Exploiting network topology for large-scale inference of nonlinear reaction models
Name
1705.04678.pdf
Description
Submitted version
Size
2.72 MB
Format
Adobe PDF
Checksum (MD5)
47e5d603006f7f9285d631835dde4e0c
Author(s)
Galagali, Nikhil
Marzouk, Youssef M
Date Issued
February 2019
Journal
Journal of the Royal Society interface
Publisher
The Royal Society
Citation
Galagali, Nikhil and Youssef M. Marzouk. “Exploiting network topology for large-scale inference of nonlinear reaction models.” Journal of the Royal Society interface, vol. 16, no. 152, 2019, 20182766 © 2019 The Author(s)
Version
Original manuscript
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 exploit the topology of networks describing potential interactions among chemical species to design improved 'between-model' proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1098/RSIF.2018.0766
