The Library
Causal network inference using biochemical kinetics
Tools
Oates, Chris J., Dondelinger, Frank, Bayani, Nora, Korkola, James, Gray, Joe W. and Mukherjee, Sach (2014) Causal network inference using biochemical kinetics. Bioinformatics, Volume 30 (Number 17). i468-i474. doi:10.1093/bioinformatics/btu452 ISSN 1367-4803.
|
PDF
WRAP_Bioinformatics-2014-Oates-i468-74.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial. Download (881Kb) | Preview |
Official URL: http://dx.doi.org/10.1093/bioinformatics/btu452
Abstract
Motivation: Networks are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of biochemical systems are generally non-linear, suggesting that suitable non-linear formulations may offer gains with respect to causal network inference and aid in associated prediction problems.
Results: We present a general framework for network inference and dynamical prediction using time course data that is rooted in non-linear biochemical kinetics. This is achieved by considering a dynamical system based on a chemical reaction graph with associated kinetic parameters. Both the graph and kinetic parameters are treated as unknown; inference is carried out within a Bayesian framework. This allows prediction of dynamical behavior even when the underlying reaction graph itself is unknown or uncertain. Results, based on (i) data simulated from a mechanistic model of mitogen-activated protein kinase signaling and (ii) phosphoproteomic data from cancer cell lines, demonstrate that non-linear formulations can yield gains in causal network inference and permit dynamical prediction and uncertainty quantification in the challenging setting where the reaction graph is unknown.
Availability and implementation: MATLAB R2014a software is available to download from warwick.ac.uk/chrisoates.
Item Type: | Journal Article | ||||
---|---|---|---|---|---|
Subjects: | Q Science > QH Natural history | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Library of Congress Subject Headings (LCSH): | Biological control systems, Bioinformatics | ||||
Journal or Publication Title: | Bioinformatics | ||||
Publisher: | Oxford University Press | ||||
ISSN: | 1367-4803 | ||||
Official Date: | August 2014 | ||||
Dates: |
|
||||
Volume: | Volume 30 | ||||
Number: | Number 17 | ||||
Page Range: | i468-i474 | ||||
DOI: | 10.1093/bioinformatics/btu452 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Description: | Supplementary data available. |
||||
Date of first compliant deposit: | 28 December 2015 | ||||
Date of first compliant Open Access: | 28 December 2015 | ||||
Funder: | United States. Department of Energy, National Institutes of Health (U.S.) (NIH), National Cancer Institute (U.S.) (NCI), Engineering and Physical Sciences Research Council (EPSRC), Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Netherlands Organisation for Scientific Research] (NWO) | ||||
Grant number: | DE-AC02-05CH11231 (US DoE), U54 CA 112970 (NCI), P50 CA 58207 (NCI), EP/E501311/1 (EPSRC) | ||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year