title: Causal network inference using biochemical kinetics.
abstract: BACKGROUND
  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.
  
  
  METHODS
  MATLAB R2014a software is available to download from warwick.ac.uk/chrisoates.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics, University of Warwick, Coventry, CV4 7AL, MRC Biostatistics Unit, Cambridge, CB2 0SR, UK, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239-3098, USA and School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SP, UK.
authors.forename: Chris J
authors.initials: CJ
authors.lastname: Oates
authors.affiliation: Department of Statistics, University of Warwick, Coventry, CV4 7AL, MRC Biostatistics Unit, Cambridge, CB2 0SR, UK, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239-3098, USA and School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SP, UK.
authors.forename: Frank
authors.initials: F
authors.lastname: Dondelinger
authors.affiliation: Department of Statistics, University of Warwick, Coventry, CV4 7AL, MRC Biostatistics Unit, Cambridge, CB2 0SR, UK, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239-3098, USA and School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SP, UK.
authors.forename: Nora
authors.initials: N
authors.lastname: Bayani
authors.affiliation: Department of Statistics, University of Warwick, Coventry, CV4 7AL, MRC Biostatistics Unit, Cambridge, CB2 0SR, UK, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239-3098, USA and School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SP, UK.
authors.forename: James
authors.initials: J
authors.lastname: Korkola
authors.affiliation: Department of Statistics, University of Warwick, Coventry, CV4 7AL, MRC Biostatistics Unit, Cambridge, CB2 0SR, UK, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239-3098, USA and School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SP, UK.
authors.forename: Joe W
authors.initials: JW
authors.lastname: Gray
authors.affiliation: Department of Statistics, University of Warwick, Coventry, CV4 7AL, MRC Biostatistics Unit, Cambridge, CB2 0SR, UK, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239-3098, USA and School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SP, UK Department of Statistics, University of Warwick, Coventry, CV4 7AL, MRC Biostatistics Unit, Cambridge, CB2 0SR, UK, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239-3098, USA and School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SP, UK.
authors.forename: Sach
authors.initials: S
authors.lastname: Mukherjee
mesh_terms: D001499:Bayes Theorem; D045744:Cell Line, Tumor; D006801:Humans; D007700:Kinetics; D020935:MAP Kinase Signaling System; D008956:Models, Chemical; D015398:Signal Transduction