A Bayesian active learning strategy for sequential experimental design in systems biology

摘要

BackgroundDynamical models used in systems biology involve unknown kinetic parameters. Setting these parameters is a bottleneck in many modeling projects. This motivates the estimation of these parameters from empirical data. However, this estimation problem has its own difficulties, the most important one being strong ill-conditionedness. In this context, optimizing experiments to be conducted in order to better estimate a system’s parameters provides a promising direction to alleviate the difficulty of the task.