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Hong Qian

Publications and source records attributed to Hong Qian.

At least 127 records · Page 7Linked to original sources

Thermodynamics and Geometry of Reversible and Irreversible Markov Processes

Master equation with microscopic reversibility ($q_{ij}\neq 0$ iff $q_{ji}\neq 0$) has a {\em thermodynamic superstructure} in terms of two state functions $S$, entropy, and $F$, free energy: It is discovered recently that entropy production rate $e_p=-dF/dt+Q_{hk}$ with both $-dF/dt=f_d, Q_{hk} \ge 0$. The free energy dissipation $f_d\ge 0$ reflects irreversibility in spontaneous self-organization; house-keeping heat $Q_{hk}\ge 0$ reveals broken time-symmetry in open system driven away from equilibrium. In a Riemannian geometric space, the master equation is a geodesic flow when $Q_{hk}=0$; here we show that the $e_p$ decomposition is orthogonal: $e_p$, $f_d$, $Q_{hk}$ forms a pythagorean triples. Gradient flow means {\em maximum dissipation principle} outside Onsager's regime. The presence of $Q_{hk}$ makses gradient flow no longer generally true. Thermodynamics of stochastic physics requires a new geometric perspective.

cond-mat.stat-mech↗

Maximum Entropy Principle, Equal Probability a Priori and Gibbs Paradox

We prove that information-theoretic maximum entropy (MaxEnt) approach to canonical ensemble is mathematically equivalent to the classic approach of Boltzmann, Gibbs and Darwin-Fowler. The two approaches, however, "interpret" a same mathematical theorem differently; most notably observing mean-energy in the former and energy conservation in the latter. However, applying the same MaxEnt method to grand canonical ensemble fails; while carefully following the classic approach based on Boltzmann's microcanonical {\em equal probability a priori} produces the correct statistics: One does not need to invoke quantum mechanics; and there is no Gibbs paradox. MaxEnt and related minimum relative entropy principle are based on the mathematical theorem concerning large deviations of rare fluctuations. As a scientific method, it requires classic mechanics, or some other assumptions, to provide meaningful {\em prior distributions} for the expected-value based statistical inference. A naive assumption of uniform prior is not valid in statistical mechanics.

cond-mat.stat-mech↗

Irreversible Thermodynamics in Multiscale Stochastic Dynamical Systems

This work extends the results of the recently developed theory of a rather complete thermodynamic formalism for discrete-state, continuous-time Markov processes with and without detailed balance. We aim at investigating the question that whether and how the thermodynamic structure is invariant in a multiscale stochastic system. That is, whether the relations between thermodynamic functions of state and process variables remain unchanged when the system is viewed at different time scales and resolutions. Our results show that the dynamics on a fast time scale contribute an entropic term to the "internal energy function", $u_S(x)$, for the slow dynamics. Based on the conditional free energy $u_S(x)$, one can then treat the slow dynamics as if the fast dynamics is nonexistent. Furthermore, we show that the free energy, which characterizes the spontaneous organization in a system without detailed balance, is invariant with or without the fast dynamics: The fast dynamics is assumed to reach stationarity instantaneously on the slow time scale; they have no effect on the system's free energy. The same can not be said for the entropy and the internal energy, both of which contain the same contribution from the fast dynamics. We also investigate the consequences of time-scale separation in connection to the concepts of quasi-stationaryty and steady-adiabaticity introduced in the phenomenological steady-state thermodynamics.

cond-mat.stat-mech↗

van't Hoff-Arrhenius Analysis of Mesoscopic and Macroscopic Dynamics of Simple Biochemical Systems: Stochastic vs. Nonlinear Bistabilities

Multistability of mesoscopic, driven biochemical reaction systems has implications to a wide range of cellular processes. Using several simple models, we show that one class of bistable chemical systems has a deterministic counterpart in the nonlinear dynamics based on the Law of Mass Action, while another class, widely known as noise-induced stochastic bistability, does not. Observing the system's volume ($V$) playing a similar role as the inverse temperature ($β$) in classical rate theory, an van't Hoff-Arrhenius like analysis is introduced. In one-dimensional systems, a transition rate between two states, represented in terms of a barrier in the landscape for the dynamics $Φ(x,V)$, $k\propto\exp\{-VΔΦ^‡(V)\}$, can be understood from a decomposition $ΔΦ^‡(V) \approxΔϕ_0^‡ Δϕ_1^‡/V$. Nonlinear bistability means $Δϕ_0^‡>0$ while stochastic bistability has $Δϕ_0^‡<0$ but $Δϕ_1^‡>0$. Stochastic bistabilities can be viewed as remants (or "ghosts) of nonlinear bifurcations or extinction phenomenon, and $Δϕ_0^‡$ and $Δϕ_1^‡$ as "enthalpic" and "entropic" barriers to a transition.

physics.chem-ph↗

An annotated English translation of `Kinetics of stationary reactions' [M. I. Temkin, Dolk. Akad. Nauk SSSR. 152, 156 (1963)]

Temkin's 1963 article on one-way fluxes and flux ratios in steady-state reaction systems bears directly on current research in physical and biological chemistry, such as in the interpretation of metabolic exchange fluxes determined from isotopomer labeling experiments. Yet, originally published in Russian [Dolk. Akad. Nauk SSSR 152, 156-159 (1963)], this article has remained inaccessible to much of the scientific community. Here we provide an English translation of the original article with several additional clarifications and corrections.

physics.chem-ph↗

The Physical Origins of Entropy Production, Free Energy Dissipation and their Mathematical Representations

A complete mathematical theory of nonequilibrium thermodynamics of stochastic systems in terms of master equations is presented. As generalizations of isothermal entropy and free energy, two functions of states play central roles: the Gibbs entropy $S$ and the relative entropy $F$, which are related via the stationary distribution of the stochastic dynamics. $S$ satisfies the fundamental entropy balance equation $dS/dt=e_p-h_d/T$ with entropy production rate $e_p\ge 0$ and heat dissipation rate $h_d$, while $dF/dt=-f_d\le 0$. For closed systems that satisfy detailed balance: $Te_p(t)=f_d(t)$. For open system one has $Te_p(t)=f_d(t)+Q_{hk}(t)$ where the housekeeping heat $Q_{hk}\ge 0$ was first introduced in the phenomenological nonequilibrium steady state thermodynamics. Entropy production $e_p$ consists of free energy dissipation associated with spontaneous relaxation, $f_d$, and active energy pumping that sustains the open system $Q_{hk}$. The amount of excess heat involved in the relaxation $Q_{ex}=h_d-Q_{hk} = f_d-T(dS/dt)$.

cond-mat.stat-mech↗

Thermodynamic Limit of a Nonequilibrium Steady-State: Maxwell-Type Construction for a Bistable Biochemical System

We show that the thermodynamic limit of a bistable phosphorylation-dephosphorylation cycle has a selection rule for the "more stable" macroscopic steady state. The analysis is akin to the Maxwell construction. Based on the chemical master equation approach, it is shown that, except at a critical point, bistability disappears in the stochastic model when fluctuation is sufficiently low but unneglectable. Onsager's Gaussian fluctuation theory applies to the unique macroscopic steady state. With initial state in the basin of attraction of the "less stable" steady state, the deterministic dynamics obtained by the Law of Mass Action is a metastable phenomenon. Stability and robustness in cell biology are stochastic concepts.

physics.chem-ph↗

Amplification and detection of single molecule conformational fluctuation through a protein interaction network with bimodal distributions

A protein undergoes conformational dynamics with multiple time scales, which results in fluctuating enzyme activities. Recent studies in single molecule enzymology have observe this "age-old" dynamic disorder phenomenon directly. However, the single molecule technique has its limitation. To be able to observe this molecular effect with real biochemical functions {\it in situ}, we propose to couple the fluctuations in enzymatic activity to noise propagations in small protein interaction networks such as zeroth order ultra-sensitive phosphorylation-dephosphorylation cycle. We showed that enzyme fluctuations could indeed be amplified by orders of magnitude into fluctuations in the level of substrate phosphorylation | a quantity widely interested in cellular biology. Enzyme conformational fluctuations sufficiently slower than the catalytic reaction turn over rate result in a bimodal concentration distribution of the phosphorylated substrate. In return, this network amplified single enzyme fluctuation can be used as a novel biochemical "reporter" for measuring single enzyme conformational fluctuation rates.

q-bio.BM↗

Nonequilibrium phase transition in a mesoscopic biochemical system: From stochastic to nonlinear dynamics and beyond

A rigorous mathematical framework for analyzing the chemical master equation (CME) with bistability, based on the theory of large deviation, is proposed. Using a simple phosphorylation-dephosphorylation cycle with feedback as an example, we show that a nonequilibrium steady-state (NESS) phase transition occurs in the system which has all the characteristics of classic equilibrium phase transition: Maxwell construction, discontinuous fraction of phosphorylation as a function of the kinase activity, and Lee-Yang's zero for the generating function. The cusp in nonlinear bifurcation theory matches the tricritical point of the phase transition. The mathematical analysis suggests three distinct time scales, and related mathematical descriptions, of (i) molecular signaling, (ii) biochemical network dynamics, and (iii) cellular evolution. The (i) and (iii) are stochastic while (ii) is deterministic.

physics.bio-ph↗

Synchronized Dynamics and Nonequilibrium Steady States in a Stochastic Yeast Cell-Cycle Network

Applying the mathematical circulation theory of Markov chains, we investigate the synchronized stochastic dynamics of a discrete network model of yeast cell-cycle regulation where stochasticity has been kept rather than being averaged out. By comparing the network dynamics of the stochastic model with its corresponding deterministic network counterpart, we show that the synchronized dynamics can be soundly characterized by a dominant circulation in the stochastic model, which is the natural generalization of the deterministic limit cycle in the deterministic system. Moreover, the period of the main peak in the power spectrum, which is in common use to characterize the synchronized dynamics, perfectly corresponds to the number of states in the main cycle with dominant circulation. Such a large separation in the magnitude of the circulations, between a dominant, main cycle and the rest, gives rise to the stochastic synchronization phenomenon.

q-bio.MN↗

Sensitivity Regulation based on Noise Propagation in Stochastic Reaction Networks

In this work we focus on how noise propagates in biochemical reaction networks and affects sensitivities of the system. We discover that the stochastic fluctuations can enhance sensitivities in one region of the value of control parameters by reducing sensitivities in another region. Based on this compensation principle, we designed a concentration detector in which enhanced amplification is achieved by an incoherent feedforward reaction network.

q-bio.MN↗

Clustering Coefficients of Protein-Protein Interaction Networks

The properties of certain networks are determined by hidden variables that are not explicitly measured. The conditional probability (propagator) that a vertex with a given value of the hidden variable is connected to k of other vertices determines all measurable properties. We study hidden variable models and find an averaging approximation that enables us to obtain a general analytical result for the propagator. Analytic results showing the validity of the approximation are obtained. We apply hidden variable models to protein-protein interaction networks (PINs) in which the hidden variable is the association free-energy, determined by distributions that depend on biochemistry and evolution. We compute degree distributions as well as clustering coefficients of several PINs of different species; good agreement with measured data is obtained. For the human interactome two different parameter sets give the same degree distributions, but the computed clustering coefficients differ by a factor of about two. This shows that degree distributions are not sufficient to determine the properties of PINs.

q-bio.QM↗

Relationship between Thermodynamic Driving Force and One-Way Fluxes in Reversible Chemical Reactions

Chemical reaction systems operating in nonequilibrium open-system states arise in a great number of contexts, including the study of living organisms, in which chemical reactions, in general, are far from equilibrium. Here we introduce a theorem that relates forward and re-verse fluxes and free energy for any chemical process operating in a steady state. This rela-tionship, which is a generalization of equilibrium conditions to the case of a chemical process occurring in a nonequilibrium steady state, provides a novel equivalent definition for chemical reaction free energy. In addition, it is shown that previously unrelated theories introduced by Ussing and Hodgkin and Huxley for transport of ions across membranes, Hill for catalytic cycle fluxes, and Crooks for entropy production in microscopically reversible systems, are united in a common framework based on this relationship.

q-bio.SC↗

Characterization of Metastatic Tumor Formation by the Colony Size Distribution

Knowledge regarding the kinetics of metastatic tumor formation, as related to the growth of the primary tumor, represents a fundamental issue in cancer biology. Using an in vivo mammalian model, we show here that one can obtain useful information from the frequency distribution of the sizes of metastatic colonies in distant organs after serial sectioning and image reconstruction. To explain the experimental findings, we constructed a biophysical model based on the respective growth patterns of the primary tumor and metastases and a stochastic process of metastatic colony formation. Heterogeneous distributions of various biological parameters were considered. We found that the elementary assumption of exponential forms of growth for the primary tumor and metastatic colonies predicts a linear relation on a log-log plot of a metastatic colony size distribution, which was consistent with the experimental results. Furthermore, the slope of the curve signifies the ratio of growth rates of the primary and the metastases. Non-exponential (Gompertzian and logistic) tumor growth patterns were also incorporated into the theory to explain possible deviation from the log-log linear relation. The observed metastasis-free probability also supported the assumption of a time-dependent Poisson process. With this approach, we determined the mechanistic parameters governing the process of metastatogenesis in the lungs for two murine tumor cell lines (KHT and MCaK). Since biological parameters specified in the model could be obtained in the laboratory, a workable metastatic "assay" may be established for various malignancies and in turn contribute in formulating rational treatment regimens for subclinical metastases.

q-bio.TO↗

Free-energy distribution of binary protein-protein binding suggests cross-species interactome differences

Major advances in large-scale yeast two hybrid (Y2H) screening have provided a global view of binary protein-protein interactions across species as dissimilar as human, yeast, and bacteria. Remarkably, these analyses have revealed that all species studied have a degree distribution of protein-protein binding that is approximately scale-free (varies as a power law) even though their evolutionary divergence times differ by billions of years. The universal power-law shows only the surface of the rich information harbored by these high-throughput data. We develop a detailed mathematical model of the protein-protein interaction network based on association free energy, the biochemical quantity that determines protein-protein interaction strength. This model reproduces the degree distribution of all of the large-scale Y2H data sets available and allows us to extract the distribution of free energy, the likelihood that a pair of proteins of a given species will bind. We find that across-species interactomes have significant differences that reflect the strengths of the protein-protein interaction. Our results identify a global evolutionary shift: more evolved organisms have weaker binary protein-protein binding. This result is consistent with the evolution of increased protein unfoldedness and challenges the dogma that only specific protein-protein interactions can be biologically functional..

q-bio.QM↗

Generalized Haldane Equation and Fluctuation Theorem in the Steady State Cycle Kinetics of Single Enzymes

Enyzme kinetics are cyclic. We study a Markov renewal process model of single-enzyme turnover in nonequilibrium steady-state (NESS) with sustained concentrations for substrates and products. We show that the forward and backward cycle times have idential non-exponential distributions: $\QQ_+(t)=\QQ_-(t)$. This equation generalizes the Haldane relation in reversible enzyme kinetics. In terms of the probabilities for the forward ($p_+$) and backward ($p_-$) cycles, $k_BT\ln(p_+/p_-)$ is shown to be the chemical driving force of the NESS, $Δμ$. More interestingly, the moment generating function of the stochastic number of substrate cycle $ν(t)$, $ $ follows the fluctuation theorem in the form of Kurchan-Lebowitz-Spohn-type symmetry. When $λ$ = $Δμ/k_BT$, we obtain the Jarzynski-Hatano-Sasa-type equality: $ $ $\equiv$ 1 for all $t$, where $νΔμ$ is the fluctuating chemical work done for sustaining the NESS. This theory suggests possible methods to experimentally determine the nonequilibrium driving force {\it in situ} from turnover data via single-molecule enzymology.

cond-mat.stat-mech↗

Metabolic Futile Cycles and Their Functions: A Systems Analysis of Energy and Control

It has long been hypothesized that futile cycles in cellular metabolism are involved in the regulation of biochemical pathways. Following the work of Newsholme and Crabtree, we develop a quantitative theory for this idea based on open-system thermodynamics and metabolic control analysis. It is shown that the {\it stoichiometric sensitivity} of an intermediary metabolite concentration with respect to changes in steady-state flux is governed by the effective equilibrium constant of the intermediate formation, and the equilibrium can be regulated by a futile cycle. The direction of the shift in the effective equilibrium constant depends on the direction of operation of the futile cycle. High stoichiometric sensitivity corresponds to ultrasensitivity of an intermediate concentration to net flow through a pathway; low stoichiometric sensitivity corresponds to super-robustness of concentration with respect to changes in flux. Both cases potentially play important roles in metabolic regulation. Futile cycles actively shift the effective equilibrium by expending energy; the magnitude of changes in effective equilibria and sensitivities is a function of the amount of energy used by a futile cycle. This proposed mechanism for control by futile cycles works remarkably similarly to kinetic proofreading in biosynthesis. The sensitivity of the system is also intimately related to the rate of concentration fluctuations of intermediate metabolites. The possibly different roles of the two major mechanisms for cellular biochemical regulation, namely reversible chemical modifications via futile cycles and shifting equilibrium by macromolecular binding, are discussed.

q-bio.SC↗

Fluctuation Theorems of Brownian Particles Controlled by a Maxwell's Demon

We study the stochastic dynamics of Brownian particles in a heat bath and subject to an active feedback control by an external, Maxwell's demon-like agent. The agent uses the information of the velocity of a particle and reduces its thermal agitation by applying a force. The entropy of the particle and the heat bath as a whole, thus, reduces. Entropy pumping [Phys. Rev. Lett. 93, 120602 (2004)] quantifies the entropy reduction. We discover that the entropy pumping has a dual role of work and heat contributing to free energy changes and entropy production of the open-system with the feedback control. Generalized Jarzynski equality and fluctuation theorems for work functional and entropy production are developed with the presence of the entropy pumping.

physics.class-ph↗