Integrating quantitative knowledge into a qualitative gene regulatory network.

PLoS Computational Biology. 2011;7(9):e1002157 DOI 10.1371/journal.pcbi.1002157

 

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Journal Title: PLoS Computational Biology

ISSN: 1553-734X (Print); 1553-7358 (Online)

Publisher: Public Library of Science (PLoS)

LCC Subject Category: Science: Biology (General)

Country of publisher: United States

Language of fulltext: English

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AUTHORS


Jérémie Bourdon

Damien Eveillard

Anne Siegel

EDITORIAL INFORMATION

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Instructions for authors

Time From Submission to Publication: 32 weeks

 

Abstract | Full Text

Despite recent improvements in molecular techniques, biological knowledge remains incomplete. Any theorizing about living systems is therefore necessarily based on the use of heterogeneous and partial information. Much current research has focused successfully on the qualitative behaviors of macromolecular networks. Nonetheless, it is not capable of taking into account available quantitative information such as time-series protein concentration variations. The present work proposes a probabilistic modeling framework that integrates both kinds of information. Average case analysis methods are used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations. The approach is illustrated by modeling the carbon starvation response in Escherichia coli. It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration. From this, the modeling technique also derives a ranking of interactions with respect to their importance during the experiment considered. Such a classification is confirmed by the literature. Therefore, our method is principally novel in that it allows (i) a hybrid model that integrates both qualitative discrete model and quantities to be built, even using a small amount of quantitative information, (ii) new quantitative predictions to be derived, (iii) the robustness and relevance of interactions with respect to phenotypic criteria to be precisely quantified, and (iv) the key features of the model to be extracted that can be used as a guidance to design future experiments.