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Molecular BioSystems Vol - 7 Issue - 4

Molecular BioSystems Vol - 7 Issue - 3

Molecular BioSystems Vol - 7 Issue - 2

Molecular BioSystems Vol -6

Molecular BioSystems Vol -5

Molecular BioSystems Vol -4

Molecular BioSystems Vol -3

Molecular BioSystems Vol -2

Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models

Content Provider	Royal Society of Chemistry (RSC)
Author	Erguler, Kamil Stumpf, Michael P. H.
Copyright Year	2011
Abstract	The size and complexity of cellular systems make building predictive models an extremely difficult task. In principle dynamical time-course data can be used to elucidate the structure of the underlying molecular mechanisms, but a central and recurring problem is that many and very different models can be fitted to experimental data, especially when the latter are limited and subject to noise. Even given a model, estimating its parameters remains challenging in real-world systems. Here we present a comprehensive analysis of 180 systems biology models, which allows us to classify the parameters with respect to their contribution to the overall dynamical behaviour of the different systems. Our results reveal candidate elements of control in biochemical pathways that differentially contribute to dynamics. We introduce sensitivity profiles that concisely characterize parameter sensitivity and demonstrate how this can be connected to variability in data. Systematically linking data and model sloppiness allows us to extract features of dynamical systems that determine how well parameters can be estimated from time-course measurements, and associates the extent of data required for parameter inference with the model structure, and also with the global dynamical state of the system. The comprehensive analysis of so many systems biology models reaffirms the inability to estimate precisely most model or kinetic parameters as a generic feature of dynamical systems, and provides safe guidelines for performing better inferences and model predictions in the context of reverse engineering of mathematical models for biological systems.
Starting Page	1593
Ending Page	1602
Page Count	10
File Format	HTM / HTML PDF
ISSN	1742206X
Volume Number	7
Issue Number	5
Journal	Molecular BioSystems
DOI	10.1039/c0mb00107d
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Systems biology Reverse engineering
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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