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A rule-based model of insulin signalling pathway

Background The insulin signalling pathway (ISP) is an important biochemical pathway, which regulates some fundamental biological functions such as glucose and lipid metabolism, protein synthesis, cell proliferation, cell differentiation and apoptosis. In the last years, different mathematical models based on ordinary differential equations have been proposed in the literature to...

Leucine modulates dynamic phosphorylation events in insulin signaling pathway and enhances insulin-dependent glycogen synthesis in human skeletal muscle cells

Background Branched-chain amino acids, especially leucine, are known to interact with insulin signaling pathway and glucose metabolism. However, the mechanism by which this is exerted, remain to be clearly defined. In order to examine the effect of leucine on muscle insulin signaling, a set of experiments was carried out to quantitate phosphorylation events along the insulin...

Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming

Motivation: Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible...

Integrating literature-constrained and data-driven inference of signalling networks

Motivation: Recent developments in experimental methods facilitate increasingly larger signal transduction datasets. Two main approaches can be taken to derive a mathematical model from these data: training a network (obtained, e.g., from literature) to the data, or inferring the network from the data alone. Purely data-driven methods scale up poorly and have limited...

A Boolean Approach to Linear Prediction for Signaling Network Modeling

The task of the DREAM4 (Dialogue for Reverse Engineering Assessments and Methods) “Predictive signaling network modeling” challenge was to develop a method that, from single-stimulus/inhibitor data, reconstructs a cause-effect network to be used to predict the protein activity level in multi-stimulus/inhibitor experimental conditions. The method presented in this paper, one of...

Prediction of human population responses to toxic compounds by a collaborative competition

), RWTH Aachen University, Aachen, Germany. Federica Eduati, Lara M Mangravite, Tao Wang & Hao Tang These authors contributed equally to this work. AffiliationsEuropean Molecular Biology Laboratory ... Zheng & Dai Ziwei Challenge organizers:  Federica Eduati, Lara M Mangravite, J Christopher Bare, Thea Norman, Mike Kellen, Michael P Menden, Stephen Friend, Gustavo Stolovitzky & Julio Saez-Rodriguez