Authors : Nawal Daamy Resen; Hanaa K Sacheat; Nahida A. Jinzeel

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/4wfrx3vk

DOI : https://doi.org/10.38124/ijisrt/25may1771

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Physical systems that are not in thermodynamic equilibrium but can be sufficiently described by variables that are an extension of the various elements required to explain the system in thermodynamic equilibrium are the subject of thermodynamics. Living systems, it is an open system that are governed by the rules of nonequilibrium thermodynamics. As a result, understanding biological systems from a non-equilibrium thermodynamic perspective is beneficial. We will quickly review the history and current state of nonequilibrium thermodynamics, particularly in biological systems, in this article. We begin by discussing how people first discovered the value of studying biological systems from a thermodynamic standpoint. The evolution of stochastic thermodynamics is then discussed, with a focus on three key concepts: Jarzynski equality, the Crooks fluctuation theorem, and the thermodynamic uncertain relation. We also provide an overview of the current theoretical model for stochastic thermodynamics in biological reaction networks, with a focus on thermodynamic principles and apparatus at nonequilibrium stable state. Finally, two applications and potential avenues for thermodynamic research in biological systems are examined.

References :

1. Schrödinger, E. What Is Life? The Physical Aspect of the Living Cell; Cambridge University Press: Cambridge, UK, 1944.
2. Prigogine, I. Etude Thermodynamique des Phénomènes Irréversibles; Desoer: Liège, Belgium, 1947.
3. Jaynes, E.T. The minimum entropy production principle. Annu. Rev. Phys. Chem. 1980, 31, 579–601.
4. Nicolis, G.; Prigogine, I. Self-Organization in Nonequilibrium Systems: From Dissipative Structures to Order through Fluctuations; Wiley: New York, NY, USA, 1977.
5. Sekimoto, K. Stochastic Energetics; Springer: Berlin/Heidelberg, Germany, 2010; Volume 799.
6. Jarzynski, C. Equilibrium free-energy differences from nonequilibrium measurements: A master-equation approach. Phys. Rev. E 1997, 56, 5018.
7. Crooks, G.E. Entropy production fluctuation theorem and the nonequilibrium work relation for free energy differences. Phys. Rev. E 1999, 60, 2721.
8. Horowitz, J.M.; Gingrich, T.R. Thermodynamic uncertainty relations constrain non-equilibrium fluctuations. Nat. Phys. 2020, 16, 15–20.
9. Mossa, A.; de Lorenzo, S.; Huguet, J.M.; Ritort, F. Measurement of work in single-molecule pulling experiments. J. Chem. Phys. 2009, 130, 234116.
10. Crooks, G.E. Entropy production fluctuation theorem and the nonequilibrium work relation for free energy differences. Phys. Rev. E 1999, 60, 2721. [Google Scholar] [CrossRef].
11. Collin, D.; Ritort, F.; Jarzynski, C.; Smith, S.B.; Tinoco, I.; Bustamante, C. Verification of the Crooks fluctuation theorem and recovery of RNA folding free energies. Nature 2005, 437, 231–234. [Google Scholar] [CrossRef] [PubMed].
12. Schuler, S.; Speck, T.; Tietz, C.; Wrachtrup, J.; Seifert, U. Experimental test of the fluctuation theorem for a driven two-level system with time-dependent rates. Phys. Rev. Lett. 2005, 94, 180602. [Google Scholar] [CrossRef].
13. Wang, G.; Reid, J.; Carberry, D.; Williams, D.; Sevick, E.M.; Evans, D.J. Experimental study of the fluctuation theorem in a nonequilibrium steady state. Phys. Rev. E 2005, 71, 046142. [Google Scholar] [CrossRef].
14. Seifert, U. Entropy production along a stochastic trajectory and an integral fluctuation theorem. Phys. Rev. Lett. 2005, 95, 040602. [Google Scholar] [CrossRef].
15. Seifert, U. Stochastic thermodynamics, fluctuation theorems and molecular machines. Rep. Prog. Phys. 2012, 75, 126001. [Google Scholar] [CrossRef].
16. Seifert, U. Stochastic thermodynamics: Principles and perspectives. Eur. Phys. J. B 2008, 64, 423–431. [Google Scholar] [CrossRef.
17. Qian, H. Phosphorylation energy hypothesis: Open chemical systems and their biological functions. Annu. Rev. Phys. Chem. 2007, 58, 113–142. [Google Scholar] [CrossRef].
18. Gong, Z.; Quan, H.T. Jarzynski equality, Crooks fluctuation theorem, and the fluctuation theorems of heat for arbitrary initial states. Phys. Rev. E 2015, 92, 012131. [Google Scholar] [CrossRef] [PubMed].
19. Barato, A.C.; Seifert, U. Thermodynamic uncertainty relation for biomolecular processes. Phys. Rev. Lett. 2015, 114, 158101. [Google Scholar] [CrossRef].
20. Polettini, M.; Lazarescu, A.; Esposito, M. Tightening the uncertainty principle for stochastic currents. Phys. Rev. E 2016, 94, 052104. [Google Scholar] [CrossRef].
21. Pietzonka, P.; Ritort, F.; Seifert, U. Finite-time generalization of the thermodynamic uncertainty relation. Phys. Rev. E 2017, 96, 012101. [Google Scholar] [CrossRef].
22. Horowitz, J.M.; Gingrich, T.R. Thermodynamic uncertainty relations constrain non-equilibrium fluctuations. Nat. Phys. 2020, 16, 15–20. [Google Scholar] [CrossRef].
23. Jülicher, F.; Ajdari, A.; Prost, J. Modeling molecular motors. Rev. Mod. Phys. 1997, 69, 1269. [Google Scholar] [CrossRef].
24. Mandadapu, K.K.; Nirody, J.A.; Berry, R.M.; Oster, G. Mechanics of torque generation in the bacterial flagellar motor. Proc. Natl. Acad. Sci. USA 2015, 112, E4381–E4389. [Google Scholar] [CrossRef].
25. Tu, Y.; Cao, Y. Design principles and optimal performance for molecular motors under realistic constraints. Phys. Rev. E 2018, 97, 022403. [Google Scholar] [CrossRef].
26. Mast, C.B.; Schink, S.; Gerland, U.; Braun, D. Escalation of polymerization in a thermal gradient. Proc. Natl. Acad. Sci. USA 2013, 110, 8030–8035. [Google Scholar] [CrossRef] [PubMed].
27. Banerjee, K.; Kolomeisky, A.B.; Igoshin, O.A. Elucidating interplay of speed and accuracy in biological error correction. Proc. Natl. Acad. Sci. USA 2017, 114, 5183–5188. [Google Scholar] [CrossRef].
28. Chiuchiú, D.; Tu, Y.; Pigolotti, S. Error-speed correlations in biopolymer synthesis. Phys. Rev. Lett. 2019, 123, 038101. [Google Scholar] [CrossRef] [PubMed].
29. Lan, G.; Sartori, P.; Neumann, S.; Sourjik, V.; Tu, Y. The energy–speed–accuracy trade-off in sensory adaptation. Nat. Phys. 2012, 8, 422–428. [Google Scholar] [CrossRef].
30. Sagawa, T.; Ueda, M. Second law of thermodynamics with discrete quantum feedback control. Phys. Rev. Lett. 2008, 100, 080403. [Google Scholar] [CrossRef].
31. Sagawa, T.; Ueda, M. Generalized Jarzynski equality under nonequilibrium feedback control. Phys. Rev. Lett. 2010, 104, 090602. [Google Scholar] [CrossRef].
32. Deffner, S.; Jarzynski, C. Information processing and the second law of thermodynamics: An inclusive, Hamiltonian approach. Phys. Rev. X 2013, 3, 041003. [Google Scholar] [CrossRef].
33. Hopfield J.J. Kinetic proofreading: A new mechanism for reducing errors in biosynthetic processes requiring high specificity. Proc. Natl. Acad. Sci. USA. 1974;71:4135–4139. doi: 10.1073/pnas.71.10.4135. [PMC free article] [PubMed] [CrossRef] [Google Scholar].
34. Qian H. Reducing intrinsic biochemical noise in cells and its thermodynamic limit. J. Mol. Biol. 2006;362:387–392. doi: 10.1016/j.jmb.2006.07.068. [PubMed] [CrossRef] [Google Scholar]
35. Lan G., Sartori P., Neumann S., Sourjik V., Tu Y. The energy–speed–accuracy trade-off in sensory adaptation. Nat. Phys. 2012;8:422–428. doi: 10.1038/nphys2276. [PMC free article] [PubMed] [CrossRef] [Google Scholar]