Reverse engineering and identification in systems biology: strategies, perspectives and challenges

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Abstract

The interplay of mathematical modelling with experiments is one of the central elements in systems biology. The aim of reverse engineering is to infer, analyse and understand, through this interplay, the functional and regulatory mechanisms of biological systems. Reverse engineering is not exclusive of systems biology and has been studied in different areas, such as inverse problem theory, machine learning, nonlinear physics, (bio)chemical kinetics, control theory and optimization, among others. However, it seems that many of these areas have been relatively closed to outsiders. In this contribution, we aim to compare and highlight the different perspectives and contributions from these fields, with emphasis on two key questions: (i) why are reverse engineering problems so hard to solve, and (ii) what methods are available for the particular problems arising from systems biology?

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Most cited references 110

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A gene-coexpression network for global discovery of conserved genetic modules.

Joshua M. Stuart, Eran Segal, Daphne Koller … (2003)

To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.

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Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood.

A. Raue, C. Kreutz, T Maiwald … (2009)

Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis. We suggest an approach that exploits the profile likelihood. It enables to detect structural non-identifiabilities, which manifest in functionally related model parameters. Furthermore, practical non-identifiabilities are detected, that might arise due to limited amount and quality of experimental data. Last but not least confidence intervals can be derived. The results are easy to interpret and can be used for experimental planning and for model reduction. An implementation is freely available for MATLAB and the PottersWheel modeling toolbox at http://web.me.com/andreas.raue/profile/software.html. Supplementary data are available at Bioinformatics online.

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Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems.

Tina Toni, David Welch, Natalja Strelkowa … (2009)

Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.

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Author and article information

Journal

Journal ID (nlm-ta): J R Soc Interface

Journal ID (iso-abbrev): J R Soc Interface

Journal ID (publisher-id): RSIF

Journal ID (hwp): royinterface

Title: Journal of the Royal Society Interface

Publisher: The Royal Society

ISSN (Print): 1742-5689

ISSN (Electronic): 1742-5662

Publication date (Print): 6 February 2014

Publication date PMC-release: 6 February 2014

Volume: 11

Issue: 91

Electronic Location Identifier: 20130505

Affiliations

BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council , Vigo 36208, Spain

Author notes

e-mail: julio@ 123456iim.csic.es

Article

Publisher ID: rsif20130505

DOI: 10.1098/rsif.2013.0505

PMC ID: 3869153

PubMed ID: 24307566

SO-VID: 8f3982b6-4c10-4f17-a026-984c53feb2e5

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© 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.

History

Date received : 7 June 2013

Date accepted : 12 November 2013

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cover-date February 6, 2014

ScienceOpen disciplines: Life sciences

Keywords: reverse engineering,identification,inference,dynamic modelling,systems biology

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ScienceOpen disciplines: Life sciences

Keywords: reverse engineering, identification, inference, dynamic modelling, systems biology

Reverse engineering and identification in systems biology: strategies, perspectives and challenges

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Most cited references 110

A gene-coexpression network for global discovery of conserved genetic modules.

Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood.

Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems.

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