Section: New Results

Systems Biology

Participants : Anne Siegel [contact] , Jérémie Bourdon, Michel Le Borgne, Nathalie Theret, Geoffroy Andrieux, Oumarou Abdou-Arbi, Sylvain Prigent, Pierre Blavy, Andres Aravena, Santiago Videla, Valentin Wucher, Brivael Trelhu.

• Average-case analysis for quantitative data integration We proposed a probabilistic modeling framework that integrates heterogeneous data. Average case analysis methods were used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations. The approach was illustrated by modeling the carbon starvation response in Escherichia coli. It accurately predicted 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 [5] . [Online publication: http://dx.plos.org/10.1371/journal.pcbi.1002157]

• Combining genetic and metabolic regulations: We mixed Gale-Nikaido reduction steps and differential inequalities to understand how genetic regulation modifies the behavior of a very abstracted model of lipid metabolism [18] [Online publication: http://www.springerlink.com/content/n437048670560782/]

• Extract relevant information with respect to a cancer phenotype: We designed dedicated logical rules to model the static response of biomolecular interactions implied in the cancer network. This allowed us to trace back genes implied in the cancer phenotype [12] . [Online publication: http://www.computer.org/portal/web/csdl/doi/10.1109/TCBB.2010.71]

• Integrative biology for brown algal We proposed a protocol focusing on integrating heterogeneous knowledge gained on brown algal metabolism. The resulting abstraction of the system helps understanding how brown algae cope with changes in abiotic parameters within their unique habitat [19] .

• Search for key regulators A method was proposed to model the effects of all transcriptional and metabolic regulations contained in transpath in a single influence network. The network was analyzed to find a set of candidates that explain the variations of a set of targets [34] .

• Identification of co-regulation patterns. We introduced a new approach based on the compilation of Simple Shared Motifs (SSM), groups of sequences defined by their length and similarity and present in conserved sequences of gene promoters. We proved that Simple Shared Motifs analysis provides a clearer definition of expression networks [10] . [Online publication: http://www.biomedcentral.com/1471-2105/12/365]

• Probabilistic models for systems biology We reviewed, in a book chapter, some classical concepts concerning probabilistic models and their applications in systems biology. Probabilistic boolean networks were presented in deep with a focus on the effect of synchronization of genes and on stochastic simulation of such networks [36] .