BMC Systems Biology

, 8:102 | Cite as

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

  • Edouard Pauwels
  • Christian Lajaunie
  • Jean-Philippe Vert
Methodology Article
Part of the following topical collections:
  1. Methods, software and technology



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.


Systems 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.

Supplementary material

12918_2014_102_MOESM1_ESM.pdf (229 kb)
Additional file 1: Annex B. Supplementary details regarding the sampling strategy used in our numerical experiments. The note also contains diagnosis information and marginal distribution samples to illustrate the efficacy of the sampling strategy in the setting of this paper. (PDF 229 KB)
12918_2014_102_MOESM2_ESM.pdf (102 kb)
Additional file 2: Annex A. PDF file. Description of the DREAM7 challenge network represented in Figure 2 and experimental design setting. (PDF 102 KB)
12918_2014_102_MOESM3_ESM.gif (9 kb)
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Authors’ original file for figure 6


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Copyright information

© Pauwels et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Edouard Pauwels
    • 1
    • 2
  • Christian Lajaunie
    • 3
    • 4
    • 5
  • Jean-Philippe Vert
    • 3
    • 4
    • 5
  1. 1.CNRS, LAASToulouseFrance
  2. 2.Univ de Toulouse LAASToulouseFrance
  3. 3.MINES ParisTech, PSL-Research University, CBIO-Centre for Computational BiologyFontainebleauFrance
  4. 4.Institut CurieParisFrance
  5. 5.INSERM U900ParisFrance

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