Chemical Langevin equations

A major difficulty is that such hierarchies of molecular models are not exactly known. Recent work by Gillespie (2000, 2002) has established such a hierarchy for stochastic models of chemical reactions in a well-mixed batch reactor. This hierarchy is depicted in Fig. 3b. In particular, it was shown that the chemical master equation is deduced to a chemical Langevin equation when the population sizes are relatively large. Finally, the deterministic behavior can be... 

Gillespie, D.T The chemical Langevin equation. J. Chem. Phys. 2000,113, 297. 

Chemical Langevin equations Daniel Gillespie devised a Langevin equation that describes the time evolution of concentrations of species reacting away from the thermodynamic limit. 

For each of the chemical species 5,, Kuntz and Gillespie derived the following chemical Langevin equation (CLE) ... 

Figure 18.1 Regimes of the problem space for multiscale stochastic simulations of chemical reaction kinetics. The r-axis represents the number of molecules of reacting species, x, and the ) -axis measures the frequency of reaction events, A. The threshold variables demarcate the partitions of modeling formalisms. In area I, the number of molecules is so small and the reaction events are so infrequent that a discrete-stochastic simulation algorithm, like the SSA, is needed. In contrast, in area V, which extends to infinity, the thermodynamic limit assumption becomes vahd and a continuous-deterministic modehng formalism becomes valid. Other areas admit different modehng formalisms, such as ones based on chemical Langevin equations, or probabilistic steady-state assumptions.

In the fast-continuous region, species populations can be assumed to be continuous variables. Because the reactions are sufficiently fast in comparison to the rest of the system, it can be assumed that they have relaxed to a steady-state distribution. Furthermore, because of the frequency of reaction rates, and the population size, the population distributions can be assumed to have a Gaussian shape. The subset of fast reactions can then be approximated as a continuous time Markov process with chemical Langevin Equations (CLE). The CLE is an ltd stochastic differential equation with multiplicative noise, as discussed in Chapter 13. 

Salis and Kaznessis separated the system into slow and fast reactions and managed to overcome the inadequacies and achieve a substantial speed up compared to the SSA while retaining accuracy. Fast reactions are approximated as a continuous Markov process, through Chemical Langevin Equations (CLE), discussed in Chapter 13, and the slow subset is approximated through jump equations derived by extending the Next Reaction variant approach. 

Propagation of the fast subsystem - chemical Langevin equations The fast subset dynamics are assumed to follow a continuous Markov process description and therefore a multidimensional Fokker-Planck equation describes their time evolution. The multidimensional Fokker-Plank equation more accurately describes the evolution of the probability distribution of only the fast reactions. The solution is a distribution depicting the state occupancies. If the interest is in obtaining one of the possible trajectories of the solution, the proper course of action is to solve a system of chemical Langevin equations (CLEs). 

Adelman, S. A. Generalized Langevin equations and many-body problems in chemical physics,... 

Adelman [530] and Stillman and Freed [531] have discussed the reduction of the generalised Langevin equation to a generalised Fokker— Planck equation, which provides a description of the probability that a molecule has a velocity u at a position r at a time t, given certain initial conditions (see Sect. 3.2.). The generalised Fokker—Planck equation has important differences by comparison with the (Markovian) Fokker— Planck equation (287). However, it has not proved so convenient a vehicle for studies of chemical reactions in solution as the generalised Langevin equation (290). 

This section is organized as follows in subsection A the approaches based on the assumption of heat bath statistical equilibrium and those which use the generalized Langevin equation are reviewed for the case of a bounded one-dimensional Brownian particle. A detailed analysis of the activation dynamics in both schemes is carried out by adopting AEP and CFP techniques. In subsection B we shall consider a case where the non-Markovian eharacter of the variable velocity stems from the finite duration of the coherence time of the light used to activate the chemical reaction process itself. 

The current attempts at generalizing the Kramers theory of chemical reactions touch two major problems The fluctuations of the potential driving the reaction coordinate, including the fluctuations driven by external radiation fields, and the non-Markovian character of the relaxation process affecting the velocity variable associated to the reaction coordinate. When the second problem is dealt with within the context of the celebrated generalized Langevin equation... 

Thus far, we have described the time-dependent nature of polymerizing environments both through stochastic [49-51] and lattice [52,53] models capable of addressing this kind of dynamics in a complex environment. The current article focuses on the former approach, but now rephrases the earlier justification of the use of the irreversible Langevin equation, iGLE, to the polymerization problem in the context of kinetic models, and specifically the chemical stochastic equation. The nonstationarity in the solvent response due to the collective polymerization of the dense solvent now appears naturally. This leads to a clear recipe for the construction of the requisite terms in the iGLE. Namely the potential of mean force and the friction kernel as described in Section 3. With these tools in hand, the iGLE is used... 

In this chapter we have focused on Hamiltonian dynamics, which describe the dynamics of molecules in the gas phase. To analyze the chemical reaction in the condensed phase, the NF chemical reaction theory has been recently extended to the Langevin equation and the generalized Langevin equa-... 

Baneijee, D., B. C. Bag, S. K. Banik, and D. S. Ray. 2004. Solution of quantum Langevin equation Approximations, theoretical and numerical aspects. Journal of Chemical Physics 120(19) 8960-8972. 

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