Evidence map›Paper›PMID 35590650›Full record

ArticlePhysical review. E2022

State-space renormalization group theory of nonequilibrium reaction networks: Exact solutions for hypercubic lattices in arbitrary dimensions.

Qiwei Yu, Yuhai Tu

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Article in Physical review. E, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Stochastic thermodynamics for biological functions.Quantitative biology (Beijing, China) · 2025
    Review
  2. Article
  3. A nonequilibrium allosteric model for receptor-kinase complexes: The role of energy dissipation in chemotaxis signaling.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  4. Article
4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Qiwei YuLewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
Yuhai TuIBM T. J. Watson Research Center, Yorktown Heights, New York 10598, USA.

Funding

Molecular Mechanisms and Biochemical Circuits for Adaptation in Biological SystemsR35GM131734 · NIGMS · IBM THOMAS J. WATSON RESEARCH CENTER · PI TU, YUHAI · 2019 to 2024
$1.8M
NIGMS NIH HHS R35 GM131734
6 · The paper itself

Abstract

Nonequilibrium reaction networks (NRNs) underlie most biological functions. Despite their diverse dynamic properties, NRNs share the signature characteristics of persistent probability fluxes and continuous energy dissipation even in the steady state. Dynamics of NRNs can be described at different coarse-grained levels. Our previous work showed that the apparent energy dissipation rate at a coarse-grained level follows an inverse power-law dependence on the scale of coarse-graining. The scaling exponent is determined by the network structure and correlation of stationary probability fluxes. However, it remains unclear whether and how the (renormalized) flux correlation varies with coarse-graining. Following Kadanoff's real space renormalization group (RG) approach for critical phenomena, we address this question by developing a state-space renormalization group theory for NRNs, which leads to an iterative RG equation for the flux correlation function. In square and hypercubic lattices, we solve the RG equation exactly and find two types of fixed point solutions. There is a family of nontrivial fixed points where the correlation exhibits power-law decay, characterized by a power exponent that can take any value within a continuous range. There is also a trivial fixed point where the correlation vanishes beyond the nearest neighbors. The power-law fixed point is stable if and only if the power exponent is less than the lattice dimension n. Consequently, the correlation function converges to the power-law fixed point only when the correlation in the fine-grained network decays slower than r^{-n} and to the trivial fixed point otherwise. If the flux correlation in the fine-grained network contains multiple stable solutions with different exponents, the RG iteration dynamics select the fixed point solution with the smallest exponent. The analytical results are supported by numerical simulations. We also discuss a possible connection between the RG flows of flux correlation with those of the Kosterlitz-Thouless transition.

Identifiers

PMID35590650
PMCPMC9223417

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