TL;DR
This paper introduces new computational methods leveraging network topology to enable scalable Bayesian inference for large-scale nonlinear reaction network models, improving sampling efficiency and feasibility.
Contribution
The paper presents novel network-aware proposals and sensitivity-based move types for reversible-jump MCMC, facilitating large-scale nonlinear network inference.
Findings
Enhanced sampling efficiency in nonlinear network inference
Successful application to systems biology models
Feasibility of large-scale Bayesian network inference
Abstract
The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters, but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we…
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