CoRegNet is an R/Bioconductor package to analyze large-scale transcriptomic data by highlighting sets of co-regulators. Based on a transcriptomic dataset, CoRegNet can be used to: reconstruct a large-scale co-regulatory network, integrate regulation evidences such as transcription factor binding sites and ChIP data, estimate sample-specific regulator activity, identify cooperative transcription factors and analyze the sample-specific combinations of active regulators through an interactive visualization tool. In this study CoRegNet was used to identify driver regulators of bladder cancer. Availability: CoRegNet is available at http://bioconductor.org/packages/CoRegNet Contact: remy.nicolle@issb.genopole.fr or mohamed.elati@issb.genopole.fr Supplementary inform a tion: Supplementary data are available at Bioinformatics online.

Motivation: One of the most challenging tasks in the post-genomic era is the reconstruction of transcriptional regulation networks. The goal is to identify, for each gene expressed in a particular cellular context, the regulators affecting its transcription, and the co-ordination of several regulators in specific types of regulation. DNA microarrays can be used to investigate relationships between regulators and their target genes, through simultaneous observations of their RNA levels. Results: We propose a data mining system for inferring transcriptional regulation relationships from RNA expression values. This system is particularly suitable for the detection of cooperative transcriptional regulation. We model regulatory relationships as labelled two-layer gene regulatory networks, and describe a method for the efficient learning of these bipartite networks from discretized expression data sets. We also evaluate the statistical significance of such inferred networks and validate our methods on two public yeast expression data sets. Availability:http://www.lri.fr/~elati/licorn.html Contact:mohamed.elati@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online.

The mechanisms underlying the heterogeneity of clinical malaria remain largely unknown. We hypothesized that differential gene expression contributes to phenotypic variation of parasites which results in a specific interaction with the host, leading to different clinical features of malaria. In this study, we analyzed the transcriptomes of isolates obtained from asymptomatic carriers and patients with uncomplicated or cerebral malaria. We also investigated the transcriptomes of 3D7 clone and 3D7-Lib that expresses severe malaria associated-variant surface antigen. Our findings revealed a specific up-regulation of genes involved in pathogenesis, adhesion to host cell, and erythrocyte aggregation in parasites from patients with cerebral malaria and 3D7-Lib, compared to parasites from asymptomatic carriers and 3D7, respectively. However, we did not find any significant difference between the transcriptomes of parasites from cerebral malaria and uncomplicated malaria, suggesting similar transcriptomic pattern in these two parasite populations. The difference between isolates from asymptomatic children and cerebral malaria concerned genes coding for exported proteins, Maurer's cleft proteins, transcriptional factor proteins, proteins implicated in protein transport, as well as Plasmodium conserved and hypothetical proteins. Interestingly, UPs A1, A2, A3 and UPs B1 of var genes were predominantly found in cerebral malaria-associated isolates and those containing architectural domains of DC4, DC5, DC13 and their neighboring rif genes in 3D7-lib. Therefore, more investigations are needed to analyze the effective role of these genes during malaria infection to provide with new knowledge on malaria pathology. In addition, concomitant regulation of genes within the chromosomal neighborhood suggests a common mechanism of gene regulation in P. falciparum.

Significance: Systems biology involves the development of large computational models of biological systems. The radical improvement of systems biology models will necessarily involve the automation of model improvement cycles. We present here a general approach to automating systems biology model improvement. Humans are eukaryotic organisms, and the yeast Saccharomyces cerevisiae is widely used in biology as a “model” for eukaryotic cells. The yeast diauxic shift is the most studied cellular transformation. We combined multiple software tools with integrated laboratory robotics to execute three semiautomated cycles of diauxic shift model improvement. All the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for execution. The resulting improved model is relevant to understanding cancer, the immune system, and aging.

Genome-scale metabolic models provide an opportunity for rational approaches to studies of the different reactions taking place inside the cell. The integration of these models with gene regulatory networks is a hot topic in systems biology. The methods developed to date focus mostly on resolving the metabolic elements and use fairly straightforward approaches to assess the impact of genome expression on the metabolic phenotype. We present here a method for integrating the reverse engineering of gene regulatory networks into these metabolic models. We applied our method to a high-dimensional gene expression data set to infer a background gene regulatory network. We then compared the resulting phenotype simulations with those obtained by other relevant methods. Our method outperformed the other approaches tested and was more robust to noise. We also illustrate the utility of this method for studies of a complex biological phenomenon, the diauxic shift in yeast.