ArticleJournal of the Royal Society, Interface2019
Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art.
Article in Journal of the Royal Society, Interface, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
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- Determining interaction directionality in complex biochemical networks from stationary measurements.Scientific reports · 2025Article
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- Guided simulation of conditioned chemical reaction networks.Statistical inference for stochastic processes · 2025Article
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- Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation.Journal of mathematical biology · 2024Article
- Efficient multifidelity likelihood-free Bayesian inference with adaptive computational resource allocation.Journal of computational physics · 2024Article
- The σB alternative sigma factor circuit modulates noise to generate different types of pulsing dynamics.PLoS computational biology · 2023Article
- Regulation strategies for two-output biomolecular networks.Journal of the Royal Society, Interface · 2023Article
- Single-cell CaJournal of the Royal Society, Interface · 2023Article
- Julia for biologists.Nature methods · 2023Review
- Explainability of the COVID-19 epidemiological model with nonnegative tensor factorization.International journal of data science and analytics · 2023Article
- On predictive inference for intractable models via approximate Bayesian computation.Statistics and computing · 2023Article
- Modeling of ionizing radiation induced hair follicle regenerative dynamics.Journal of theoretical biology · 2022Article
- Frequency spectra and the color of cellular noise.Nature communications · 2022Article
- Inference and uncertainty quantification of stochastic gene expression via synthetic models.Journal of the Royal Society, Interface · 2022Article
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3 authors.
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Abstract
Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour and variability of a stochastic biochemical reaction network requires many probabilistic simulations of its evolution. Using a biochemical reaction network model to assist in the interpretation of time-course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities. These computational challenges have been subjects of active research for over four decades. In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models. Detailed algorithms for particularly important methods are described and complemented with Matlab
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