Guilhem Richard

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Mathematical models of biochemical networks, such as metabolic, signaling, and gene networks, have been studied extensively and have been shown to provide accurate descriptions of various cell processes. Nevertheless, their usage is restricted by the fact that they are usually studied in isolation, without feedback from the environment in which they evolve.(More)
The Toll-Like Receptors (TLRs) are proteins involved in the immune system that increase cytokine levels when triggered. While cytokines coordinate the response to infection, they appear to be detrimental to the host when reaching too high levels. Several studies have shown that the deletion of specific TLRs was beneficial for the host, as cytokine levels(More)
We describe the reconstruction of a gene regulatory network involved with the Toll-like Receptor signaling pathways. By applying our recent identification algorithm to a time series gene expression dataset, we identify regulatory interactions between genes and construct discrete-time piece-wise affine regulatory functions. Our validation shows that our(More)
Obesity is a chronic inflammatory disease that weakens macrophage innate immune response to infections. Since M1 polarization is crucial during acute infectious diseases, we hypothesized that diet-induced obesity inhibits M1 polarization of macrophages in the response to bacterial infections. Bone marrow macrophages (BMMΦ) from lean and obese mice were(More)
With the advent of next generation genome sequencing, the number of sequenced algal genomes and transcriptomes is rapidly growing. Although a few genome portals exist to browse individual genome sequences, exploring complete genome information from multiple species for the analysis of user-defined sequences or gene lists remains a major challenge.(More)
This paper is concerned with the problem of identifying a discrete-time dynamical system model for a gene regulatory network with unknown topology using time series gene expression data. The topology of such a network can be characterized by a set of regulation hypotheses, one for each gene. In our earlier work, we formulated a convex optimization method to(More)
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