Exploiting network topology for large-scale inference of nonlinear reaction models
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....
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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The Royal Society
2020
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| Online Access: | https://hdl.handle.net/1721.1/126467 |
| _version_ | 1826207068015558656 |
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| author | Galagali, Nikhil Marzouk, Youssef M |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Galagali, Nikhil Marzouk, Youssef M |
| author_sort | Galagali, Nikhil |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:42:58Z |
| format | Article |
| id | mit-1721.1/126467 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:42:58Z |
| publishDate | 2020 |
| publisher | The Royal Society |
| record_format | dspace |
| spelling | mit-1721.1/1264672022-10-01T16:45:30Z Exploiting network topology for large-scale inference of nonlinear reaction models Galagali, Nikhil Marzouk, Youssef M Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Department of Mechanical Engineering 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. 2020-08-03T13:07:27Z 2020-08-03T13:07:27Z 2019-02 2019-10-29T18:46:58Z Article http://purl.org/eprint/type/JournalArticle 1742-5689 1742-5662 https://hdl.handle.net/1721.1/126467 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) en 10.1098/RSIF.2018.0766 Journal of the Royal Society interface Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf The Royal Society arXiv |
| spellingShingle | Galagali, Nikhil Marzouk, Youssef M Exploiting network topology for large-scale inference of nonlinear reaction models |
| title | Exploiting network topology for large-scale inference of nonlinear reaction models |
| title_full | Exploiting network topology for large-scale inference of nonlinear reaction models |
| title_fullStr | Exploiting network topology for large-scale inference of nonlinear reaction models |
| title_full_unstemmed | Exploiting network topology for large-scale inference of nonlinear reaction models |
| title_short | Exploiting network topology for large-scale inference of nonlinear reaction models |
| title_sort | exploiting network topology for large scale inference of nonlinear reaction models |
| url | https://hdl.handle.net/1721.1/126467 |
| work_keys_str_mv | AT galagalinikhil exploitingnetworktopologyforlargescaleinferenceofnonlinearreactionmodels AT marzoukyoussefm exploitingnetworktopologyforlargescaleinferenceofnonlinearreactionmodels |