Causal network inference using biochemical kinetics

Chris J. Oates, Frank Dondelinger, Nora Bayani, James Korkola, Joe Gray, Sach Mukherjee

Research output: Contribution to journalArticle

25 Citations (Scopus)

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 languageEnglish (US)
JournalBioinformatics
Volume30
Issue number17
DOIs
StatePublished - Sep 1 2014

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Kinetics
Kinetic parameters
Prediction
Graph in graph theory
Unknown
Mitogen-Activated Protein Kinases
Uncertainty
Uncertainty Quantification
Protein Kinase
Statistical Estimation
Formulation
Chemical reactions
Cell Line
Dynamical systems
Discrete Model
Dynamical Behavior
Chemical Reaction
Cells
Linear Model
Proteins

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 journalArticle

Oates, Chris J. ; Dondelinger, Frank ; Bayani, Nora ; Korkola, James ; Gray, Joe ; Mukherjee, Sach. / Causal network inference using biochemical kinetics. In: Bioinformatics. 2014 ; Vol. 30, No. 17.
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