Markovian approaches to modeling intracellular reaction processes with molecular memory

Jiajun Zhang Sun Yat-sen University Tianshou Zhou Sun Yat-sen University

Data Analysis, Bio-Statistics, Bio-Mathematics mathscidoc:2004.42003

Distinguished Paper Award in 2020

PNAS, 116, 23542-23550, 2019.11
Many cellular processes are governed by stochastic reaction events. These events do not necessarily occur in single steps of individual molecules, and, conversely, each birth or death of a macromolecule (e.g., protein) could involve several small reaction steps, creating a memory between individual events and thus leading to nonmarkovian reaction kinetics. Characterizing this kinetics is challenging. Here, we develop a systematic approach for a general reaction network with arbitrary intrinsic waiting-time distributions, which includes the stationary generalized chemical-master equation (sgCME), the stationary generalized Fokker–Planck equation, and the generalized linear-noise approximation. The first formulation converts a nonmarkovian issue into a markovian one by introducing effective transition rates (that explicitly decode the effect of molecular memory) for the reactions in an equivalent reaction network with the same substrates but without molecular memory. Nonmarkovian features of the reaction kinetics can be revealed by solving the sgCME. The latter 2 formulations can be used in the fast evaluation of fluctuations. These formulations can have broad applications, and, in particular, they may help us discover new biological knowledge underlying memory effects. When they are applied to generalized stochastic models of gene-expression regulation, we find that molecular memory is in effect equivalent to a feedback and can induce bimodality, fine-tune the expression noise, and induce switch.
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  title={Markovian approaches to modeling intracellular reaction processes with molecular memory},
  author={Jiajun Zhang, and Tianshou Zhou},
Jiajun Zhang, and Tianshou Zhou. Markovian approaches to modeling intracellular reaction processes with molecular memory. 2019. Vol. 116. In PNAS. pp.23542-23550.
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