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PreprintCC BY 4.0arXiv

Mutual linearity from classical response theory

  • Timur Aslyamov
  • Massimiliano Esposito
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Abstract

We unify known mutual-linearity relations for Markov jump processes and chemical reaction networks within a common framework based on classical finite-response theory. In a Markov jump process, an exact finite-response identity shows that all stationary probability changes share a common factor when the rates of one edge change. Eliminating this factor gives the relations between probabilities, observables, and currents, even far from equilibrium. We also explain how the same argument applies when one coefficient of a linear system changes and recovers the known relations in Laplace space and between stationary complex activities in chemical reaction networks.

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Cite

BibTeXRIS
BibTeX
@misc{aslyamov2026mutual,
  title = {{Mutual linearity from classical response theory}},
  author = {Timur Aslyamov and Massimiliano Esposito},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.16894},
  eprint = {2610.16894},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.16894},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Mutual linearity from classical response theory
AU  - Timur Aslyamov
AU  - Massimiliano Esposito
PY  - 2026
DA  - 2026/10/12
PB  - arXiv
DO  - 10.48550/arxiv.2610.16894
UR  - https://arxiv.org/abs/2610.16894
AB  - We unify known mutual-linearity relations for Markov jump processes and chemical reaction networks within a common framework based on classical finite-response theory. In a Markov jump process, an exact finite-response identity shows that all stationary probability changes share a common factor when the rates of one edge change. Eliminating this factor gives the relations between probabilities, observables, and currents, even far from equilibrium. We also explain how the same argument applies when one coefficient of a linear system changes and recovers the known relations in Laplace space and between stationary complex activities in chemical reaction networks.
ER  -