Thermodynamically consistent coarse graining of biocatalysts beyond Michaelis–Menten

Wachtel, Artur; Rao, Riccardo; Esposito, Massimiliano

doi:10.1088/1367-2630/aab5c9

Cited by 58 publications

(66 citation statements)

References 50 publications

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“…Many of these are based on time-scale separation: fast degrees of freedom reach a local stationary state over timescales much shorter than the slow dynamics and can be adiabatically eliminated. The resulting transition rates of the slow dynamics then satisfy a local detailed balance condition which carries the information about the thermodynamic potentials (energetic and/or entropic) [70][71][72] or the driving forces [73][74][75] resulting from the fast dynamics. Some coarse-grainings do not require time-scale separation and the hidden degrees of freedom have been shown to behave as work sources (pure energy no entropy) on the remaining degrees of freedom, see e.g.…”

Section: Examplementioning

confidence: 99%

Stochastic thermodynamics of all-to-all interacting many-body systems

et al. 2020

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We provide a stochastic thermodynamic description across scales for N identical units with allto-all interactions that are driven away from equilibrium by different reservoirs and external forces. We start at the microscopic level with Poisson rates describing transitions between many-body states. We then identify an exact coarse graining leading to a mesoscopic description in terms of Poisson transitions between systems occupations. We also study macroscopic fluctuations using the Martin-Siggia-Rose formalism and large deviation theory. In the macroscopic limit (N → ∞), we derive an exact nonlinear (mean-field) rate equation describing the deterministic dynamics of the most likely occupations. Thermodynamic consistency, in particular the detailed fluctuation theorem, is demonstrated across microscopic, mesoscopic and macroscopic scales. The emergent notion of entropy at different scales is also outlined. Macroscopic fluctuations are calculated semianalytically in an out-of-equilibrium Ising model. Our work provides a powerful framework to study thermodynamics of nonequilibrium phase transitions.

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Section: Examplementioning

confidence: 99%

Stochastic thermodynamics of all-to-all interacting many-body systems

et al. 2020

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“…Still, it is worth noticing that whenever these chemical schemes are used as effective models that coarse-grain some nonequilibrium reactions, the dissipation Σ is always smaller than the total entropy production rate of the original process [67]. Instead, if only equilibrated subprocesses are lumped or discarded, a complete thermodynamic description of the original process exists [68]. It identifies  with the chemical potential difference (in units of R T=1) of the fixed species (respectively, P and S, F and W) and Σ with the entropy production rate [26].…”

Section: Discussionmentioning

confidence: 99%

Negative differential response in chemical reactions

et al. 2019

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Reaction currents in chemical networks usually increase when increasing their driving affinities. But far from equilibrium the opposite can also happen. We find that such negative differential response (NDR) occurs in reaction schemes of major biological relevance, namely, substrate inhibition and autocatalysis. We do so by deriving the full counting statistics of two minimal representative models using large deviation methods. We argue that NDR implies the existence of optimal affinities that maximize the robustness against environmental and intrinsic noise at intermediate values of dissipation. An analogous behavior is found in dissipative self-assembly, for which we identify the optimal working conditions set by NDR.[3]. Close to equilibrium, such response is severely constrained [4].Since currents are proportional to affinities,  á ñ = J R , the response R must be positive to ensure positivity of the entropy production 1    S = á ñ = J R 0 2 . Far from equilibrium, instead, á ñ J need not be linear in  thus making R not only dependent on the entropy production. Kinetic aspects become relevant [5], thus opening the way to regimes of negative differential response (NDR) [6]. This counterintuitive, yet common phenomenon has been found in a wealth of physical systems after its first discovery in low-temperature semiconductors [7]. Examples are particles in crowded and glassy environments [8][9][10][11][12], tracers in external flows [13,14], hopping processes in disordered media [15,16], molecular motors [17,18], polymer electrophoresis in gels [19], quantum spin chains [20], graphene and thermal transistors [21,22]. The shared feature underlying all these systems is a trapping mechanism arising by (e.g. energetic, geometric, topological) constraints on the system states [23].Here, we show that NDR plays a key role in open chemical reactions networks [24][25][26]. We show for three paradigmatic models-substrate inhibition, autocatalysis and dissipative self-assembly-how it appears in the average macroscopic behavior as well as in the stochastic regime. While the first two are well described core reaction schemes in living organisms [27,28], the latter is currently drawing the attention of chemists [29,30]. Within the scope of these examples we discuss the role of NDR with respect to environmental and intrinsic noise [31][32][33][34]. We first show that the region of marginal stability, i.e. where R;0, ensures robustness against external perturbations (in the affinity) at moderate values of dissipation. We then argue that those systems affected by NDR that are not poised in the region of marginal stability, behave so in order to minimize the dispersion of the current. Such precision is found to be achieved at moderate values of dissipation, yet again. Hence, our findings show that the performance of life-supporting processes does not always increase at larger dissipation rates OPEN ACCESS RECEIVED

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“…At the same time, ongoing work is uncovering how thermodynamics constrains flux patterns at steady state starting from the kinetics of individual enzymes, with results which suggest that the problem of computing thermodynamic potentials described here might have to be modified as more is known about individual reaction mechanisms, at least to some degree. 30 In this respect, it is the convergence of novel statistical physics, [31][32][33][34] biochemical and algorithmic ideas that will likely provide the tools to effectively tackle this challenge.…”

Section: Discussionmentioning

confidence: 99%

The Essential Role of Thermodynamics in Metabolic Network Modeling: Physical Insights and Computational Challenges

Martino¹,

Martino²,

Marinari³

2019

Chemical Kinetics

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Quantitative studies of cell metabolism are often based on large chemical reaction network models. A steady state approach is suited to analyze phenomena on the timescale of cell growth and circumvents the problem of incomplete experimental knowledge on kinetic laws and parameters, but it shall be supported by a correct implementation of thermodynamic constraints. In this article we review the latter aspect highlighting its computational challenges and physical insights. The simple introduction of Gibbs inequalities avoids the presence of unfeasible loops allowing for correct timescale analysis but leads to possibly non-convex feasible flux spaces, whose exploration needs efficient algorithms. We shorty review on the implementation of thermodynamics through variational principles in constraints based models of metabolic networks.

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Thermodynamically consistent coarse graining of biocatalysts beyond Michaelis–Menten

Cited by 58 publications

References 50 publications

Stochastic thermodynamics of all-to-all interacting many-body systems

Stochastic thermodynamics of all-to-all interacting many-body systems

Negative differential response in chemical reactions

The Essential Role of Thermodynamics in Metabolic Network Modeling: Physical Insights and Computational Challenges

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