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 nonlinear 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.
| Original language | English (US) |
|---|---|
| Journal | Bioinformatics |
| Volume | 30 |
| Issue number | 17 |
| DOIs | |
| State | Published - Sep 1 2014 |
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ASJC Scopus subject areas
- Biochemistry
- Molecular Biology
- Computational Theory and Mathematics
- Computer Science Applications
- Computational Mathematics
- Statistics and Probability
Cite this
Causal network inference using biochemical kinetics. / Oates, Chris J.; Dondelinger, Frank; Bayani, Nora; Korkola, James; Gray, Joe; Mukherjee, Sach.
In: Bioinformatics, Vol. 30, No. 17, 01.09.2014.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Causal network inference using biochemical kinetics
AU - Oates, Chris J.
AU - Dondelinger, Frank
AU - Bayani, Nora
AU - Korkola, James
AU - Gray, Joe
AU - Mukherjee, Sach
PY - 2014/9/1
Y1 - 2014/9/1
N2 - 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 nonlinear 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.
AB - 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 nonlinear 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.
UR - http://www.scopus.com/inward/record.url?scp=84907019497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907019497&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btu452
DO - 10.1093/bioinformatics/btu452
M3 - Article
C2 - 25161235
AN - SCOPUS:84907019497
VL - 30
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 17
ER -