Gene expression data generated systematically in a given system over multiple time points provides a source of perturbation that can be leveraged to infer causal relationships among genes explaining network changes. Previously, we showed that food intake has a large impact on blood gene expression patterns and that these responses, either in terms of gene expression level or gene-gene connectiv...

Large scale gene perturbation experiments generate information about the number of genes whose activity is directly or indirectly affected by a gene perturbation. From this information, one can numerically estimate coarse structural network features such as the total number of direct regulatory interactions and the number of isolated subnetworks in a transcriptional regulation network. Applied ...

Motivation: Development of mathematical models that adequately describe moleculargenetic mechanisms is one of the main tasks in modern bioinformatics. Not only should a model be sensitive to the particulars of enzyme reactions underlying cell life activity, but also to regulation exerted on enzyme activity by metabolites and specialized cell proteins. Results: The gene network for branched-chai...

We present a weighted-LASSO method to infer the parameters of a first-order vector auto-regressive model that describes time course expression data generated by directed gene-to-gene regulation networks. These networks are assumed to own prior internal structures of connectivity which drive the inference method. This prior structure can be either derived from prior biological knowledge or infer...