Causal network inference using biochemical kinetics

By Chris J. Oates, Frank Dondelinger, Nora Bayani, James Korkola, Joe W. Gray, Sach Mukherjee. 2014

60 citations recorded by the source corpus.

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. Contact: c.oates@warwick.ac.uk or sach@mrc-bsu.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Causal Models for Dynamical Systems (2020, methodology): “Some methods [Oates et al. 2014] explicitly consider the causal nature of this problem.”
Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics (2016, methodology): “functional forms including: sparse Bayesian inference on a library of Hill functions [42], [43], Bayesian inference on the chemical reaction graph and kinetic parameters using a library of Hill functions [44], [45], an alternating regression using the compact S-system representation [2], [46], and a”
Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods (2014, methodology): “…of statistical efficiency it would be interesting to extend the reducedvariance methodology to the non-parametric setting recently considered by Oates et al. (2014) that provides a mechanism to learn a suitable trial function P (θ) that need not be polynomial, leading (in some cases) to…”
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees (2015, methodology): “Correctness of this result was confirmed using the more computationally intensive methods in [19].”