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Review
. 2013 Dec 4;11(91):20130505.
doi: 10.1098/rsif.2013.0505. Print 2014 Feb 6.

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

Alejandro F Villaverde  1 , Julio R Banga
Affiliations
Review

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

Alejandro F Villaverde et al. J R Soc Interface. .

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?

Keywords: dynamic modelling; identification; inference; reverse engineering; systems biology.

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Figures

Figure 1.
Figure 1.
Approaches for inferring interaction networks. Schematic of the process of inferring a network structure from data, showing three approaches for measuring dependence among variables: correlation-based, information theoretic and Bayesian.
Figure 2.
Figure 2.
Perspectives on reverse engineering. An overview of the different perspectives that converge in the area of systems biology, showing some of their key concepts and tools. (Online version in colour.)
See this image and copyright information in PMC

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