A Bayesian active learning strategy for sequential experimental design in systems biology
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Dynamical 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.
Borrowing ideas from Bayesian experimental design and active learning, we propose a new strategy for optimal experimental design in the context of kinetic parameter estimation in systems biology. We describe algorithmic choices that allow to implement this method in a computationally tractable way and make it fully automatic. Based on simulation, we show that it outperforms alternative baseline strategies, and demonstrate the benefit to consider multiple posterior modes of the likelihood landscape, as opposed to traditional schemes based on local and Gaussian approximations.
This analysis demonstrates that our new, fully automatic Bayesian optimal experimental design strategy has the potential to support the design of experiments for kinetic parameter estimation in systems biology.
KeywordsSystems biology Kinetic parameter estimation Active learning Bayesian experimental design
The authors would like to thank Gautier Stoll for insightful discussions. This work was supported by the European Research Council (SMAC-ERC-280032). Most of this work was carried out during EP’s PhD at Mines ParisTech.
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