4.7 Article

Exploring dissipative sources of non-Markovian biochemical reaction systems

Journal

PHYSICAL REVIEW E
Volume 103, Issue 5, Pages -

Publisher

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevE.103.052411

Keywords

-

Funding

  1. National Nature Science Foundation of People's Republic of China [11601094, 11475273, 11631005, 11775314, 11931019, 12001377]
  2. Guangdong Key Research and Development Project [2019B0233002]
  3. Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University [2020B1212060032]

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Biological processes are controlled by biochemical reaction networks operating far from thermodynamic equilibrium, dissipated free energy. Single reaction events occur in a memory manner, leading to non-Markovian kinetics. By calculating the energy consumption of a general reaction system with molecular memory, it is revealed that energy dissipation is influenced by broken detailed balance and direction-time dependence of waiting-time distributions.
Many biological processes including important intracellular processes are governed by biochemical reaction networks. Usually, these reaction systems operate far from thermodynamic equilibrium, implying free-energy dissipation. On the other hand, single reaction events happen often in a memory manner, leading to non-Markovian kinetics. A question then arises: how do we calculate free-energy dissipation (defined as the entropy production rate) in this physically real case? We derive an analytical formula for calculating the energy consumption of a general reaction system with molecular memory characterized by nonexponential waiting-time distributions. It shows that this dissipation is composed of two parts: one from broken detailed balance of an equivalent Markovian system with the same topology and substrates, and the other from the direction-time dependence of waiting-time distributions. But, if the system is in a detailed balance and the waiting-time distribution is direction-time independent, there is no energy dissipation even in the non-Markovian case. These general results provide insights into the physical mechanisms underlying nonequilibrium processes. A continuous-time random-walk model and a generalized model of stochastic gene expression are chosen to clearly show dissipative sources and the relationship between energy dissipation and molecular memory.

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