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Generalized fluctuation-dissipation theorem for non-Markovian reaction networks.
Aimin Chen1, Huahai Qiu2, Tianhai Tian3
1School of Mathematics and Statistics, Henan University, Kaifeng 475004, China.
Physical Review. E
|July 20, 2022
Summary
Researchers developed a generalized fluctuation-dissipation theorem (gFDT) for non-Markovian biochemical networks. This new tool efficiently analyzes molecular fluctuations and noise sources in complex biological systems.
Area of Science:
- Biochemistry
- Systems Biology
- Chemical Kinetics
Background:
- Intracellular biochemical networks exhibit significant fluctuations in molecule numbers and reactive species concentrations.
- The fluctuation-dissipation theorem (FDT) is established for Markovian networks, aiding in fluctuation analysis.
- A similar theorem for non-Markovian networks has been lacking.
Purpose of the Study:
- To establish a generalized fluctuation-dissipation theorem (gFDT) for non-Markovian reaction networks.
- To provide a method for analyzing intrinsic and extrinsic noise effects in biochemical systems.
- To enable efficient evaluation of fluctuations in non-Markovian systems.
Main Methods:
- Developed a generalized chemical master equation (gCME) for non-Markovian networks.
- Incorporated general intrinsic-event waiting-time distributions for intrinsic noise.
- Included general stochastic reaction delays for extrinsic noise.
- Derived the linear noise approximation of the stationary gCME.
Main Results:
- Presented a novel gFDT applicable to a broad range of non-Markovian reaction networks.
- The gFDT effectively accounts for intrinsic noise via waiting-time distributions and extrinsic noise via reaction delays.
- The method allows for rapid identification of noise sources within these complex networks.
Conclusions:
- The gFDT offers a powerful new tool for understanding noise in non-Markovian biochemical systems.
- This framework extends fluctuation analysis beyond traditional Markovian assumptions.
- The effectiveness of the gFDT is validated through example analyses.
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