Bayesian learning of effective chemical master equations in crowded intracellular conditions
Abstract
Biochemical reactions inside living cells often occur in the presence of crowders - molecules that do not participate in the reactions but influence the reaction rates through excluded volume effects. However the standard approach to modelling stochastic intracellular reaction kinetics is based on the chemical master equation (CME) whose propensities are derived assuming no crowding effects. Here, we propose a machine learning strategy based on Bayesian Optimisation utilising synthetic data obtained from spatial cellular automata (CA) simulations (that explicitly model volume-exclusion effects) to learn effective propensity functions for CMEs. The predictions from a small CA training data set can then be extended to the whole range of parameter space describing physiologically relevant levels of crowding by means of Gaussian Process regression. We demonstrate the method on an enzyme-catalyzed reaction and a genetic feedback loop, showing good agreement between the time-dependent distributions of molecule numbers predicted by the effective CME and CA simulations.
Citation
Braichenko , S , Grima , R & Sanguinetti , G 2022 , Bayesian learning of effective chemical master equations in crowded intracellular conditions . in I Petre & A Păun (eds) , Computational Methods in Systems Biology : 20th International Conference, CMSB 2022, Bucharest, Romania, September 14–16, 2022, Proceedings . Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics) , vol. 13447 , Springer Science and Business Media , Cham , pp. 239-258 , 20th International Conference on Computational Methods in Systems Biology, CMSB 2022 , Bucharest , Romania , 14/09/22 . https://doi.org/10.1007/978-3-031-15034-0_12 conference
Publication
Computational Methods in Systems Biology
ISSN
0302-9743Type
Conference item
Description
Funding: supported by Leverhulme Trust.Collections
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