EurekaMag.com logo
+ Site Statistics
References:
53,623,987
Abstracts:
29,492,080
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on LinkedInFollow on LinkedIn

+ Translate

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



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



Molecular Biosystems 7(5): 1593-1602



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.

(PDF same-day service: $19.90)

Accession: 055087833

Download citation: RISBibTeXText

PMID: 21380410

DOI: 10.1039/c0mb00107d



Related references

Parameter synthesis in nonlinear dynamical systems: application to systems biology. Journal of Computational Biology 17(3): 325-336, 2010

Parameter reduction for stable dynamical systems based on Hankel singular values and sensitivity analysis. Chemical Engineering Science 61(16): 5393-5403, 2006

Reverse-engineering of polynomial dynamical systems. Advances in Applied Mathematics 39(4): 477-489, 2007

Automated reverse engineering of nonlinear dynamical systems. Proceedings of the National Academy of Sciences of the United States of America 104(24): 9943-9948, 2007

Probabilistic polynomial dynamical systems for reverse engineering of gene regulatory networks. Eurasip Journal on Bioinformatics & Systems Biology 2011(1): 1-1, 2011

Reverse engineering time discrete finite dynamical systems: a feasible undertaking?. Plos One 4(3): E4939-E4939, 2009

Logic-based models in systems biology: a predictive and parameter-free network analysis method. Integrative Biology 4(11): 1323-1337, 2013

Cardiac systems biology and parameter sensitivity analysis: intracellular Ca2+ regulatory mechanisms in mouse ventricular myocytes. Advances in Biochemical Engineering/Biotechnology 110: 25-45, 2008

Reverse engineering discrete dynamical systems from data sets with random input vectors. Journal of Computational Biology 13(8): 1435-1456, 2006

Systems biology: the role of engineering in the reverse engineering of biological signaling. Cells 2(2): 393-413, 2013

Sensitivity analysis approaches applied to systems biology models. Iet Systems Biology 5(6): 336-336, 2012

Systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering. Cells 2(4): 635-688, 2013

Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions. Bmc Systems Biology 12(1): 1-1, 2018

SensSB: a software toolbox for the development and sensitivity analysis of systems biology models. Bioinformatics 26(13): 1675-1676, 2010

Complex systems biology: exploring universal statistical and dynamical features in cellular processes. Genome Informatics. International Conference on Genome Informatics 15(2): 302-303, 2005