ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Articles  (2)
  • Oxford University Press  (2)
  • Soil Science Society of America
  • 2010-2014  (2)
  • Glycobiology  (1)
  • Briefings in Bioinformatics  (1)
  • 2589
  • 9058
Collection
  • Articles  (2)
Publisher
  • Oxford University Press  (2)
  • Soil Science Society of America
Years
  • 2010-2014  (2)
Year
  • 1
    Publication Date: 2012-11-02
    Description: The raffinose family oligosaccharides (RFOs), such as raffinose and stachyose, are synthesized by a set of distinct galactosyltransferases, which sequentially add galactose units to sucrose. The accumulation of RFOs in plant cells are closely associated with the responses to environmental factors, such as cold, heat and drought stresses. Systematic analysis of genes involved in the raffinose metabolism has not been reported to date. Searching the recently available working draft of the maize genome, six kinds of enzyme genes were speculated, which should encode all the enzymes involved in the raffinose metabolism in maize. Expression patterns of some related putative genes were analyzed. The conserved domains and phylogenetic relationships among the deduced maize proteins and their homologs isolated from other plant species were revealed. It was discovered that some of the key enzymes, such as galactinol synthase (ZmGolS5, ZmGolS45 and ZmGolS37), raffinose synthase (ZmRS1, ZmRS2, ZmRS3 and ZmRS10), stachyose synthase (ZmRS8) and β-fructofuranosidase, are encoded by multiple gene members with different expression patterns. These results reveal the complexity of the raffinose metabolism and the existence of metabolic channels for diverse RFOs in maize and provide useful information for improving maize stress tolerance through genetic engineering.
    Print ISSN: 0959-6658
    Electronic ISSN: 1460-2423
    Topics: Biology , Medicine
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 2
    Publication Date: 2014-09-24
    Description: With the increase of available protein–protein interaction (PPI) data, more and more efforts have been put to PPI network modeling, and a number of models of PPI networks have been proposed. Roughly speaking, good models of PPI networks should be able to accurately describe PPI mechanisms, and thus reproduce the structures of PPI networks. With such models, theoretical and/or computational biologists can efficiently explore the evolution and dynamics of PPI networks. However, a theoretical and/or computational biologist may feel confused when she/he has to choose a proper PPI model for her/his research work from a dozen of candidate models, while there is no guideline available to help her/him. To tackle this problem, in this article, we carry out a comprehensive performance comparison study on 12 existing models over PPI datasets of four species (yeast, mouse, fruit fly and nematode), by comparing the global and local statistical properties of the original PPI networks and the model-reproduced ones. To draw more convincing conclusions, we use the mean reciprocal rank to combine the ranks of a certain model on all statistical properties. Our experimental results indicate that the PS_Seed model [Solé and Pastor-Satorras (PS) model with seed] the STICKY model and the DD_Seed model (Duplication-Divergence model with seed) fit best with the test PPI datasets. By analyzing the underlying mechanisms of the models with better fitting ability, our analysis shows that the evolutionary mechanism of node duplication and link dynamics and the mechanisms with ‘degree-weighted’ behaviors seem to be able to describe the PPI networks better.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...