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  • 1
    Publication Date: 2008-10-31
    Description: One defining goal of synthetic biology is the development of engineering-based approaches that enable the construction of gene-regulatory networks according to 'design specifications' generated from computational modelling. This approach provides a systematic framework for exploring how a given regulatory network generates a particular phenotypic behaviour. Several fundamental gene circuits have been developed using this approach, including toggle switches and oscillators, and these have been applied in new contexts such as triggered biofilm development and cellular population control. Here we describe an engineered genetic oscillator in Escherichia coli that is fast, robust and persistent, with tunable oscillatory periods as fast as 13 min. The oscillator was designed using a previously modelled network architecture comprising linked positive and negative feedback loops. Using a microfluidic platform tailored for single-cell microscopy, we precisely control environmental conditions and monitor oscillations in individual cells through multiple cycles. Experiments reveal remarkable robustness and persistence of oscillations in the designed circuit; almost every cell exhibited large-amplitude fluorescence oscillations throughout observation runs. The oscillatory period can be tuned by altering inducer levels, temperature and the media source. Computational modelling demonstrates that the key design principle for constructing a robust oscillator is a time delay in the negative feedback loop, which can mechanistically arise from the cascade of cellular processes involved in forming a functional transcription factor. The positive feedback loop increases the robustness of the oscillations and allows for greater tunability. Examination of our refined model suggested the existence of a simplified oscillator design without positive feedback, and we construct an oscillator strain confirming this computational prediction.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Stricker, Jesse -- Cookson, Scott -- Bennett, Matthew R -- Mather, William H -- Tsimring, Lev S -- Hasty, Jeff -- GM69811-01/GM/NIGMS NIH HHS/ -- R01 GM069811/GM/NIGMS NIH HHS/ -- England -- Nature. 2008 Nov 27;456(7221):516-9. doi: 10.1038/nature07389. Epub 2008 Oct 29.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/18971928" target="_blank"〉PubMed〈/a〉
    Keywords: Computer Simulation ; Escherichia coli/*genetics ; Feedback ; Flow Cytometry ; *Gene Expression Regulation, Bacterial ; Gene Regulatory Networks/*genetics ; Genes, Synthetic/*genetics ; *Genetic Engineering ; Luminescent Measurements ; Microfluidic Analytical Techniques ; Models, Genetic ; *Periodicity ; Sensitivity and Specificity ; Time Factors ; Transcription Factors/metabolism
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 2
    Publication Date: 2012-04-28
    Description: Motivation: Many important aspects of evolutionary dynamics can only be addressed through simulations. However, accurate simulations of realistically large populations over long periods of time needed for evolution to proceed are computationally expensive. Mutants can be present in very small numbers and yet (if they are more fit than others) be the key part of the evolutionary process. This leads to significant stochasticity that needs to be accounted for. Different evolutionary events occur at very different time scales: mutations are typically much rarer than reproduction and deaths. Results: We introduce a new exact algorithm for fast fully stochastic simulations of evolutionary dynamics that include birth, death and mutation events. It produces a significant speedup compared to direct stochastic simulations in a typical case when the population size is large and the mutation rates are much smaller than birth and death rates. The algorithm performance is illustrated by several examples that include evolution on a smooth and rugged fitness landscape. We also show how this algorithm can be adapted for approximate simulations of more complex evolutionary problems and illustrate it by simulations of a stochastic competitive growth model. Contact: ltsimring@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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