(541 d) Identification of Families of Signal Transduction Models Using Pareto Optimal Ensemble Techniques (POETs)

摘要

Mathematical modeling of complex signal transduction and gene expression programs is an emerging tool for understanding disease mechanisms. However, conventional wisdom suggests that the data requirement to identify and validate complex mechanistic models is too large. Typically, it is not possible to uniquely identify model parameters, even with extensive training data and perfect models. This reality has brought into the foreground a number of interesting questions. For example, do we actually need exact parameter knowledge to predict qualitatively important properties of a molecular network? Ensemble approaches, which use parametrically and potentially even structurally uncertain model families, have emerged to deal with uncertainty in systems biology and other fields like weather prediction. Their central value has been the ability to quantify simulation uncertainty and to constrain model predictions, despite sometimes only order-of-magnitude parameter estimates. In this study, we introduce Pareto Optimal Ensemble Techniques (POETs) to identify a family of simplified proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to estimate parameter sets on or near the optimal tradeoff surface between competing training objectives.