- Letter
- Open Access
Violation of local detailed balance upon lumping despite a clear timescale separation
Phys. Rev. Research 5, L032017 – Published 7 August, 2023
DOI: https://doi.org/10.1103/PhysRevResearch.5.L032017
Abstract
Integrating out fast degrees of freedom is known to yield, to a good approximation, memory-less, i.e., Markovian, dynamics. In the presence of such a timescale separation local detailed balance is believed to inherently emerge and to guarantee thermodynamic consistency arbitrarily far from equilibrium. Here we present a transparent example of a Markov model of a molecular motor where lumping leads to a violation of local detailed balance despite a clear timescale separation and hence Markovian dynamics. Driving the system far from equilibrium can lead to a violation of local detailed balance against the driving force. We further show that local detailed balance can be restored, even in the presence of memory, if the coarse-graining is carried out as Milestoning. Our work establishes Milestoning not only as a kinetically but as far as we know for the first time also as a thermodynamically consistent coarse-graining method. Our results are relevant as soon as individual transition paths are appreciable or can be resolved.
Physics Subject Headings (PhySH)
Article Text
Supplemental Material
References (85)
- C. Jarzynski, Equalities and inequalities: Irreversibility and the second law of thermodynamics at the nanoscale, Annu. Rev. Condens. Matter Phys. 2, 329 (2011).
- U. Seifert, Stochastic thermodynamics, fluctuation theorems and molecular machines, Rep. Prog. Phys. 75, 126001 (2012).
- C. Van den Broeck and M. Esposito, Ensemble and trajectory thermodynamics: A brief introduction, Physica A 418, 6 (2015).
- S. Katz, J. L. Lebowitz, and H. Spohn, Phase transitions in stationary nonequilibrium states of model lattice systems, Phys. Rev. B 28, 1655 (1983).
- U. Seifert, Stochastic thermodynamics of single enzymes and molecular motors, Eur. Phys. J. E 34, 26 (2011).
- K. Yoshimura and S. Ito, Information geometric inequalities of chemical thermodynamics, Phys. Rev. Res. 3, 013175 (2021).
- K. Blom and A. Godec, Criticality in cell adhesion, Phys. Rev. X 11, 031067 (2021).
- C. Maes, Local detailed balance, SciPost Phys. Lect. Notes, 32 (2021).
- J. V. Koski, T. Sagawa, O.-P. Saira, Y. Yoon, A. Kutvonen, P. Solinas, M. Möttönen, T. Ala-Nissila, and J. P. Pekola, Distribution of entropy production in a single-electron box, Nat. Phys. 9, 644 (2013).
- P. Strasberg, G. Schaller, T. Brandes, and M. Esposito, Thermodynamics of a Physical Model Implementing a Maxwell Demon, Phys. Rev. Lett. 110, 040601 (2013).
- J. V. Koski, V. F. Maisi, J. P. Pekola, and D. V. Averin, Experimental realization of a Szilard engine with a single electron, Proc. Natl. Acad. Sci. USA 111, 13786 (2014).
- G. Manzano, D. Subero, O. Maillet, R. Fazio, J. P. Pekola, and E. Roldán, Thermodynamics of gambling demons, Phys. Rev. Lett. 126, 080603 (2021).
- S. Rahav and C. Jarzynski, Fluctuation relations and coarse-graining, J. Stat. Mech. (2007) P09012.
- S. Pigolotti and A. Vulpiani, Coarse graining of master equations with fast and slow states, J. Chem. Phys. 128, 154114 (2008).
- A. Gomez-Marin, J. M. R. Parrondo, and C. Van den Broeck, Lower bounds on dissipation upon coarse graining, Phys. Rev. E 78, 011107 (2008).
- É. Roldán and J. M. R. Parrondo, Estimating Dissipation from Single Stationary Trajectories, Phys. Rev. Lett. 105, 150607 (2010).
- A. Puglisi, S. Pigolotti, L. Rondoni, and A. Vupani, Entropy production and coarse graining in Markov processes, J. Stat. Mech. (2010) P05015.
- É. Roldán and J. M. R. Parrondo, Entropy production and Kullback-Leibler divergence between stationary trajectories of discrete systems, Phys. Rev. E 85, 031129 (2012).
- M. Esposito, Stochastic thermodynamics under coarse graining, Phys. Rev. E 85, 041125 (2012).
- D. Andrieux, Bounding the coarse graining error in hidden Markov dynamics, Appl. Math. Lett. 25, 1734 (2012).
- S. Bo and A. Celani, Entropy production in stochastic systems with fast and slow time-scales, J. Stat. Phys. 154, 1325 (2014).
- G. Diana and M. Esposito, Mutual entropy production in bipartite systems, J. Stat. Mech. (2014) P04010.
- A. C. Barato, D. Hartich, and U. Seifert, Efficiency of cellular information processing, New J. Phys. 16, 103024 (2014).
- E. Zimmermann and U. Seifert, Effective rates from thermodynamically consistent coarse-graining of models for molecular motors with probe particles, Phys. Rev. E 91, 022709 (2015).
- S. Bo and A. Celani, Multiple-scale stochastic processes: Decimation, averaging and beyond, Phys. Rep. 670, 1 (2017).
- M. Kahlen and J. Ehrich, Hidden slow degrees of freedom and fluctuation theorems: An analytically solvable model, J. Stat. Mech. (2018) 063204.
- M. Uhl, P. Pietzonka, and U. Seifert, Fluctuations of apparent entropy production in networks with hidden slow degrees of freedom, J. Stat. Mech. (2018) 023203.
- A. Lapolla and A. Godec, Manifestations of projection-induced memory: General theory and the tilted single file, Front. Phys. 7, 182 (2019).
- A. Lapolla and A. Godec, Single-file diffusion in a bi-stable potential: Signatures of memory in the barrier-crossing of a tagged-particle, J. Chem. Phys. 153, 194104 (2020).
- A. Lapolla and A. Godec, Toolbox for quantifying memory in dynamics along reaction coordinates, Phys. Rev. Res. 3, L022018 (2021).
- J. Ehrich, Tightest bound on hidden entropy production from partially observed dynamics, J. Stat. Mech. (2021) 083214.
- B. Ertel, J. van der Meer, and U. Seifert, Operationally accessible uncertainty relations for thermodynamically consistent semi-Markov processes, Phys. Rev. E 105, 044113 (2022).
- J. Mehl, B. Lander, C. Bechinger, V. Blickle, and U. Seifert, Role of Hidden Slow Degrees of Freedom in the Fluctuation Theorem, Phys. Rev. Lett. 108, 220601 (2012).
- B. Altaner and J. Vollmer, Fluctuation-Preserving Coarse Graining for Biochemical Systems, Phys. Rev. Lett. 108, 228101 (2012).
- G. Teza and A. L. Stella, Exact Coarse Graining Preserves Entropy Production Out of Equilibrium, Phys. Rev. Lett. 125, 110601 (2020).
- D. Hartich and A. Godec, Emergent Memory and Kinetic Hysteresis in Strongly Driven Networks, Phys. Rev. X 11, 041047 (2021).
- F. Knoch and T. Speck, Cycle representatives for the coarse-graining of systems driven into a non-equilibrium steady state, New J. Phys. 17, 115004 (2015).
- M. Polettini and M. Esposito, Effective Thermodynamics for a Marginal Observer, Phys. Rev. Lett. 119, 240601 (2017).
- G. Bisker, M. Polettini, T. R. Gingrich, and J. M. Horowitz, Hierarchical bounds on entropy production inferred from partial information, J. Stat. Mech. (2017) 093210.
- I. A. Martínez, G. Bisker, J. M. Horowitz, and J. M. R. Parrondo, Inferring broken detailed balance in the absence of observable currents, Nat. Commun. 10, 3542 (2019).
- D. J. Skinner and J. Dunkel, Improved bounds on entropy production in living systems, Proc. Natl. Acad. Sci. USA 118, e2024300118 (2021).
- D. J. Skinner and J. Dunkel, Estimating Entropy Production from Waiting Time Distributions, Phys. Rev. Lett. 127, 198101 (2021).
- D. Hartich and A. Godec, Thermodynamic Uncertainty Relation Bounds the Extent of Anomalous Diffusion, Phys. Rev. Lett. 127, 080601 (2021).
- M. Sarich, F. Noé, and C. Schütte, On the approximation quality of Markov state models, Multiscale Model. Simul. 8, 1154 (2010).
- C. Schütte, F. Noé, J. Lu, M. Sarich, and E. Vanden-Eijnden, Markov state models based on milestoning, J. Chem. Phys. 134, 204105 (2011).
- H. S. Chung, K. McHale, J. M. Louis, and W. A. Eaton, Single-molecule fluorescence experiments determine protein folding transition path times, Science 335, 981 (2012).
- H. S. Chung and W. A. Eaton, Single-molecule fluorescence probes dynamics of barrier crossing, Nature (London) 502, 685 (2013).
- K. Neupane, D. B. Ritchie, H. Yu, D. A. N. Foster, F. Wang, and M. T. Woodside, Transition Path Times for Nucleic Acid Folding Determined from Energy-Landscape Analysis of Single-Molecule Trajectories, Phys. Rev. Lett. 109, 068102 (2012).
- D. B. Ritchie and M. T. Woodside, Probing the structural dynamics of proteins and nucleic acids with optical tweezers, Curr. Opin. Struct. Biol. 34, 43 (2015).
- K. Neupane, A. P. Manuel, and M. T. Woodside, Protein folding trajectories can be described quantitatively by one-dimensional diffusion over measured energy landscapes, Nat. Phys. 12, 700 (2016).
- J.-Y. Kim and H. S. Chung, Disordered proteins follow diverse transition paths as they fold and bind to a partner, Science 368, 1253 (2020).
- A. K. Faradjian and R. Elber, Computing time scales from reaction coordinates by milestoning, J. Chem. Phys. 120, 10880 (2004).
- D. Shalloway and A. K. Faradjian, Efficient computation of the first passage time distribution of the generalized master equation by steady-state relaxation, J. Chem. Phys. 124, 054112 (2006).
- R. Elber, D. E. Makarov, and H. Orland, Molecular Kinetics in Condensed Phases: Theory, Simulation, and Analysis (John Wiley & Sons, New York, 2020).
- R. Elber, Milestoning: An efficient approach for atomically detailed simulations of kinetics in biophysics, Annu. Rev. Biophys. 49, 69 (2020).
- E. Suárez, R. P. Wiewiora, C. Wehmeyer, F. Noé, J. D. Chodera, and D. M. Zuckerman, What Markov state models can and cannot do: Correlation versus path-based observables in protein-folding models, J. Chem. Theory Comput. 17, 3119 (2021).
- A. M. Berezhkovskii and A. Szabo, Committors, first-passage times, fluxes, Markov states, milestones, and all that, J. Chem. Phys. 150, 054106 (2019).
- D. Nagel, A. Weber, B. Lickert, and G. Stock, Dynamical coring of Markov state models, J. Chem. Phys. 150, 094111 (2019).
- A. Jain and G. Stock, Hierarchical folding free energy landscape of HP35 revealed by most probable path clustering, J. Phys. Chem. B 118, 7750 (2014).
- R. Yasuda, H. Noji, M. Yoshida, K. Kinosita Jr., and H. Itoh, Resolution of distinct rotational substeps by submillisecond kinetic analysis of -ATPase, Nature (London) 410, 898 (2001).
- J. Schnakenberg, Network theory of microscopic and macroscopic behavior of master equation systems, Rev. Mod. Phys. 48, 571 (1976).
- B. Nguyen, D. Hartich, U. Seifert, and P. De Los Rios, Thermodynamic bounds on the ultra- and infra-affinity of Hsp70 for its substrates, Biophys. J. 113, 362 (2017).
- G. G. Yin and Q. Zhang, Continuous-Time Markov Chains and Applications (Springer, New York, 1998).
- See Supplemental Material at http://link.aps.org/supplemental/10.1103/PhysRevResearch.5.L032017 for a detailed discussion of the timescale separation and provides additional examples which cite Refs. [65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76].
- G. J. Moro, Kinetic equations for site populations from the Fokker-Planck equation, J. Chem. Phys. 103, 7514 (1995).
- G. Falasco and M. Esposito, Local detailed balance across scales: From diffusions to jump processes and beyond, Phys. Rev. E 103, 042114 (2021).
- P. Hänggi, P. Talkner, and M. Borkovec, Reaction-rate theory: Fifty years after Kramers, Rev. Mod. Phys. 62, 251 (1990).
- R. D. Astumian, Adiabatic Pumping Mechanism for Ion Motive ATPases, Phys. Rev. Lett. 91, 118102 (2003).
- S. Rahav, J. Horowitz, and C. Jarzynski, Directed Flow in Nonadiabatic Stochastic Pumps, Phys. Rev. Lett. 101, 140602 (2008).
- M. Esposito and J. M. R. Parrondo, Stochastic thermodynamics of hidden pumps, Phys. Rev. E 91, 052114 (2015).
- H. Qian and M. Qian, Pumped Biochemical Reactions, Nonequilibrium Circulation, and Stochastic Resonance, Phys. Rev. Lett. 84, 2271 (2000).
- M. Ehrenberg and C. Blomberg, Thermodynamic constraints on kinetic proofreading in biosynthetic pathways, Biophys. J. 31, 333 (1980).
- R. Rao and L. Peliti, Thermodynamics of accuracy in kinetic proofreading: Dissipation and efficiency trade-offs, J. Stat. Mech. (2015) P06001.
- C. W. Gardiner, Handbook of Stochastic Methods, 3rd ed. (Springer, Berlin, 2004).
- V. Holubec, K. Kroy, and S. Steffenoni, Physically consistent numerical solver for time-dependent Fokker-Planck equations, Phys. Rev. E 99, 032117 (2019).
- A. Lapolla, D. Hartich, and A. Godec, Spectral theory of fluctuations in time-average statistical mechanics of reversible and driven systems, Phys. Rev. Res. 2, 043084 (2020).
- At physiological conditions the ATP concentration is mMol. Thus, clearly corresponds to an unphysical ATP concentration of .
- R. Satija, A. M. Berezhkovskii, and D. E. Makarov, Broad distributions of transition-path times are fingerprints of multidimensionality of the underlying free energy landscapes, Proc. Natl. Acad. Sci. USA 117, 27116 (2020).
- D. E. Makarov, Barrier crossing dynamics from single-molecule measurements, J. Phys. Chem. B 125, 2467 (2021).
- A. M. Berezhkovskii and D. E. Makarov, On distributions of barrier crossing times as observed in single-molecule studies of biomolecules, Biophys. Rep. 1, 100029 (2021).
- A. M. Berezhkovskii, G. Hummer, and S. M. Bezrukov, Identity of Distributions of Direct Uphill and Downhill Translocation Times for Particles Traversing Membrane Channels, Phys. Rev. Lett. 97, 020601 (2006).
- J. Gladrow, M. Ribezzi-Crivellari, F. Ritort, and U. F. Keyser, Experimental evidence of symmetry breaking of transition-path times, Nat. Commun. 10, 55 (2019).
- A. Ryabov, D. Lips, and P. Maass, Counterintuitive short uphill transitions in single-file diffusion, J. Phys. Chem. C 123, 5714 (2019).
- H. Kramers, Brownian motion in a field of force and the diffusion model of chemical reactions, Physica 7, 284 (1940).
- A. T. Hawk and D. E. Makarov, Milestoning with transition memory, J. Chem. Phys. 135, 224109 (2011).