Gotowa bibliografia na temat „Chemical Langevin equation”

Stochastic modeling of biochemical systems has been the subject of intense research in recent years due to the large number of important applications of these systems. A critical stochastic model of well-stirred biochemical systems in the regime of relatively large molecular numbers, far from the thermodynamic limit, is the chemical Langevin equation. This model is represented as a system of stochastic differential equations, with multiplicative and noncommutative noise. Often biochemical systems in applications evolve on multiple time-scales; examples include slow transcription and fast dimerization reactions. The existence of multiple time-scales leads to mathematical stiffness, which is a major challenge for the numerical simulation. Consequently, there is a demand for efficient and accurate numerical methods to approximate the solution of these models. In this paper, we design an adaptive time-stepping method, based on control theory, for the numerical solution of the chemical Langevin equation. The underlying approximation method is the Milstein scheme. The adaptive strategy is tested on several models of interest and is shown to have improved efficiency and accuracy compared with the existing variable and constant-step methods.

This thesis is concerned with methodologies for the accurate quantitative modelling of molecular biological systems. The first part is devoted to the chemical Langevin equation (CLE), a stochastic differential equation driven by a multidimensional Wiener process. The CLE is an approximation to the standard discrete Markov jump process model of chemical reaction kinetics. It is valid in the regime where molecular populations are abundant enough to assume their concentrations change continuously, but stochastic fluctuations still play a major role. We observe that the CLE is not a single equation, but a family of equations with shared finite-dimensional distributions. On the theoretical side, we prove that as many Wiener processes are sufficient to formulate the CLE as there are independent variables in the equation, which is just the rank of the stoichiometric matrix. On the practical side, we show that in the case where there are m_1 pairs of reversible reactions and m_2 irreversible reactions, there is another, simple formulation of the CLE with only m_1+m_2 Wiener processes, whereas the standard approach uses 2m_1+m_2. Considerable computational savings are achieved with this latter formulation. A flaw of the CLE model is identified: trajectories may leave the nonnegative orthant with positive probability. The second part addresses the challenge when alternative, structurally different ordinary differential equation models of similar complexity fit the available experimental data equally well. We review optimal experiment design methods for choosing the initial state and structural changes on the biological system to maximally discriminate between the outputs of rival models in terms of L_2-distance. We determine the optimal stimulus (input) profile for externally excitable systems. The numerical implementation relies on sum of squares decompositions and is demonstrated on two rival models of signal processing in starving Dictyostelium amoebae. Such experiments accelerate the perfection of our understanding of biochemical mechanisms.

In spite of remarkable progress in molecular biology, our understanding of the dynamics and functions of intra- and inter-cellular biological networks has been hampered by their complexity. Kinetics modelling, an important type of mathematical modelling, provides a rigorous and reliable way to reveal the complexity of biological networks. In this thesis, two genetic regulatory networks have been investigated via kinetic models. In the first part of the study, a model is developed to represent the transcriptional regulatory network essential for the circadian rhythms in Drosophila. The model incorporates the transcriptional feedback loops revealed so far in the network of the circadian clock (PER/TIM and VRI/PDP1 loops). Conventional Hill functions are not used to describe the regulation of genes, instead the explicit reactions of binding and unbinding processes of transcription factors to promoters are modelled. The model is described by a set of ordinary differential equations and the parameters are estimated from the in vitro experimental data of the clocks’ components. The simulation results show that the model reproduces sustained circadian oscillations in mRNA and protein concentrations that are in agreement with experimental observations. It also simulates the entrainment by light-dark cycles, the disappearance of the rhythmicity in constant light and the shape of phase response curves resembling that of experimental results. The model is robust over a wide range of parameter variations. In addition, the simulated E-box mutation, perS and perL mutants are similar to that observed in the experiments. The deficiency between the simulated mRNA levels and experimental observations in per01, tim01 and clkJrk mutants suggests some differences in the model from reality. Finally, a possible function of VRI/PDP1 loops is proposed to increase the robustness of the clock. In the second part of the study, the sources of intrinsic noise and the influence of extrinsic noise are investigated on an intracellular viral infection system. The contribution of the intrinsic noise from each reaction is measured by means of a special form of stochastic differential equation, the chemical Langevin equation. The intrinsic noise of the system is the linear sum of the noise in each of the reactions. The intrinsic noise arises mainly from the degradation of mRNA and the transcription processes. Then, the effects of extrinsic noise are studied by means of a general form of stochastic differential equation. It is found that the noise of the viral components grows logarithmically with increasing noise intensities. The system is most susceptible to noise in the virus assembly process. A high level of noise in this process can even inhibit the replication of the viruses. In summary, the success of this thesis demonstrates the usefulness of models for interpreting experimental data, developing hypotheses, as well as for understanding the design principles of genetic regulatory networks.

Avec les progrès de la technologie, il est désormais devenu possible de manipuler des faibles quantités d’objets nanométriques, voire des objets uniques. Observer une réaction chimique de quelques centaines de molécules sur des catalyseurs, étudier le travail exercé lors du déploiement d’un brin d’ADN unique ou mesurer la chaleur émise par un unique électron dans un circuit électrique constituent aujourd’hui des actes expérimentaux courants. Cependant, à cette échelle, le caractère aléatoire des processus physiques étudiés se fait plus fortement ressentir. Développer une théorie thermodynamique à ces échelles nécessite d'y inclure de manière exhaustive ces fluctuations.Ces préoccupations et les résultats expérimentaux et théoriques associés ont mené à l’émergence de ce que l’on appelle aujourd’hui la thermodynamique stochastique. Cette thèse se propose de développer une approche originale à la thermodynamique stochastique, basée sur une extension de l'hypothèse d'équilibre local aux variables fluctuantes d'un système. Cette théorie offre de nouvelles définitions des grandeurs thermodynamiques stochastiques, dont l'évolution est donnée par des équations différentielles stochastiques (EDS).Nous avons choisi d'étudier cette théorie à travers des modèles simplifiés de phénomènes physiques variés; transport (diffusif) de chaleur ou de masse, transport couplé (comme la thermodiffusion), ainsi que des modèles de réactions chimiques linéaires et non-linéaires. A travers ces exemples, nous avons proposé des versions stochastiques de plusieurs grandeurs thermodynamiques d'intérêt. Une large part de cette thèse est dévolue à l'entropie et aux différents termes apparaissant dans son bilan (flux d'entropie, production d'entropie ou dissipation). D'autres exemples incluent l'énergie libre d'Helmholtz, la production d'entropie d'excès, ou encore les efficacités thermodynamiques dans le transport couplé.A l'aide de cette théorie, nous avons étudié les propriétés statistiques de ces différentes grandeurs, et plus particulièrement l'effet des contraintes thermodynamiques ainsi que les propriétés cinétiques du modèle sur celles-là. Dans un premier temps, nous montrons comment l'état thermodynamique d'un système (à l' équilibre ou hors d'équilibre) contraint la forme de la distribution de la production d'entropie. Au-delà de la production d'entropie, cette contrainte apparaît également pour d'autres quantités, comme l'énergie libre d'Helmholtz ou la production d'entropie d'excès. Nous montrons ensuite comment des paramètres de contrôle extérieurs peuvent induire des bimodalités dans les distributions d'efficacités stochastiques.Les non-linéarités de la cinétique peuvent également se répercuter sur la thermodynamique stochastique. En utilisant un modèle non-linéaire de réaction chimique, le modèle de Schlögl, nous avons calculé la dissipation moyenne, non-nulle, engendrée par les fluctuations du système. Les non-linéarités offrent aussi la possibilité de produire des bifurcations dans le système. Les différentes propriétés statistiques (moments et distributions) de la production d'entropie ont été étudiées à différents points avant, pendant et après la bifurcation dans le modèle de Schlögl.Ces nombreuses propriétés ont été étudiées via des développements analytiques supportés par des simulations numériques des EDS du système. Nous avons ainsi pu montrer la fine connexion existant entre les équations cinétiques du système, les contraintes thermodynamiques et les propriétés statistiques des fluctuations de différentes grandeurs thermodynamiques stochastiques.
Over the last decades, nanotechnology has experienced great steps forwards, opening new ways to manipulate micro- and nanosystems. These advances motivated the development of a thermodynamic theory for such systems, taking fully into account the unavoidable fluctuations appearing at that scale. This ultimately leads to an ensemble of experimental and theoretical results forming the emergent field of stochastic thermodynamics. In this thesis, we propose an original theoretical approach to stochastic thermodynamics, based on the extension of the local equilibrium hypothesis (LEH) to fluctuating variables in small systems. The approach provides new definitions of stochastic thermodynamic quantities, whose evolution is given by stochastic differential equations (SDEs).We applied this new formalism to a diverse range of systems: heat or mass diffusive transport, coupled transport phenomena (thermodiffusion), and linear or non-linear chemical systems. In each model, we used our theory to define key stochastic thermodynamic quantities. A great emphasis has been put on entropy and the different contributions to its evolution (entropy flux and entropy production) throughout this thesis. Other examples include also the stochastic Helmholtz energy, stochastic excess entropy production and stochastic efficiencies in coupled transport. We investigated how the statistical properties of these quantities are affected by external thermodynamic constraints and by the kinetics of the system. We first studied how the thermodynamic state of the system (equilibrium \textit{vs.} non-equilibrium) strongly impacts the distribution of entropy production. We then extended those findings to other related quantities, such as the Helmholtz free energy and excess entropy production. We also analysed how some external control parameters could lead to bimodality in stochastic efficiencies distributions.In addition, non-linearities affect stochastic thermodynamics quantities in different ways. Using the example of the Schlögl chemical model, we computed the average dissipation of the fluctuations in a non-linear system. Such systems can also undergo a bifurcation, and we studied how the moments and the distribution of entropy production change while crossing the critical point.All these properties were investigated with theoretical analyses and supported by numerical simulations of the SDEs describing the system. It allows us to show that properties of the evolution equations and external constraints could strongly reflect in the statistical properties of stochastic thermodynamic quantities.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

With an increased popularity for systems-based approaches in biology, a wide spectrum of techniques has been applied to the simulation and analysis of biochemical systems which involves uncertainty and stochasticity. It is particularly concerned with modelling and analysis of metabolic pathways, regulatory and signal transduction networks for understanding intra-cellular pathway behaviour. Typically, parameter estimation in ordinary differential equations(ODEs) models is used for this purpose when there is large number of molecules involved in the reaction system. However this approach is correct when the system is large enough to be deterministic in nature. But there are uncertainty involved in the system and the processes are stochastic in nature due to smaller population molecules participating in the pathway reactions. In this thesis the common theme is the study of uncertainties in the chemical kinetics of biochemical reaction systems associated with various intra-cellular pathways and channels. The study is at the mesoscale of the system, i.e., we study systems that do not have too few molecules disallowing any higher scale system level approximation nor too many where a non-stochastic (mesoscale) system approximation will be valid. In our first study we estimate the parameters in the mitogen activated protein kinase (MAPK) signal transduction pathway involved in the departure from the normal Epithelial Growth Factor(EGF) dose-dependency in prostate cancer cells. A model-based pathway analysis is performed. The pathway is mathematically modelled with 28 rate equations yielding those many ordinary differential equations(ODE) with kinetic rate constants that have been reported to take random values in the existing literature. This has led to us treating the ODE model of the pathways kinetics as a random differential equations(RDE) system in which the parameters are random variables. The most likely set of values of the kinetic rate constants obtained from fitting the RDE model into the experimental data is then used in a direct transcription based dynamic optimization method for computing the changes needed in these kinetic rate constant values for the restoration of the normal EGF dose response. It identifies the parameters, i.e., the kinetic rate constants in the RDE model, that are the most sensitive to the change in the EGF dose response behaviour in the PC3 prostate cancer cells. Biochemical pathways involving chemical kinetics equations in terms of low concen-trations of the model variables can be represented as chemical Langevin equations(CLE) as there is stochasticity involved in the processes. Most CLE systems come with the implicit constraint that the concentration state cannot be negative at any time over the sample path. Due to the inherent stiffness(especially in diffusion coefficient) of the CLE system, it has been difficult for numerical schemes to meet this positivity constraint during numerical simulations. Most available methods resort to heuristics by dropping selective noise terms from the original CLE inconsistent with the mesoscale physics involved in forming the CLE. Other methods take very small time steps thus making the simulation inefficient. In our second study we preserve positivity by using a physically consistent numerical scheme which is a modified form of fully stochastic α method for stiff stochastic differential equation. Ion channels are fundamental molecules in the nervous system that catalyse the flux of ions across the cell membrane. Single ion channel flux activity is comparable to the catalytic activity of single enzyme molecules. Saturating concentrations of substrate induce dynamic disorder in the kinetic rate processes of single enzyme molecules and consequently, develop correlative memory of the previous history of activities. Conversely, binding of substrate ion is known to alter the catalytic turnover of single ion channels. Here, we investigated the possible existence of dynamic disorder and molecular memory in single human TREK1 channel due to binding of substrate/agonist using the excised inside-out patch-clamp technique. Our results suggest that single hTREK1 channel behaves as a typical Michaelis-Menten enzyme molecule with a single high-affinity binding site for substrate K+ ion but with uncertainty in reaction rates.

This chapter discusses dynamical solvent effects on the rate constants for chemical reactions in solution. The effect is described by stochastic dynamics, where the influence of the solvent on the reaction dynamics is included by describing the motion along the reaction coordinate as Brownian motion. Two theoretical approaches are discussed: Kramers theory with a constant time-independent solvent friction coefficient and Grote–Hynes theory, a generalization of Kramers theory, based on the generalized Langevin equation with a time-dependent solvent friction coefficient. The expressions for the rate constants have the same form as in transition-state theory, but are multiplied by transmission coefficients that incorporate the dynamical solvent effect. In the limit of fast motion along the reaction coordinate, the solvent molecules can be considered as “frozen,” and the predictions of the Grote–Hynes theory can differ from the Kramers theory by several orders of magnitude.

This chapter explores stochastic behavior in biomolecular systems. It does so by first building on the preliminary discussion of stochastic modeling laid out in Chapter 2. The chapter reviews methods for modeling stochastic processes, including the chemical master equation (CME), the chemical Langevin equation (CLE), and the Fokker–Planck equation (FPE). Given a stochastic description, the chapter then analyzes the behavior of the system using a collection of stochastic simulation and analysis tools. This chapter makes use of a variety of topics in stochastic processes; readers should have a good working knowledge of basic probability and some exposure to simple stochastic processes.

Molecular-physics aspects of cold chemistry are introduced with the example of few-electron molecules. After a brief overview of general aspects of molecular physics, the solution of the molecular Schrödinger equation is presented based on the Born-Oppenheimer approximation and the subsequent evaluation of adiabatic, nonadiabatic, relativistic and radiative (QED) corrections. Low-temperature chemical phenomena are introduced with the example of ion-molecule reactions, using the classical Langevin model for barrier-free exothermic reactions as reference. Then, methods to generate cold few-electron molecules by supersonic-beam-deceleration methods such as Stark, Zeeman, and Rydberg-Stark decelerations are presented. Two astrophysically important reactions, the reaction between H2 and H2+ forming H3+ and H, a very fast reaction following Langevin-capture going over to quantum-Langevin capture at low temperature, and the radiative association reaction H+ + H forming H2+, a very slow reaction in which quantum effects (shape resonances) become important at low temperatures, are used to illustrate the concepts introduced.