Ensembles of signal transduction models using Pareto Optimal Ensemble Techniques (POETs).

ABSTRACT: Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model ensembles using multiobjective optimization. In this study, we used Pareto Optimal Ensemble Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass-action kinetics within an ordinary differential equation (ODE) framework (

SUBMITTER: Song SO 

PROVIDER: S-EPMC3021968 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature