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

Edouard Pauwels,Christian Lajaunie,Jean-Philippe Vert

doi:10.1186/s12918-014-0102-6

Abstract

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.ResultsBorrowing 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.ConclusionThis 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-014-0102-6) contains supplementary material, which is available to authorized users.

Highlights

Dynamical models used in systems biology involve unknown kinetic parameters
Ordinary differential equation(ODE) based models, which are the focus of this work, have proved very useful to model numerous regulatory, signaling and metabolic pathways [2,3,4], including for example the cell cycle in budding yeast [5], the regulatory module of nuclear factor κB (NF-κB) signaling pathway [6, 7], the MAP kinase signaling pathways [8] or the caspase function in apoptosis [9]
In silico network description In order to evaluate the relevance of our new sequential Bayesian optimal experimental design (OED) strategy in the context of systems biology, we test it on an in silico network proposed in the DREAM7 Network Topology and Parameter Inference Challenge which we describe [49]

Summary

Introduction

Dynamical models used in systems biology involve unknown kinetic parameters. Setting these parameters is a bottleneck in many modeling projects. Ordinary differential equation(ODE) based models, which are the focus of this work, have proved very useful to model numerous regulatory, signaling and metabolic pathways [2,3,4], including for example the cell cycle in budding yeast [5], the regulatory module of nuclear factor κB (NF-κB) signaling pathway [6, 7], the MAP kinase signaling pathways [8] or the caspase function in apoptosis [9] Such dynamical models involve unknown parameters, such as kinetic parameters, that one must guess from prior knowledge or estimate from experimental data in order to analyze and simulate the model. When the system has more than a few unknown parameters, computational issues arise to efficiently sample the space of parameters [21, 22], which has been found to be very rugged and sometimes misleading in the sense that many sets of parameters that have a good fit to experimental data are meaningless from a biological point of view [23]

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