investigated several methods that adequately adapt the parameters of stochastic simulations of the facilitated diffusion, when the full sequence space is reduced to smaller regions of interest. We found two methods that scale the system accordingly: the (copy number model and the association rate model). We systematically compared the results produced by simulations of the subsystem with respect to the original system. Our results confirmed that the copy number model is adequate only for abundant TFs, while for low abundant TFs the association rate model is the only one that reproduces with high accuracy the results of the full system. Conclusions: We propose a strategy to reduce the size of the analysed system in stochastic simulations of facilitated diffusion that adequately adapts important parameters to capture the behaviour of the full system. This enables correct simulations of a smaller sequence space (which can be as small as 100 Kbp) and thus provides independence from computationally intensive genome-wide simulations of the facilitated diffusion mechanism.

Description: Golgi phosphoprotein 2 (GOLPH2, also termed GP73 and GOLM1) is a type II transmembrane protein residing in the cis and medial-Golgi cisternae. GOLPH2 is predominantly expressed in the epithelial cells of many human tissues. Under poorly defined circumstances, GOLPH2 can be cleaved and released to the extracellular space. Despite of its relatively "young age" since the first description in 2000, the physiological and pathological roles of GOLPH2 have been the subject that has attracted considerable amount of attention in recent years. Here, we review the history of GOLPH2's discovery and the multitude of studies by many groups around the world aimed at understanding its molecular, cellular, physiological, and pathogenic activities in various settings.

Description: Background: The genome is continuously attacked by a variety of agents that cause DNA damage. Recognition of DNA lesions activates the cellular DNA damage response (DDR), which comprises a network of signal transduction pathways to maintain genome integrity. In response to severe DNA damage, cells undergo apoptosis to avoid transformation into tumour cells, or alternatively, the cells enter permanent cell cycle arrest, called senescence. Most tumour cells have defects in pathways leading to DNA repair or apoptosis. In addition, apoptosis could be counteracted by nuclear factor kappa B (NF-kappaB), the main anti-apoptotic transcription factor in the DDR. Despite the high clinical relevance, the interplay of the DDR pathways is poorly understood. For therapeutic purposes DNA damage signalling processes are induced to induce apoptosis in tumour cells. However, the efficiency of radio- and chemotherapy is strongly hampered by cell survival pathways in tumour cells. In this study logical modelling was performed to facilitate understanding of the complexity of the signal transduction networks in the DDR and to provide cancer treatment options. Results: Our comprehensive discrete logical model provided new insights into the dynamics of the DDR in human epithelial tumours. We identified new mechanisms by which the cell regulates the dynamics of the activation of the tumour suppressor p53 and NF-kappaB. Simulating therapeutic intervention by agents causing DNA single-strand breaks (SSBs) or DNA double-strand breaks (DSBs) we identified candidate target proteins for sensitization of carcinomas to therapeutic intervention. Further, we enlightened the DDR in different genetic diseases, and by failure mode analysis we defined molecular defects putatively contributing to carcinogenesis. Conclusion: By logic modelling we identified candidate target proteins that could be suitable for radio- and chemotherapy, and contributes to the design of more effective therapies.

Description: Background: Apoptosis is an essential property of all higher organisms that involves extremely complex signaling pathways. Mathematical modeling provides a rigorous integrative approach for analyzing and understanding such intricate biological systems. Results: Here, we constructed a large-scale, literature-based model of apoptosis pathways responding to an external stimulus, cisplatin. Our model includes the key elements of three apoptotic pathways induced by cisplatin: death receptor-mediated, mitochondrial, and endoplasmic reticulum-stress pathways. We showed that cisplatin-induced apoptosis had dose- and time-dependent characteristics, and the level of apoptosis was saturated at higher concentrations of cisplatin. Simulated results demonstrated that the effect of the mitochondrial pathway on apoptosis was the strongest of the three pathways. The cross-talk effect among pathways accounted for approximately 25% of the total apoptosis level. Conclusions: Using this model, we revealed a novel mechanism by which cisplatin induces dose-dependent cell death. Our finding that the level of apoptosis was affected by not only cisplatin concentration, but also by cross talk among pathways provides in silico evidence for a functional impact of system-level characteristics of signaling pathways on apoptosis.

Description: Background: Marek's Disease (MD) is a hyperproliferative, lymphomatous, neoplastic disease of chickens caused by the oncogenic Gallid herpesvirus type 2 (GaHV-2; MDV). Like several human lymphomas the neoplastic MD lymphoma cells overexpress the CD30 antigen (CD30hi) and are in minority, while the non-neoplastic cells (CD30lo) form the majority of population. MD is a unique natural in-vivo model of human CD30hi lymphomas with both natural CD30hi lymphomagenesis and spontaneous regression. The exact mechanism of neoplastic transformation from CD30lo expressing phenotype to CD30hi expressing neoplastic phenotype is unknown. Here, using microarray, proteomics and Systems Biology modeling; we compare the global gene expression of CD30lo and CD30hi cells to identify key pathways of neoplastic transformation. We propose and test a specific mechanism of neoplastic transformation, and genetic resistance, involving the MDV oncogene Meq, host gene products of the Nuclear Factor Kappa B (NF-kappaB) family and CD30; we also identify a novel Meq protein interactome. Results: Our results show that a) CD30lo lymphocytes are pre-neoplastic precursors and not merely reactive lymphocytes; b) multiple transformation mechanisms exist and are potentially controlled by Meq; c) Meq can drive a feed-forward cycle that induces CD30 transcription, increases CD30 signaling which activates NF-kappaB, and, in turn, increases Meq transcription; d) Meq transcriptional repression or activation of the CD30 promoter generally correlates with polymorphisms in the CD30 promoter distinguishing MD-lymphoma resistant and susceptible chicken genotypes e) MDV oncoprotein Meq interacts with proteins involved in physiological processes central to lymphomagenesis. Conclusions: In the context of the MD lymphoma microenvironment (and potentially in other CD30hi lymphomas as well), our results show that the neoplastic transformation is a continuum and the non-neoplastic cells are actually pre-neoplastic precursor cells and not merely immune bystanders. We also show that NF-kappaB is a central player in MDV induced neoplastic transformation of CD30-expressing lymphocytes in vivo. Our results provide insights into molecular mechanisms of neoplastic transformation in MD specifically and also herpesvirus induced lymphoma in general.

Description: Background: Modeling dynamic regulatory networks is a major challenge since much of theprotein-DNA interaction data available is static. The Dynamic Regulatory Events Miner (DREM) uses a Hidden Markov Model-based approach to integrate this static interactiondata with time series gene expression leading to models that can determine whentranscription factors (TFs) activate genes and what genes they regulate. DREM has beenused successfully in diverse areas of biological research. However, several issues were notaddressed by the original version. Results: DREM 2.0 is a comprehensive software for reconstructing dynamic regulatory networksthat supports interactive graphical or batch mode. With version 2.0 a set of new featuresthat are unique in comparison with other softwares are introduced. First, we provide staticinteraction data for additional species. Second, DREM 2.0 now accepts continuousbinding values and we added a new method to utilize TF expression levels when searchingfor dynamic models. Third, we added support for discriminative motif discovery, which isparticularly powerful for species with limited experimental interaction data. Finally, weimproved the visualization to support the new features. Combined, these changes improvethe ability of DREM 2.0 to accurately recover dynamic regulatory networks and make itmuch easier to use it for analyzing such networks in several species with varying degreesof interaction information. Conclusions: DREM 2.0 provides a unique framework for constructing and visualizing dynamicregulatory networks. DREM 2.0 can be downloaded from: www.sb.cs.cmu.edu/drem.

Description: Background: Inference about regulatory networks from high-throughput genomics data is of great interest in systems biology. We present a Bayesian approach to infer gene regulatory networks from time series expression data by integrating various types of biological knowledge. Results: We formulate network construction as a series of variable selection problems and use linear regression to model the data. Our method summarizes additional data sources with an informative prior probability distribution over candidate regression models. We extend the Bayesian model averaging (BMA) variable selection method to select regulators in the regression framework. We summarize the external biological knowledge by an informative prior probability distribution over the candidate regression models. Conclusions: We demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.

Description: Background: The molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions. The mechanisms that drive complex disease can be productively viewed in the context of the perturbations of these components. One challenge in this regard is to identify the pathways altered in specific diseases. To address this challenge, Gene Set Enrichment Analysis (GSEA) and others have been developed, which focus on alterations of individual properties of the entities (such as gene expression). However, the dynamics of the interactions with respect to disease have been less well studied (i.e., properties of components ii and iii). Results: Here, we present a novel method called Gene Interaction Enrichment and Network Analysis (GIENA) to identify dysregulated gene interactions, i.e., pairs of genes whose relationships differ between disease and control. Four functions are defined to model the biologically relevant gene interactions of cooperation (sum of mRNA expression), competition (difference between mRNA expression), redundancy (maximum of expression), or dependency (minimum of expression) among the expression levels. The proposed framework identifies dysregulated interactions and pathways enriched in dysregulated interactions; points out interactions that are perturbed across pathways; and moreover, based on the biological annotation of each type of dysregulated interaction gives clues about the regulatory logic governing the systems level perturbation. We demonstrated the potential of GIENA using published datasets related to cancer. Conclusions: We showed that GIENA identifies dysregulated pathways that are missed by traditional enrichment methods based on the individual gene properties and that use of traditional methods combined with GINEA provides coverage of the largest number of relevant pathways. In addition, using the interactions detected by GINEA, specific gene networks both within and across pathways associated with the relevant phenotypes are constructed andanalyzed.

Description: Mutations in LMNA encoding lamins A and C are associated with at least 10 different degenerative disorders affecting diverse tissues, collectively called laminopathies. A recent study showed that mis-accumulation of SUN1 underlies the pathology of degenerative features in laminopathies, and concomitantly suggests a gain-of-function versus a loss-of-function model for the action of lamin A mutants.

Description: Background: Constraint-based analysis of genome-scale metabolic models typically relies uponmaximisation of a cellular objective function such as the rate or efficiency of biomassproduction. Whilst this assumption may be valid in the case of microorganisms growingunder certain conditions, it is likely invalid in general, and especially for multicellularorganisms, where cellular objectives differ greatly both between and within cell types.Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomassper se. Results: An alternative objective function is presented, that is based upon maximising the correlationbetween experimentally measured absolute gene expression data and predicted internalreaction fluxes. Using quantitative transcriptomics data acquired from Saccharomycescerevisiae cultures under two growth conditions, the method outperforms traditionalapproaches for predicting experimentally measured exometabolic flux that are reliant uponmaximisation of the rate of biomass production. Conclusion: Due to its improved prediction of experimentally measured metabolic fluxes, and of its lackof a requirement for knowledge of the biomass composition of the organism under theconditions of interest, the approach is likely to be of rather general utility. The method hasbeen shown to predict fluxes reliably in single cellular systems. Subsequent work willinvestigate the method's ability to generate condition- and tissue-specific flux predictions inmulticellular organisms.

Description: Background: Tardigrades are multicellular organisms, resistant to extreme environmental changes suchas heat, drought, radiation and freezing. They outlast these conditions in an inactive form(tun) to escape damage to cellular structures and cell death. Tardigrades are apparentlyable to prevent or repair such damage and are therefore a crucial model organism for stresstolerance. Cultures of the tardigrade Milnesium tardigradum were dehydrated byremoving the surrounding water to induce tun formation. During this process and thesubsequent rehydration, metabolites were measured in a time series by GC-MS.Additionally expressed sequence tags are available, especially libraries generated from theactive and inactive state. The aim of this integrated analysis is to trace changes intardigrade metabolism and identify pathways responsible for their extreme resistanceagainst physical stress. Results: In this study we propose a novel integrative approach for the analysis of metabolicnetworks to identify modules of joint shifts on the transcriptomic and metabolic levels. Wederive a tardigrade-specific metabolic network represented as an undirected graph with3,658 nodes (metabolites) and 4,378 edges (reactions). Time course metabolite profilesare used to score the network nodes showing a significant change over time. The edges arescored according to information on enzymes from the EST data. Using this combinedinformation, we identify a key subnetwork (functional module) of concerted changes inmetabolic pathways, specific for de- and rehydration. The module is enriched in reactionsshowing significant changes in metabolite levels and enzyme abundance during thetransition. It resembles the cessation of a measurable metabolism (e.g. glycolysis andamino acid anabolism) during the tun formation, the production of storage metabolites andbioprotectants, such as DNA stabilizers, and the generation of amino acids and cellularcomponents from monosaccharides as carbon and energy source during rehydration. Conclusions: The functional module identifies relationships among changed metabolites (e.g.spermidine) and reactions and provides first insights in important altered metabolicpathways. With sparse and diverse data available, the presented integrated metabolitenetwork approach is suitable to integrate all existing data and analyse it in a combinedmanner.

Description: Background: Spatial signal transduction plays a vital role in many intracellular processes such as eukaryotic chemotaxis, polarity generation, cell division. Furthermore it is being increasingly realized that the spatial dimension to signalling may play an important role in other apparently purely temporal signal transduction processes. It is being recognized that a conceptual basis for studying spatial signal transduction in signalling networks is necessary. Results: In this work we examine spatial signal transduction in a series of standard motifs/networks. These networks include coherent and incoherent feedforward, positive and negative feedback, cyclic motifs, monostable switches, bistable switches and negative feedback oscillators. In all these cases, the driving signal has spatial variation. For each network we consider two cases, one where all elements are essentially non diffusible, and the other where one of the network elements may be highly diffusible. A careful analysis of steady state signal transduction provides many insights into the behaviour of all these modules. While in the non-diffusible case for the most part, spatial signalling reflects the temporal signalling behaviour, in the diffusible cases, we see significant differences between spatial and temporal signalling characteristics. Our results demonstrate that the presence of diffusible elements in the networks provides important constraints and capabilities for signalling. Conclusions: Our results provide a systematic basis for understanding spatial signalling in networks and the role of diffusible elements therein. This provides many insights into the signal transduction capabilities and constraints in such networks and suggests ways in which cellular signalling and information processing is organized to conform to or bypass those constraints. It also provides a framework for starting to understand the organization and regulation of spatial signal transduction in individual processes.

Description: Background: The process of drug discovery and development is time-consuming and costly, and the probability of success is low. Therefore, there is rising interest in repositioning existing drugs for new medical indications. When successful, this process reduces the risk of failure and costs associated with de novo drug development. However, in many cases, new indications of existing drugs have been found serendipitously. Thus there is a clear need for establishment of rational methods for drug repositioning. Results: In this study, we have established a database we call "PharmDB" which integrates data associated with disease indications, drug development, and associated proteins, and known interactions extracted from various established databases. To explore linkages of known drugs to diseases of interest from within PharmDB, we designed the Shared Neighborhood Scoring (SNS) algorithm. And to facilitate exploration of tripartite (Drug-Protein-Disease) network, we developed a graphical data visualization software program called phExplorer, which allows us to browse PharmDB data in an interactive and dynamic manner. We validated this knowledge-based tool kit, by identifying a potential application of a hypertension drug, benzthiazide (TBZT), to induce lung cancer cell death. Conclusions: By combining PharmDB, an integrated tripartite database, with Shared Neighborhood Scoring (SNS) algorithm, we developed a knowledge platform to rationally identify new indications for known FDA approved drugs, which can be customized to specific projects using manual curation. The data in PharmDB is open access and can be easily explored with phExplorer and accessed via BioMart web service (http://www.i-pharm.org/, http://biomart.i-pharm.org/).

Description: Background: Stochastic biochemical reaction networks are commonly modelled by the chemical master equation, and can be simulated as first order linear differential equationsthrough a finite state projection. Due to the very high state space dimension of these equations, numerical simulations are computationally expensive. This is a particular problem for analysis tasks requiring repeated simulations for different parameter values. Such tasks are computationally expensive to the point of infeasibility with the chemical master equation. Results: In this article, we apply parametric model order reduction techniques in order to construct accurate low-dimensional parametric models of the chemical master equation. These surrogate models can be used in various parametric analysis task such as identifiability analysis, parameter estimation, or sensitivity analysis. As biological examples, we consider two models for gene regulation networks, a bistable switch and a network displaying stochastic oscillations. Conclusions: The results show that the parametric model reduction yields efficient models of stochastic biochemical reaction networks, and that these models can be useful for systems biology applications involving parametric analysis problems such as parameter exploration, optimization, estimation or sensitivity analysis.

Description: Background: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal. Results: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases. Conclusions: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

Description: Phosphatases are important regulators of intracellular signaling events, and their functions have been implicated in many biological processes. Dual-specificity phosphatases (DUSPs), whose family currently contains 25 members, are phosphatases that can dephosphorylate both tyrosine and serine/threonine residues of their substrates. The archetypical DUSP, DUSP1/MKP1, was initially discovered to regulate the activities of MAP kinases by dephosphorylating the TXY motif in the kinase domain. However, although DUSPs were discovered more than a decade ago, only in the past few years have their various functions begun to be described. DUSPs can be categorized based on the presence or absence of a MAP kinase-interacting domain into typical DUSPs and atypical DUSPs, respectively. In this review, we discuss the current understanding of how the activities of typical DUSPs are regulated and how typical DUSPs can regulate the functions of their targets. We will also summarize recent findings from several in vivo DUSP-deficient mouse models that studied the involvement of DUSPs during the development and functioning of T cells. Finally, we discuss briefly the potential roles of DUSPs in the regulation of non-MAP kinase targets, as well as in the modulation of tumorigenesis.

Description: Background: Advances in modern high-throughput techniques of molecular biology have enabled top-down approaches for the estimation of parameter values in metabolic systems, based on time series data. Special among them is the recent method of dynamic flux estimation (DFE), which uses such data not only for parameter estimation but also for the identification of functional forms of the processes governing a metabolic system. DFE furthermore provides diagnostic tools for the evaluation of model validity and of the quality of a model fit beyond residual errors. Unfortunately, DFE works only when the data are more or less complete and the system contains as many independent fluxes as metabolites. These drawbacks may be ameliorated with other types of estimation and information. However, such supplementations incur their own limitations. In particular, assumptions must be made regarding the functional forms of some processes and detailed kinetic information must be available, in addition to the time series data. Results: The authors propose here a systematic approach that supplements DFE and overcomes some of its shortcomings. Like DFE, the approach is model-free and requires only minimal assumptions. If sufficient time series data are available, the approach allows the determination of a subset of fluxes that enables the subsequent applicability of DFE to the rest of the flux system. The authors demonstrate the procedure with three artificial pathway systems exhibiting distinct characteristics and with actual data of the trehalose pathway in Saccharomyces cerevisiae. Conclusion: The results demonstrate that the proposed method successfully complements DFE under various situations and without a priori assumptions regarding the model representation. The proposed method also permits an examination of whether at all, to what degree, or within what range the available time series data can be validly represented in a particular functional format of a flux within a pathway system. Based on these results, further experiments may be designed to generate data points that genuinely add new information to the structure identification and parameter estimation tasks at hand.

Description: Background: Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations.This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class ofsystems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. Results: Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. Conclusions: In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distributionprofiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well howdynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of generic distributed biological systems.

Description: Background: Identification of driver mutations among numerous genomic alternations remains a critical challenge to the elucidation of the underlying mechanisms of cancer. Because driver mutations by definition are associated with a greater number of cancer phenotypes compared to other mutations, we hypothesized that driver mutations could more easily be identified once the genotype-phenotype correlations are detected across tumor samples. Results: In this study, we describe a novel network analysis to identify the driver mutation through integrating both cancer genomes and transcriptomes. Our method successfully identified a significant genotype-phenotype change correlation in all six solid tumor types and revealed core modules that contain both significantly enriched somatic mutations and aberrant expression changes specific to tumor development. Moreover, we found that the majority of these core modules contained well known cancer driver mutations, and that their mutated genes tended to occur at hub genes with central regulatory roles. In these mutated genes, the majority were cancer-type specific and exhibited a closer relationship within the same cancer type rather than across cancer types. The remaining mutated genes that exist in multiple cancer types led to two cancer type clusters, one cluster consisted of three neural derived or related cancer types, and the other cluster consisted of two adenoma cancer types. Conclusions: Our approach can successfully identify the candidate drivers from the core modules. Comprehensive network analysis on the core modules potentially provides critical insights into convergent cancer development in different organs.

Description: Background: Understanding gene interactions is a fundamental question in systems biology. Currently, modeling of gene regulations using the Bayesian Network (BN) formalism assumes that genes interact either instantaneously or with a certain amount of time delay. However in reality, biological regulations, both instantaneous and time-delayed, occur simultaneously. A framework that can detect and model both these two types of interactions simultaneously would represent gene regulatory networks more accurately. Results: In this paper, we introduce a framework based on the Bayesian Network (BN) formalism that can represent both instantaneous and time-delayed interactions between genes simultaneously. A novel scoring metric having rm mathematical underpinnings is also proposed that, unlike other recent methods, can score both interactions concurrently and takes into account the reality that multiple regulators can regulate a gene jointly, rather than in an isolated pair-wise manner. Further, a gene regulatory network (GRN) inference method employing an evolutionary search that makes use of the framework and the scoring metric is also presented. Conclusion: By taking into consideration the biological fact that both instantaneous and time-delayed regulations can occur among genes, our approach models gene interactions with greater accuracy. The proposed framework is efcient and can be used to infer gene networkshaving multiple orders of instantaneous and time-delayed regulations simultaneously. Experiments are carried out using three different synthetic networks (with three different mechanisms for generating synthetic data) as well as real life networks of Saccharomyces cerevisiae, E. coli and cyanobacteria gene expression data. The results show the effectiveness of our approach.

Description: Background: Both transcriptional control and microRNA (miRNA) control are critical regulatory mechanisms for cells to direct their destinies. At present, the combinatorial regulatory network composed of transcriptional regulations and post-transcriptional regulations is often constructed through a forward engineering strategy that is based solely on searching of transcriptional factor binding sites or miRNA seed regions in the putative target sequences. If the reverse engineering strategy is integrated with the forward engineering strategy, a more accurate and more specific combinatorial regulatory network will be obtained. Results: In this work, utilizing both sequence-matching information and parallel expression datasets of miRNAs and mRNAs, we integrated forward engineering with reverse engineering strategies and as a result built a hypothetical combinatorial gene regulatory network in human cancer. The credibility of the regulatory relationships in the network were validated by random permutation procedures and supported by authoritative experimental evidence-based databases. The global and local architecture properties of the combinatorial regulatory network were explored, and the most important tumor-regulating miRNAs and TFs were highlighted from a topological point of view. Conclusions: By integrating the forward engineering and reverse engineering strategies, we manage to sketch a genome-scale combinatorial gene regulatory network in human cancer, which includes transcriptional regulations and miRNA regulations, allowing systematic study of cancer gene regulation. Our work establishes a pipeline that can be extended to reveal conditional combinatorial regulatory landscapes correlating to specific cellular contexts.

Description: Background: Spirulina (Arthrospira) platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438) genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium. Results: In this work, we present the genome-scale metabolic network analysis of S. platensis C1, iAK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and875 reactions. We validated iAK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The iAK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of iAK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP) analysis. Conclusions: This study proposes the first genome-scale model of S. platensis C1, iAK692, which is a predictive metabolic platform for a global understanding of physiological behaviors and metabolic engineering. This platform could accelerate the integrative analysis of various "-omics" data, leading to strain improvement towards a diverse range of desired industrial products from Spirulina.

Description: Background: MicroRNAs (miRNAs) are involved in carcinogenesis and tumor progression by regulating post-transcriptional gene expression. However, the miRNA-mRNA regulatory network is far from being fully understood. The objective of this study is to identify the colorectal cancer (CRC) specific miRNAs and their target mRNAs using a multi-step approach. Results: A multi-step approach combining microarray miRNA and mRNA expression profile and bioinformatics analysis was adopted to identify the CRC specific miRNA-mRNA regulatory network. First, 32 differentially expressed miRNAs and 2916 mRNAs from CRC samples and their corresponding normal epithelial tissues were identified by miRNA and mRNA microarray, respectively. Secondly, 22 dysregulated miRNAs and their 58 target mRNAs (72 miRNA-mRNA pairs) were identified by a combination of Pearson's correlation analysis and prediction by databases TargetScan and miRanda. Bioinformatics analysis revealed that these miRNA-mRNAs pairs were involved in Wnt signaling pathway. Additionally, 6 up-regulated miRNAs (mir-21, mir-223, mir-224, mir-29a, mir-29b, and mir-27a) and 4 down-regulated predicted target mRNAs (SFRP1, SFRP2, RNF138, and KLF4) were selected to validate the expression level and their anti-correlationship in an extended cohort of CRC patients by qRT-PCR. Except for mir-27a, the differential expression and their anti-correlationship were proven. Finally, a transfection assay was performed to validate a regulatory relationship between mir-29a and KLF4 at both RNA and protein levels. Conclusions: Seventy-two miRNA-mRNA pairs combined by 22 dysregulated miRNAs and their 58 target mRNAs identified by the multi-step approach appear to be involved in CRC tumorigenesis. The results in our study were worthwhile to further investigation via a functional study to fully understand the underlying regulatory mechanisms of miRNA in CRC.

Description: Background: Mutations in the smooth endoplasmic reticulum (sER) calcium channel Inositol Trisphosphate Receptor type 1 (IP3R1) in humans with the motor function coordination disorders Spinocerebellar Ataxia Types 15 and 16 (SCA15/16) and in a corresponding mouse model, the IP3R1delta18/delta18 mice, lead to reduced IP3R1 levels. We posit that increasing IP3R1 sensitivity to IP3 in ataxias with reduced IP3R1 could restore normal calcium response. On the other hand, in mouse models of the human polyglutamine (polyQ) ataxias, SCA2, and SCA3, the primary finding appears to be hyperactive IP3R1-mediated calcium release. It has been suggested that the polyQ SCA1 mice may also show hyperactive IP3R1. Yet, SCA1 mice show downregulated gene expression of IP3R1, Homer, metabotropic glutamate receptor (mGluR), smooth endoplasmic reticulum Ca-ATP-ase (SERCA), calbindin, parvalbumin, and other calcium signaling proteins. Results: We create a computational model of pathological alterations in calcium signaling in cerebellar Purkinje neurons to investigate several forms of spinocerebellar ataxia associated with changes in the abundance, sensitivity, or activity of the calcium channel IP3R1. We find that increasing IP3R1 sensitivity to IP3 in computational models of SCA15/16 can restore normal calcium response if IP3R1 abundance is not too low. The studied range in IP3R1 levels reflects variability found in human and mouse ataxic models. Further, the required fold increases in sensitivity are within experimental ranges from experiments that use IP3R1 phosphorylation status to adjust its sensitivity to IP3. Results from our simulations of polyglutamine SCAs suggest that downregulation of some calcium signaling proteins may be partially compensatory. However, the downregulation of calcium buffer proteins observed in the SCA1 mice may contribute to pathology. Finally, our model suggests that the calcium-activated voltage-gated potassium channels may provide an important link between calcium metabolism and membrane potential in Purkinje cell function. Conclusion: Thus, we have established an initial platform for computational evaluation and prediction of ataxia pathophysiology. Specifically, the model has been used to investigate SCA15/16, SCA1, SCA2, and SCA3. Results suggest that experimental studies treating mouse models of any of these ataxias with appropriately chosen peptides resembling the C-terminal of IP3R1 could adjust receptor sensitivity, and thereby modulate calcium release and normalize IP3 response. In addition, the model supports the hypothesis of IP3R1 supersensitivity in SCA1.

Description: Background: Circadian rhythm pathways influence the expression patterns of as much as 31% of theArabidopsis genome through complicated interaction pathways, and have been found to besignificantly disrupted by biotic and abiotic stress treatments, complicating treatmentresponsegene discovery methods due to clock pattern mismatches in the fold change-based statistics. The PRIISM (Pattern Recomposition for the Isolation of Independent Signals inMicroarray data) algorithm outlined in this paper is designed to separate pattern changesinduced by different forces, including treatment-response pathways and circadian clockrhythm disruptions. Results: Using the Fourier transform, high-resolution time-series microarray data is projected to thefrequency domain. By identifying the clock frequency range from the core circadian clockgenes, we separate the frequency spectrum to different sections containing treatmentfrequency(representing up- or down-regulation by an adaptive treatment response), clockfrequency(representing the circadian clock-disruption response) and noise-frequencycomponents. Then, we project the components' spectra back to the expression domain toreconstruct isolated, independent gene expression patterns representing the effects of thedifferent influences.By applying PRIISM on a high-resolution time-series Arabidopsis microarray dataset under acold treatment, we systematically evaluated our method using maximum fold change andprincipal component analyses. The results of this study showed that the ranked treatmentfrequencyfold change results produce fewer false positives than the original methodology,and the 26-hour timepoint in our dataset was the best statistic for distinguishing the mostknown cold-response genes. In addition, six novel cold-response genes were discovered.PRIISM also provides gene expression data which represents only circadian clock influences,and may be useful for circadian clock studies. Conclusion: PRIISM is a novel approach for overcoming the problem of circadian disruptions from stresstreatments on plants. PRIISM can be integrated with any existing analysis approach on geneexpression data to separate circadian-influenced changes in gene expression, and it can beextended to apply to any organism with regular oscillations in gene expression patterns acrossa large portion of the genome.

Description: Excessive loss of pancreatic ß-cells mainly through apoptosis contributes to the development of diabetic hyperglycemia. Oxidative stress plays a major role in the process of ß-cell apoptosis due to low expression level of endogenous antioxidants in the ß-cells. Peroxiredoxins (PRDX) are a family of peroxide reductases which uses thioredoxin to clear peroxides. Several members of PRDX have been found in ß-cells and recent studies suggested that these antioxidant enzymes possess protective effects in ß-cells against oxidative stress mediated apoptosis. In this study, we aimed to investigate the role of PRDX2 in modulating ß-cell functions. We detected the expression of PRDX2 both at the transcript and protein levels in the clonal ß-cells INS-1 and MIN6 as well as rodent islets. Western blot showed that treatment of MIN6 ß-cell line with proinflammatory cytokines, palmitic acid or streptozotocin dose- or time-dependently increased apoptosis, which was associated with reduced endogenous expression levels of PRDX2. To examine the role for PRDX2 in the apoptotic stimuli-induced ß-cell apoptosis, we used plasmid overexpression and siRNA knockdown strategies to investigate whether the elevation or knockdown of PRDX2 affects stimuli-induced apoptosis in the ß-cells. Remarkably, overexpression of PRDX2 in MIN6 cells significantly attenuated the oxidative stresses mediated apoptosis, as evaluated by cleaved caspase-3 expression, nuclear condensation and fragmentation, as well as FACS analysis. Conversely, attenuation of PRDX2 protein expression using siRNA knockdown exaggerated the cell death induced by proinflammatory cytokines and palmitic acid in the MIN6 cells. These results suggest that PRDX2 may play a protective role in pancreatic ß-cells under oxidative stress.

Description: Background: Proteolytic breakdown of the amyloid precursor protein (APP) by secretases is a complex cellular process that results in formation of neurotoxic Aß peptides, causative of neurodegeneration in Alzheimer's disease (AD). Processing involves monomeric and dimeric forms of APP that traffic through distinct cellular compartments where the various secretases reside. Amyloidogenic processing is also influenced by modifiers such as sorting receptor-related protein (SORLA), an inhibitor of APP breakdown and major AD risk factor. Results: In this study, we developed a multi-compartment model to simulate the complexity of APP processing in neurons and to accurately describe the effects of SORLA on these processes. Based on dose-response data, our study concludes that SORLA specifically impairs processing of APP dimers, the preferred secretase substrate. In addition, SORLA alters the dynamic behavior of ß-secretase, the enzyme responsible for the initial step in the amyloidogenic processing cascade. Conclusions: Our multi-compartment model represents a major conceptual advance over single-compartment models previously used to simulate APP processing; and it identified APP dimers and ß-secretase as the two distinct targets of the inhibitory action of SORLA in Alzheimer's disease.

Description: Background: Wounding following whole-body gamma-irradiation (radiation combined injury, RCI) increases mortality. Wounding-induced increases in radiation mortality are triggered by sustained activation of inducible nitric oxide synthase pathways, persistent alteration of cytokine homeostasis, and increased susceptibility to bacterial infection. Among these factors, cytokines along with other biomarkers have been adopted for biodosimetric evaluation and assessment of radiation dose and injury. Therefore, wounding could complicate biodosimetric assessments. Results: In this report, such confounding effects were addressed. Mice were given 60Co gamma-photon radiation followed by skin wounding. Wound trauma exacerbated radiation-induced mortality, body-weight loss, and wound healing. Analyses of DNA damage in bone-marrow cells and peripheral blood mononuclear cells (PBMCs), changes in hematology and cytokine profiles, and fundamental clinical signs were evaluated. Early biomarkers (1 d after RCI) vs. irradiation alone included significant decreases in survivin expression in bone marrow cells, enhanced increases in gamma-H2AX formation in Lin+ bone marrow cells, enhanced increases in IL-1beta, IL-6, IL-8, and G-CSF concentrations in blood, and concomitant decreases in gamma-H2AX formation in PBMCs and decreases in numbers of splenocytes, lymphocytes, and neutrophils. Intermediate biomarkers (7 - 10 d after RCI) included continuously decreased gamma-H2AX formation in PBMC and enhanced increases in IL-1beta, IL-6, IL-8, and G-CSF concentrations in blood. The clinical signs evaluated after RCI were increased water consumption, decreased body weight, and decreased wound healing rate and survival rate. Late clinical signs (30 d after RCI) included poor survival and wound healing. Conclusion: Results suggest that confounding factors such as wounding alters ionizing radiation dose assessment and agents inhibiting these responses may prove therapeutic for radiation combined injury and reduce related mortality.

Description: A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.

Description: Nuclear receptors are a family of ligand-activated, DNA sequence-specific transcription factors that regulate various aspects of animal development, cell proliferation, differentiation, and homeostasis. The physiological roles of nuclear receptors and their ligands have been intensively studied in cancer and metabolic syndrome. However, their role in kidney diseases is still evolving, despite their ligands being used clinically to treat renal diseases for decades. This review will discuss the progress of our understanding of the role of nuclear receptors and their ligands in kidney physiology with emphasis on their roles in treating glomerular disorders and podocyte injury repair responses.

Description: Background: Collective rhythms of gene regulatory networks have been a subject of considerable interest for biologists and theoreticians, in particular the synchronization of dynamic cells mediated by intercellular communication. Synchronization of a population of synthetic genetic oscillators is an important design in practical applications, because such a population distributed over different host cells needs to exploit molecular phenomena simultaneously in order to emerge a biological phenomenon. However, this synchronization may be corrupted by intrinsic kinetic parameter fluctuations and extrinsic environmental molecular noise. Therefore, robust synchronization is an important design topic in nonlinear stochastic coupled synthetic genetic oscillators with intrinsic kinetic parameter fluctuations and extrinsic molecular noise. Results: Initially, the condition for robust synchronization of synthetic genetic oscillators was derived based on Hamilton Jacobi inequality (HJI). We found that if the synchronization robustness can confer enough intrinsic robustness to tolerate intrinsic parameter fluctuation and extrinsic robustness to filter the environmental noise, then robust synchronization of coupled synthetic genetic oscillators is guaranteed. If the synchronization robustness of a population of nonlinear stochastic coupled synthetic genetic oscillators distributed over different host cells could not be maintained, then robust synchronization could be enhanced by external control input through quorum sensing molecules. In order to simplify the analysis and design of robust synchronization of nonlinear stochastic synthetic genetic oscillators, the fuzzy interpolation method was employed to interpolate several local linear stochastic coupled systems to approximate the nonlinear stochastic coupled system so that the HJI-based synchronization design problem could be replaced by a simple linear matrix inequality (LMI)-based design problem, which could be solved with the help of LMI toolbox in MATLAB easily. Conclusion: If the synchronization robustness criterion, i.e. the synchronization robustness 〉= intrinsic robustness + extrinsic robustness, then the stochastic coupled synthetic oscillators can be robustly synchronized in spite of intrinsic parameter fluctuation and extrinsic noise. If the synchronization robustness criterion is violated, external control scheme by adding inducer can be designed to improve synchronization robustness of coupled synthetic genetic oscillators. The investigated robust synchronization criteria and proposed external control method are useful for a population of coupled synthetic networks with emergent synchronization behavior, especially for multi-cellular, engineered networks.

Description: Background: Constraint-based modeling is increasingly employed for metabolic network analysis. Its underlyingassumption is that natural metabolic phenotypes can be predicted by adding physicochemical constraints toremove unrealistic metabolic flux solutions. The loopless-COBRA approach provides an additional constraintthat eliminates thermodynamically infeasible internal cycles (or loops) from the space of solutions. Thisallows the prediction of flux solutions that are more consistent with experimental data. However, it is not clearif this approach over-constrains the models by removing non-loop solutions as well. Results: Here we apply Gordan's theorem from linear algebra to prove for the first time that the constraints added inloopless-COBRA do not over-constrain the problem beyond the elimination of the loops themselves. Conclusions: The loopless-COBRA constraints can be reliably applied. Furthermore, this proof may be adapted to evaluatethe theoretical soundness for other methods in constraint-based modeling.

Description: Background: 5-lipoxygenase (5-LO) is a key enzyme in the synthesis of leukotrienes and 5-Oxo-6E,8Z,11Z,14Z-eicosatetraenoic acid (oxoETE). These inflammatory signaling molecules play a role in the pathology of asthma and so 5-LO inhibition is a promising target for asthma therapy. The 5-LO redox inhibitor zileuton (Zyflo IR/CR(R)) is currently marketed for the treatment of asthma in adults and children, but widespread use of zileuton is limited by its efficacy/safety profile, potentially related to its redox characteristics. Thus, a quantitative, mechanistic description of its functioning may be useful for development of improved anti-inflammatory targeting this mechanism. Results: A mathematical model describing the operation of 5-LO, phospholipase A2, glutathione peroxidase and 5-hydroxyeicosanoid dehydrogenase was developed. The catalytic cycles of the enzymes were reconstructed and kinetic parameters estimated on the basis of available experimental data. The final model describes each stage of cys-leukotriene biosynthesis and the reactions involved in oxoETE production. Regulation of these processes by substrates (phospholipid concentration) and intracellular redox state (concentrations of reduced glutathione, glutathione (GSH), and lipid peroxide) were taken into account. The model enabled us to reveal differences between redox and non-redox 5-LO inhibitors under conditions of oxidative stress. Despite both redox and non-redox inhibitors suppressing leukotriene A4 (LTA4) synthesis, redox inhibitors are predicted to increase oxoETE production, thus compromising efficacy. This phenomena can be explained in terms of the pseudo-peroxidase activity of 5-LO and the ability of lipid peroxides to transform 5-LO into its active form even in the presence of redox inhibitors. Conclusions: The mathematical model developed described quantitatively different mechanisms of 5-LO inhibition and simulations revealed differences between the potential therapeutic outcomes for these mechanisms.

Description: Asymmetric cell division is a developmental process utilized by several organisms. On the most basic level, an asymmetric division produces two daughter cells, each possessing a different identity or fate. Drosophila melanogaster progenitor cells, referred to as neuroblasts, undergo asymmetric division to produce a daughter neuroblast and another cell known as a ganglion mother cell (GMC). There are several features of asymmetric division in Drosophila that make it a very complex process, and these aspects will be discussed at length. The cell fate determinants that play a role in specifying daughter cell fate, as well as the mechanisms behind setting up cortical polarity within neuroblasts, have proved to be essential to ensuring that neurogenesis occurs properly. The role that mitotic spindle orientation plays in coordinating asymmetric division, as well as how cell cycle regulators influence asymmetric division machinery, will also be addressed. Most significantly, malfunctions during asymmetric cell division have shown to be causally linked with neoplastic growth and tumor formation. Therefore, it is imperative that the developmental repercussions as a result of asymmetric cell division gone awry be understood.

Description: Background: Human tissues perform diverse metabolic functions. Mapping out these tissue-specific functions in genome-scale models will advance our understanding of the metabolic basis of various physiological and pathological processes. The global knowledgebase of metabolic functions categorized for the human genome (Human Recon1) coupled with abundant high-throughput data now makes possible the reconstruction of tissue-specific metabolic models. However, the number of available tissue-specific models remains incomplete compared with the large diversity of human tissues. Results: We developed a method called metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE). mCADRE is able to infer a tissue-specific network based on gene expression data and metabolic network topology, along with evaluation of functional capabilities during model building. mCADRE produces models with similar or better functionality and achieves dramatic computational speed up over existing methods. Using our method, we reconstructed draft genome-scale metabolic models for 126 human tissue and cell types. Among these, there are models for 26 tumor tissues along with their normal counterparts, and 30 different brain tissues. We performed pathway-level analyses of this large collection of tissue-specific models and identified the eicosanoid metabolic pathway, especially reactions catalyzing the production of leukotrienes from arachidnoic acid, as potential drug targets that selectively affect tumor tissues. Conclusions: This large collection of 126 genome-scale draft metabolic models provides a useful resource for studying the metabolic basis for a variety of human diseases across many tissues. The functionality of the resulting models and the fast computational speed of the mCADRE algorithm make it a useful tool to build and update tissue-specific metabolic models.

Description: Background: Estrogen therapy has positively impact the treatment of several cancers, such as prostate, lung and breast cancers. Moreover, several groups have reported the importance of estrogen induced gene regulation in esophageal cancer (EC). This suggests that there could be a potential for estrogen therapy for EC. The efficient design of estrogen therapies requires as complete as possible list of genes responsive to estrogen. Our study develops a systems biology methodology using esophageal squamous cell carcinoma (ESCC) as a model to identify estrogen responsive genes. These genes, on the other hand, could be affected by estrogen therapy in ESCC. Results: Based on different sources of information we identified 418 genes implicated in ESCC. Putative estrogen responsive elements (EREs) mapped to the promoter region of the ESCC genes were used to initially identify candidate estrogen responsive genes. EREs mapped to the promoter sequence of 30.62% (128/418) of ESCC genes of which 43.75% (56/128) are known to be estrogen responsive, while 56.25% (72/128) are new candidate estrogen responsive genes. EREs did not map to 290 ESCC genes. Of these 290 genes, 50.34% (146/290) are known to be estrogen responsive. By analyzing transcription factor binding sites (TFBSs) in the promoters of the 202 (56+146) known estrogen responsive ESCC genes under study, we found that their regulatory potential may be characterized by 44 significantly over-represented co-localized TFBSs (cTFBSs). We were able to map these cTFBSs to promoters of 32 of the 72 new candidate estrogen responsive ESCC genes, thereby increasing confidence that these 32 ESCC genes are responsive to estrogen since their promoters contain both: a/mapped EREs, and b/at least four cTFBSs characteristic of ESCC genes that are responsive to estrogen. Recent publications confirm that 47% (15/32) of these 32 predicted genes are indeed responsive to estrogen. Conclusion: To the best of our knowledge our study is the first to use a cancer disease model as the framework to identify hormone responsive genes. Although we used ESCC as the disease model and estrogen as the hormone, the methodology can be extended analogously to other diseases as the model and other hormones. We believe that our results provide useful information for those interested in genes responsive to hormones and in the design of hormone-based therapies.

Description: Background: PDLIM2 is essential for the termination of the inflammatory transcription factors NF-kappaB and STAT but is dispensable for the development of immune cells and immune tissues/organs. Currently, it remains unknown whether and how PDLIM2 is involved in physiologic and pathogenic processes. Results: Here we report that naive PDLIM2 deficient CD4+ T cells were prone to differentiate into Th1 and Th17 cells. PDLIM2 deficiency, however, had no obvious effect on lineage commitment towards Th2 or Treg cells. Notably, PDLIM2 deficient mice exhibited increased susceptibility to experimental autoimmune encephalitis (EAE), a Th1 and/or Th17 cell-mediated inflammatory disease model of multiple sclerosis (MS). Mechanistic studies further indicate that PDLIM2 was required for restricting expression of Th1 and Th17 cytokines, which was in accordance with the role of PDLIM2 in the termination of NF-kappaB and STAT activation. Conclusion: These findings suggest that PDLIM2 is a key modulator of T-cell-mediated immune responses that may be targeted for the therapy of human autoimmune diseases.

Description: Background: Modeling the dynamics of intracellular regulation networks by systems of ordinary differential equations has become a standard method in systems biology, and it has been shown that the behavior of these networks is often tightly connected to the network topology. We have recently introduced the circuit-breaking algorithm, a method that uses the network topology to construct a one-dimensional circuit-characteristic of the system. It was shownthat this characteristic can be used for an efcient calculation of the system's xed points. Results: Here we extend previous work and show several connections between the circuit-characteristic and the stability of xed points. In particular, we derive a sufcient condition on the characteristic for a xed point to be unstable for certain graph structures and demonstrate that the characteristic does not contain the information to decide whether a xed point is asymptotically stable. All statements are illustrated on biological network models. Conclusions: Single feedback circuits and their role for complex dynamic behavior of biological networks have extensively been investigated, but a transfer of most of these concepts to more complex topologies is difcult. In this context, our algorithm is a powerful new approach for the analysis of regulation networks that goes beyond single isolated feedback circuits.

Description: Background: Biological pathways are important for understanding biological mechanisms. Thus, finding important pathways that underlie biological problems helps researchers to focus on the most relevant sets of genes. Pathways resemble networks with complicated structures, but most of the existing pathway enrichment tools ignore topological information embedded within pathways, which limits their applicability. Results: A systematic and extensible pathway enrichment method in which nodes are weighted by network centrality was proposed. We demonstrate how choice of pathway structure and centrality measurement, as well as the presence of key genes, affects pathway significance. We emphasize two improvements of our method over current methods. First, allowing for the diversity of genes' characters and the difficulty of covering gene importance from all aspects, we set centrality as an optional parameter in the model. Second, nodes rather than genes form the basic unit of pathways, such that one node can be composed of several genes and one gene may reside in different nodes. By comparing our methodology to the original enrichment method using both simulation data and real-world data, we demonstrate the efficacy of our method in finding new pathways from biological perspective. Conclusions: Our method can benefit the systematic analysis of biological pathways and help to extract more meaningful information from gene expression data. The algorithm has been implemented as an R package CePa, and also a web-based version of CePa is provided.

Description: Background: The half-life of a protein is regulated by a range of system properties, including the abundance of components of the degradative machinery and protein modiers. It is also inuenced by protein-specic properties, such as a protein's structural make-up and interaction partners. New experimental techniques coupled with powerful data integration methods now enable us to not only investigate what features govern protein stability in general, but also to build models that identify what properties determine each protein's metabolic stability. Results: In this work we present ve groups of features useful for predicting protein stability: (1) post-translational modications, (2) domain types, (3) structural disorder, (4) the identityof a protein's N-terminal residue and (5) amino acid sequence. We incorporate these features into a predictive model with promising accuracy. At a 20% false positive rate, the model exhibits an 80% true positive rate, outperforming the only previously proposed stability predictor. We also investigate the impact of N-terminal protein tagging as used to generate the data set, in particular the impact it may have on the measurements for secreted and transmembrane proteins; we train and test our model on a subset of the data with those proteins removed, and show that the model sustains high accuracy. Finally, we estimate system-wide metabolic stability by surveying the whole human proteome. Conclusions: We describe a variety of protein features that are signicantly over- or under-represented in stable and unstable proteins, including phosphorylation, acetylation and destabilizing N-terminal residues. Bayesian networks are ideal for combining these features into a predictive model with superior accuracy and transparency compared to the only other proposed stability predictor. Furthermore, our stability predictions of the human proteome will nd application in the analysis of functionally related proteins, shedding new light on regulation by protein synthesis and degradation.

Description: Background: Alzheimer's disease (AD) is the most common cause of dementia among the elderly. To clarify pathogenesis of AD, thousands of reports have been accumulating. However, knowledge of signaling pathways in the field of AD has not been compiled as a database before.DescriptionHere, we have constructed a publicly available pathway map called "AlzPathway" that comprehensively catalogs signaling pathways in the field of AD. We have collected and manually curated over 100 review articles related to AD, and have built an AD pathway map using CellDesigner. AlzPathway is currently composed of 1347 molecules and 1070 reactions in neuron, brain blood barrier, presynaptic, postsynaptic, astrocyte, and microglial cells and their cellular localizations. AlzPathway is available as both the SBML (Systems Biology Markup Language) map for CellDesigner and the high resolution image map. AlzPathway is also available as a web service (online map) based on Payao system, a community-based, collaborative web service platform for pathway model curation, enabling continuous updates by AD researchers. Conclusions: AlzPathway is the first comprehensive map of intra, inter and extra cellular AD signaling pathways which can enable mechanistic deciphering of AD pathogenesis. The AlzPathway map is accessible at http://alzpathway.org/.

Description: Background: Efforts to improve the computational reconstruction of the Saccharomyces Cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature. Results: Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model's predictive accuracy and to demonstrate basic model applications, such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2, and 3. Conclusions: Yeast 5 expands and refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a stoichiometrically constrained yeast metabolic model. It differs from previous reconstructions and models by emphasizing the distinction between the yeast metabolic reconstruction and the stoichiometrically constrained model, and makes both available as Additional file 4 and Additional file 5 and at http://yeast.sf.net/ as separate systems biology markup language (SBML) files. Through this separation, we intend to make the modeling process more accessible, explicit, transparent, and reproducible.

Description: Background: Systems biology approaches to study metabolic switching in Streptomyces coelicolor A3(2) depend on cultivation conditions ensuring high reproducibility and distinct phases of culture growth and secondary metabolite production. In addition, biomass concentrations must be sufficiently high to allow for extensive time-series sampling before occurrence of a given nutrient depletion for transition triggering. The present study describes for the first time the development of a dedicated optimized submerged batch fermentation strategy as the basis for highly time-resolved systems biology studies of metabolic switching in S. coelicolor A3(2). Results: By a step-wise approach, cultivation conditions and two fully defined cultivation media were developed and evaluated using strain M145 of S. coelicolor A3(2), providing a high degree of cultivation reproducibility and enabling reliable studies of the effect of phosphate depletion and L-glutamate depletion on the metabolic transition to antibiotic production phase. Interestingly, both of the two carbon sources provided, D-glucose and L-glutamate, were found to be necessary in order to maintain high growth rates and prevent secondary metabolite production before nutrient depletion. Comparative analysis of batch cultivations with (i) both L-glutamate and D-glucose in excess, (ii) L-glutamate depletion and D-glucose in excess, (iii) L-glutamate as the sole source of carbon and (iv) D-glucose as the sole source of carbon, reveal a complex interplay of the two carbon sources in the bacterium's central carbon metabolism. Conclusions: The present study presents for the first time a dedicated cultivation strategy fulfilling the requirements for systems biology studies of metabolic switching in S. coelicolor A3(2). Key results from labelling and cultivation experiments on either or both of the two carbon sources provided indicate that in the presence of D-glucose, L-glutamate was the preferred carbon source, while D-glucose alone appeared incapable of maintaining culture growth, likely due to a metabolic bottleneck at the oxidation of pyruvate to acetyl-CoA.

Description: Background: High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus. Results: Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFalpha, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells toverify the approach. The NPA scoring method successfully quantified the amplitude of TNFalpha-induced perturbation for each network model when compared against NF-kappaB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined. Conclusions: The NPA scoring method leverages high-throughput measurements and a priori literaturederived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.

Description: Background: The representation of a biochemical system as a network is the precursor of any mathematical model of the processes driving the dynamics of that system. Pharmacokinetics uses mathematical models to describe the interactions between drug, and drug metabolites and targets and through the simulation of these models predicts drug levels and/or dynamic behaviors of drug entities in the body. Therefore, the development of computational techniques for inferring the interaction network of the drug entities and its kinetic parameters from observational data is raising great interest in the scientic community of pharmacologists. In fact, the network inference is a set of mathematical procedures deducing the structure of a model from the experimental data associated to the nodes of the network of interactions. In this paper, we deal with the inference of a pharmacokinetic network from the concentrations of the drug and its metabolites observed at discrete time points. Results: The method of network inference presented in this paper is inspired by the theory of time-lagged correlation inference with regard to the deduction of the interaction network, and on a maximum likelihood approach with regard to the estimation of the kinetic parameters of the network. Both network inference and parameter estimation have been designed specically to identify systems of biotransformations, at the biochemical level, from noisy time-resolved experimental data. We use our inference method to deduce the metabolic pathway of the gemcitabine. The inputs to our inference algorithm are the experimental time series of the concentration of gemcitabine and its metabolites. The output is the set of reactions of the metabolic network of the gemcitabine. Conclusions: Time-lagged correlation based inference pairs up to a probabilistic model of parameter inference from metabolites time series allows the identication of the microscopic pharmacokinetics and pharmacodynamics of a drug with a minimal a priori knowledge. In fact, the inference model presented in this paper is completely unsupervised. It takes as input the time series of the concetrations of the parent drug and its metabolites. The method, applied to the case study of the gemcitabine pharmacokinetics, shows good accuracy and sensitivity

Description: Background: Complete transcriptional regulatory network inference is a huge challenge because of the complexity of the network and sparsity of available data. One approach to make it more manageable is to focus on the inference of context-specic networks involving a few interacting transcription factors (TFs) and all of their target genes. Results: We present a computational framework for Bayesian statistical inference of target genes of multiple interacting TFs from high-throughput gene expression time-series data. We use ordinary differential equation models that describe transcription of target genes taking into account combinatorial regulation. The method consists of a training and a prediction phase. During the training phase we infer the unobserved TF protein concentrations on a subnetwork of approximately known regulatory structure. During the prediction phase we apply Bayesian model selection on a genome-wide scale and score all alternativeregulatory structures for each target gene. We use our methodology to identify targets of ve TFs regulating Drosophila melanogaster mesoderm development. We nd that condent predicted links between TFs and targets are signicantly enriched for supporting ChIP-chip binding events and annotated TF-gene interations. Our method statistically signicantly outperforms existing alternatives. Conclusions: Our results show that it is possible to infer regulatory links between multiple interacting TFs and their target genes even from a single relatively short time series and in presence of unmodelled confounders and unreliable prior knowledge on training network connectivity. Introducing data from several different experimental perturbationssignicantly increases the accuracy.

Description: Background: Mathematical models play a key role in systems biology: they summarize the currently available knowledge in a way that allows to make experimentally verifiable predictions. Model calibration consists of finding the parameters that give the best fit to a set of experimental data, which entails minimizing a cost function that measures the goodness of this fit. Most mathematical models in systems biology present three characteristics which make this problem very difficult to solve: they are highly non-linear, they have a large number of parameters to be estimated, and the information content of the available experimental data is frequently scarce. Hence, there is a need for global optimization methods capable of solving this problem efficiently. Results: A new approach for parameter estimation of large scale models, called Cooperative Enhanced Scatter Search (CeSS), is presented. Its key feature is the cooperation between different programs ("threads") that run in parallel in different processors. Each thread implements a state of the art metaheuristic, the enhanced Scatter Search algorithm (eSS). Cooperation, meaning information sharing between threads, modifies the systemic properties of the algorithm and allows to speed up performance. Two parameter estimation problems involving models related with the central carbon metabolism of E. coli which include different regulatory levels (metabolic and transcriptional) are used as case studies. The performance and capabilities of the method are also evaluated using benchmark problems of large-scale global optimization, with excellent results. Conclusions: The cooperative CeSS strategy is a general purpose technique that can be applied to any model calibration problem. Its capability has been demonstrated by calibrating two large-scale models of different characteristics, improving the performance of previously existing methods in both cases. The cooperative metaheuristic presented here can be easily extended to incorporate other global and local search solvers and specific structural information for particular classes of problems.

Description: Background: Experiments in silico using stochastic reaction-diffusion models have emerged as an important tool in molecular systems biology. Designing computational software for such applications poses several challenges. Firstly, realistic lattice-based modeling for biological applications requires a consistent way of handling complex geometries, including curved inner- and outer boundaries. Secondly, spatiotemporal stochastic simulations are computationally expensive due to the fast time scales of in- dividual reaction- and diffusion events when compared to the biological phenomena of actual interest. We therefore argue that simulation software needs to be both compu- tationally efficient, employing sophisticated algorithms, yet in the same time flexible in order to meet present and future needs of increasingly complex biological modeling. Results: We have developed URDME, a flexible software framework for general stochastic reaction-transport modeling and simulation. URDME uses Unstructured triangular and tetrahedral meshes to resolve general geometries, and relies on the Reaction-Diffusion Master Equation formalism to model the processes under study. An interface to a mature geometry and mesh handling external software (Comsol Mul- tiphysics) provides for a stable and interactive environment for model construction. The core simulation routines are logically separated from the model building interface and written in a low-level language for computational efficiency. The connection to the geometry handling software is realized via a Matlab interface which facilitates script computing, data management, and post-processing. For practitioners, the software therefore behaves much as an interactive Matlab toolbox. At the same time, it is possi- ble to modify and extend URDME with newly developed simulation routines. Since the overall design effectively hides the complexity of managing the geometry and meshes, this means that newly developed methods may be tested in a realistic setting already at an early stage of development. Conclusions: In this paper we demonstrate, in a series of examples with high relevance to the molecular systems biology community, that the proposed software framework is a useful tool for both practitioners and developers of spatial stochastic simulation algorithms. Through the combined efforts of algorithm development and improved modeling accuracy, increasingly complex biological models become feasible to study through computational methods. URDME is freely available at http://www. urdme.org.

Description: Background: Eukaryotic cell proliferation involves DNA replication, a tightly regulated process mediatedby a multitude of protein factors. In budding yeast, the initiation of replication is facilitatedby the heterohexameric origin recognition complex (ORC). ORC binds to specific origins ofreplication and then serves as a scaffold for the recruitment of other factors such as Cdt1,Cdc6, the Mcm2-7 complex, Cdc45 and the Dbf4-Cdc7 kinase complex. While many of themechanisms controlling these associations are well documented, mathematical models areneeded to explore the network's dynamic behaviour. We have developed an ordinarydifferential equation-based model of the protein-protein interaction network describingreplication initiation. Results: The model was validated against quantified levels of protein factors over a range of cell cycletimepoints. Using chromatin extracts from synchronized Saccharomyces cerevisiae cellcultures, we were able to monitor the in vivo fluctuations of several of the aforementionedproteins, with additional data obtained from the literature. The model behaviour conforms toperturbation trials previously reported in the literature, and accurately predicts the results ofour own knockdown experiments. Furthermore, we successfully incorporated our replicationinitiation model into an established model of the entire yeast cell cycle, thus providing acomprehensive description of these processes. Conclusions: This study establishes a robust model of the processes driving DNA replication initiation. Themodel was validated against observed cell concentrations of the driving factors, andcharacterizes the interactions between factors implicated in eukaryotic DNA replication.Finally, this model can serve as a guide in efforts to generate a comprehensive model of themammalian cell cycle in order to explore cancer-related phenotypes.

Description: Background: Complex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments. Results: To facilitate the analysis of gene regulatory networks, we have developed a computational modeling pipeline method that complements traditional evaluation of experimental data. For a proof-of-concept example, we have focused on the gene regulatory network in the nematode C. elegans that mediates the developmental choice between mesodermal (muscle) and ectodermal (skin) cell fates in the embryonic C lineage. We have used gene expression data to build two models: a knowledge-driven model based on gene expression changes following gene perturbation experiments, and a data-driven mathematical model derived from time-course gene expression data recovered from wild-type animals. We show that both models can identify a rich set of network gene interactions. Importantly, the mathematical model built only from wild-type data can predict interactions demonstrated by the perturbation experiments better than chance, and better than an existing knowledge-driven model built from the same data set. The mathematical model also provides new biological insight, including a dissection of zygotic from maternal functions of a key transcriptional regulator, PAL-1, and identification of non-redundant activities of the T-box genes tbx-8 and tbx-9. Conclusions: This work provides a strong example for a mathematical modeling approach that solely uses wild-type data to predict an underlying gene regulatory network. The modeling approach complements traditional methods of data analysis, suggesting non-intuitive network relationships and guiding future experiments.

Description: Retinoic acid (RA) is a morphogen derived from retinol (vitamin A) that plays important roles in cell growth, differentiation, and organogenesis. The production of RA from retinol requires two consecutive enzymatic reactions catalyzed by different sets of dehydrogenases. The retinol is first oxidized into retinal, which is then oxidized into RA. The RA interacts with retinoic acid receptor (RAR) and retinoic acid X receptor (RXR) which then regulate the target gene expression. In this review, we have discussed the metabolism of RA and the important components of RA signaling pathway, and highlighted current understanding of the functions of RA during early embryonic development.

Description: TGF-β signaling regulates diverse cellular processes, including cell proliferation, differentiation, apoptosis, cell plasticity and migration. Its dysfunctions can result in various kinds of diseases, such as cancer and tissue fibrosis. TGF-β signaling is tightly regulated at different levels along the pathway, and modulation of TGF-β receptor activity is a critical step for signaling regulation. This review focuses on our recent understanding of regulation of TGF-β receptor activity.

Description: Mesenchymal stem cells (MSCs) have acquired great interests for their potential use in the clinical therapy of many diseases because of their functions including multiple lineage differentiation, low immunogenicity and immunosuppression. Many studies suggest that MSCs are strongly immunosuppressive in vitro and in vivo. MSCs exert a profound inhibitory effect on the proliferation of T cells, B cells, dendritic cells and natural killer cells. In addition, several soluble factors have been reported to involved in the immunosuppressive effects by MSCs such as TGF-β, HGF, PGE2, IDO and iNOS. These results suggest that MSCs can be used in the therapy of immune disorder diseases, prevention of organ transplantation rejection and tissue injury. In recent study, we demonstrated that MSCs in tumor inflammatory microenvironment might be elicited of immunosuppressive function. Thus, the application of MSCs in cancer therapy might have negative effect by helping tumor cells escaping from the immune surveillance.

Description: Background: About 15 types of human papillomavirus (HPV) are classified as high-risk based on their epidemiological link with cervical cancer. These HPV types have deferent degrees of oncogenicity and their distribution among cervical precancers and cancers varies ethnogeographically. HPV58 is rare worldwide but being found more commonly in East Asia.FindingsA high prevalence of HPV58 among squamous cell carcinoma has been reported from China (28% in Shanghai, 10% in Hong Kong and 10% in Taiwan) and other countries in East Asia including Korea (16%) and Japan (8%). HPV58 ranks the third in Asia overall, but contributes to only 3.3% of cervical cancers globally. The reasons for a difference in disease attribution may lie on the host as well as the virus itself. HLA-DQB1*06 was found to associate with a higher risk of developing HPV58-positive cervical neoplasia in Hong Kong women, but not neoplasia caused by other HPV types. An HPV58 variant (E7 T20I, G63S) commonly detected in Hong Kong was found to confer a 6.9-fold higher risk of developing cervical cancer compared to other variants. A study involving 15 countries/cities has shown a predilection in the distribution of HPV58 variant lineages. Sublineage A1, the prototype derived from a cancer patient in Japan, was rare worldwide except in Asia. Conclusions: HPV58 accounts for a larger share of disease burden in East Asia, which may be a result of differences in host genetics as well as the oncogenicity of circulating variants. These unique characteristics of HPV58 should be considered in the development of next generation vaccines and diagnostic assays.