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Tucker-Kellogg L.,Computational Systems Biology Programme | Tucker-Kellogg L.,National University of Singapore | Shi Y.,Computational Systems Biology Programme | White J.K.,Computational Systems Biology Programme | And 3 more authors.
Biochemical Pharmacology | Year: 2012

Background: The compound LY303511 (LY30) has been proven to induce production of ROS and to sensitize cancer cells to TRAIL-induced apoptosis, but the mechanisms and mediators of LY30-induced effects are potentially complex. Bayesian networks are a modelling technique for making probabilistic inferences about complex networks of uncertain causality. Methods: Fluorescent indicators for ROS, reactive nitrogen species (RNS), and free calcium were measured in time-series after LY30 treatment. This "correlative" dataset was used as input for Bayesian modelling to predict the causal dependencies among the measured species. Predictions were compared against a separate "causal" dataset, in which cells had been treated with FeTPPS to scavenge peroxynitrite, EGTA-am to chelate calcium, and Tiron to scavenge O 2 -. Finally, cell viability measurements were integrated into an extended model of LY30 effects. Results: LY30 treatment caused a rapid increase of ROS (measured by DCFDA) as well as a significant increase in RNS and calcium. Bayesian modelling predicted that Ca2+was a partial cause of the ROS induced by short incubations with LY30, and that RNS was strongly responsible for the ROS induced by long incubations with LY30. Validation experiments confirmed the predicted roles of RNS and calcium, and also demonstrated a causal role for O2 -. In cell viability experiments, the additive effects of calcium and peroxynitrite were responsible for 90% of LY30-mediated sensitization to TRAIL-induced apoptosis. Conclusions: We conclude that LY30 induces interdependent pathways of reactive species and stress signalling, with peroxynitrite and calcium contributing most significantly to apoptosis sensitization. © 2012 Elsevier Inc. Source

Nim T.H.,Computational Systems Biology Programme | Luo L.,National University of Singapore | Clement M.-V.,National University of Singapore | White J.K.,Computational Systems Biology Programme | And 4 more authors.
Bioinformatics | Year: 2013

Motivation: Computational models of biological signalling networks, based on ordinary differential equations (ODEs), have generated many insights into cellular dynamics, but the model-building process typically requires estimating rate parameters based on experimentally observed concentrations. New proteomic methods can measure concentrations for all molecular species in a pathway; this creates a new opportunity to decompose the optimization of rate parameters.Results: In contrast with conventional parameter estimation methods that minimize the disagreement between simulated and observed concentrations, the SPEDRE method fits spline curves through observed concentration points, estimates derivatives and then matches the derivatives to the production and consumption of each species. This reformulation of the problem permits an extreme decomposition of the high-dimensional optimization into a product of low-dimensional factors, each factor enforcing the equality of one ODE at one time slice. Coarsely discretized solutions to the factors can be computed systematically. Then the discrete solutions are combined using loopy belief propagation, and refined using local optimization. SPEDRE has unique asymptotic behaviour with runtime polynomial in the number of molecules and timepoints, but exponential in the degree of the biochemical network. SPEDRE performance is comparatively evaluated on a novel model of Akt activation dynamics including redox-mediated inactivation of PTEN (phosphatase and tensin homologue). © 2013 The Author. Source

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