Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art.

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 ti

SUBMITTER: Warne DJ 

PROVIDER: S-EPMC6408336 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature