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Maximizing the Information Content of Experiments in Systems Biology

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Title: Maximizing the Information Content of Experiments in Systems Biology
Authors: Liepe, J
Filippi, S
Komorowski, M
Stumpf, MPH
Item Type: Journal Article
Abstract: Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior.
Issue Date: 31-Jan-2013
Date of Acceptance: 30-Nov-2012
URI: http://hdl.handle.net/10044/1/40297
DOI: http://dx.doi.org/10.1371/journal.pcbi.1002888
ISSN: 1553-734X
Publisher: Public Library of Science
Journal / Book Title: PLOS Computational Biology
Volume: 9
Issue: 1
Copyright Statement: © 2013 Liepe et al. 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 author and source are credited.
Sponsor/Funder: Biotechnology and Biological Sciences Research Council (BBSRC)
Biotechnology and Biological Sciences Research Council (BBSRC)
Biotechnology and Biological Sciences Research Council (BBSRC)
Biotechnology and Biological Sciences Research Cou
Medical Research Council (MRC)
Funder's Grant Number: BB/G001863/1
BB/G020434/1
BB/G007934/1
BB/G530268/1
G1002092
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
BIOCHEMICAL RESEARCH METHODS
MATHEMATICAL & COMPUTATIONAL BIOLOGY
APPROXIMATE BAYESIAN COMPUTATION
EXPERIMENTAL-DESIGN
PARAMETER-ESTIMATION
MODEL SELECTION
DYNAMICAL-SYSTEMS
UNCERTAINTY
NETWORKS
CELL
INFERENCE
ENTROPY
Bayes Theorem
Models, Theoretical
Systems Biology
Uncertainty
Bioinformatics
06 Biological Sciences
08 Information And Computing Sciences
01 Mathematical Sciences
Publication Status: Published
Article Number: e1002888
Appears in Collections:Faculty of Natural Sciences