Section: New Results

Data and knowledge integration based on combinatorial optimization

Participants : Marie Chevallier, Damien Eveillard, Jeanne Got, Julie Laniau, François Moreews, Jacques Nicolas, Anne Siegel.

Packing graphs with ASP for landscape simulation This study is part of a more general research track on graph compression, a fundamental issue for the analysis of biological networks that we address with Answer Set Programming (ASP)modelling. The general issue is to cover a given graph by a set of subgraphs. The IJCAI paper describes an application to crop allocation for generating realistic landscapes. The aim is to cover optimally a bare landscape, represented by its plot graph, with spatial patterns describing local arrangements of crops. This problem belongs to the hard class of graph packing problems. The approach provides a compact solution to the basic problem and at the same time allows extensions such as a flexible integration of expert knowledge. Particular attention is paid to the treatment of symmetries, especially due to sub-graph isomorphism issues. Experiments were conducted on a database of simulated and real landscapes. Currently, our program can process graphs of medium size, a size that enables studies on real agricultural practices. [J. Nicolas] [29]

Deciphering transcriptional regulations coordinating the response to environmental changes We introduce a method that extracts from a transcriptional regulatory network determined from a set of predicted transcription factors (TF) and binding site (BS) a subnetwork explaining a given set of observed co-expressions, highlighting those TFs and BSs most likely involved in the co-regulation. The method solves an optimization problem on a graph to select confident paths within the given transcriptional regulatory network joining a putative common regulator with two co-expressed genes via regulatory cascades. It provides a useful modeling scheme for deciphering the regulatory mechanisms that underly the phenotypical response of an organism to environmental challenges and can be used as a reliable tool for further research on genome scale transcriptional regulation studies. [M. Chevallier, D. Eveillard, A. Siegel] [13]

Putative bacterial interactions from metagenomic knowledge with an integrative systems ecology approach. Our software tool shogen [62] was used to decipher functional roles within a consortium of five mining bacteria through the integration of genomic and metabolic knowledge at genome scale. We first reconstructed a global metabolic network. Next, using a parsimony assumption, we deciphered sets of genes, called Sets from Genome Segments (SGS), that (i) are close on their respective genomes, (ii) take an active part in metabolic pathways and (iii) whose associated metabolic reactions are also closely connected within metabolic networks. The use of SGS (shogen) pinpoints a functional compartmentalization among the investigated species and exhibits putative bacterial interactions necessary for promoting these pathways. [M. Chevallier, D. Eveillard, A. Siegel] [15]

Molecular alterations induced by a high-fat high-fiber diet in porcine adipose tissues: variations according to the anatomical fat location Our methods based on the integration of metabolic and regulatory regulations [61] were combined to statistical approaches and applied to the understanding of fatty acid metabolism in porcs and chicken. The analyses evidenced that a high-fat high-fiber diet depressed glucose and lipid anabolic molecular pathways, thus counteracting adipose tissue expansion. Interaction effects between dietary intake of fiber and lipids on gene expression may modulate innate immunity and inflammation, a response which is of interest with regard to chronic inflammation and its adverse effects on health and performance. [F. Moreews, A. Siegel] [20]