Document Detail


Network inference using steady-state data and Goldbeter-Koshland kinetics. [corrected].
MedLine Citation:
PMID:  22815361     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
MOTIVATION: Network inference approaches are widely used to shed light on regulatory interplay between molecular players such as genes and proteins. Biochemical processes underlying networks of interest (e.g. gene regulatory or protein signalling networks) are generally nonlinear. In many settings, knowledge is available concerning relevant chemical kinetics. However, existing network inference methods for continuous, steady-state data are typically rooted in statistical formulations, which do not exploit chemical kinetics to guide inference.
RESULTS: Herein, we present an approach to network inference for steady-state data that is rooted in non-linear descriptions of biochemical mechanism. We use equilibrium analysis of chemical kinetics to obtain functional forms that are in turn used to infer networks using steady-state data. The approach we propose is directly applicable to conventional steady-state gene expression or proteomic data and does not require knowledge of either network topology or any kinetic parameters. We illustrate the approach in the context of protein phosphorylation networks, using data simulated from a recent mechanistic model and proteomic data from cancer cell lines. In the former, the true network is known and used for assessment, whereas in the latter, results are compared against known biochemistry. We find that the proposed methodology is more effective at estimating network topology than methods based on linear models.
AVAILABILITY: mukherjeelab.nki.nl/CODE/GK_Kinetics.zip
CONTACT: c.j.oates@warwick.ac.uk; s.mukherjee@nki.nl
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors:
Chris J Oates; Bryan T Hennessy; Yiling Lu; Gordon B Mills; Sach Mukherjee
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2012-07-19
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  28     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2012 Sep 
Date Detail:
Created Date:  2012-09-10     Completed Date:  2013-05-15     Revised Date:  2013-09-24    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  2342-8     Citation Subset:  IM    
Affiliation:
Centre for Complexity Science, University of Warwick, CV4 7AL, Coventry, UK. c.j.oates@warwick.ac.uk
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MeSH Terms
Descriptor/Qualifier:
Breast Neoplasms / enzymology
Cell Line, Tumor
Female
Gene Regulatory Networks
Humans
Kinetics
MAP Kinase Signaling System
Markov Chains
Monte Carlo Method
Phosphorylation
Proteomics*
Systems Biology / methods*
Grant Support
ID/Acronym/Agency:
CA016672/CA/NCI NIH HHS; P30 CA016672/CA/NCI NIH HHS; U54 CA112970/CA/NCI NIH HHS
Comments/Corrections
Erratum In:
Bioinformatics. 2013 Mar 15;29(6):819

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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