Stochastic simulation of chemical reactions with spatial resolution and single molecule detail

Methods are presented for simulating chemical reaction networks with a spatial resolution that is accurate to nearly the size scale of individual molecules. Using an intuitive picture of chemical reaction systems, each molecule is treated as a point-like particle that diffuses freely in three-dimensional space. When a pair of reactive molecules collide, such as an enzyme and its substrate, a reaction occurs and the simulated reactants are replaced by products. Achieving accurate bimolecular reaction kinetics is surprisingly difficult, requiring a careful consideration of reaction processes that are often overlooked. This includes whether the rate of a reaction is at steady-state and the probability that multiple reaction products collide with each other to yield a back reaction. Inputs to the simulation are experimental reaction rates, diffusion coefficients and the simulation time step. From these are calculated the simulation parameters, including the 'binding radius' and the 'unbinding radius', where the former defines the separation for a molecular collision and the latter is the initial separation between a pair of reaction products. Analytic solutions are presented for some simulation parameters while others are calculated using look-up tables. Capabilities of these methods are demonstrated with simulations of a simple bimolecular reaction and the Lotka-Volterra system.

[1]  M. Muir Physical Chemistry , 1888, Nature.

[2]  E. M.,et al.  Statistical Mechanics , 2021, Manual for Theoretical Chemistry.

[3]  J. C. Jaeger,et al.  Conduction of Heat in Solids , 1952 .

[4]  George E. Kimball,et al.  Diffusion-controlled reaction rates , 1949 .

[5]  Richard M. Noyes,et al.  KINETICS OF COMPETITIVE PROCESSES WHEN REACTIVE FRAGMENTS ARE PRODUCED IN PAIRS , 1955 .

[6]  John Crank,et al.  The Mathematics Of Diffusion , 1956 .

[7]  Donald L. Kreider,et al.  Elementary differential equations , 1968 .

[8]  R. L. E. Schwarzenberger,et al.  Elementary Differential Equations , 1970 .

[9]  G. Barton The Mathematics of Diffusion 2nd edn , 1975 .

[10]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[11]  D. Ermak,et al.  Brownian dynamics with hydrodynamic interactions , 1978 .

[12]  Scott H. Northrup,et al.  The stable states picture of chemical reactions. I. Formulation for rate constants and initial condition effects , 1980 .

[13]  D. Weaver Nonequilibrium decay effects in diffusion‐controlled processes , 1980 .

[14]  P. Cordier,et al.  Modified Smoluchowski equation and a unified theory of the diffusion‐controlled recombination , 1980 .

[15]  H. Berg Random Walks in Biology , 2018 .

[16]  Noam Agmon,et al.  Diffusion with back reaction , 1984 .

[17]  Stephen A. Rice Diffusion-limited reactions , 1985 .

[18]  D. Bertsekas,et al.  Introduction to Probability , 2002 .

[19]  A. Szabó,et al.  Theory of diffusion-influenced fluorescence quenching , 1989 .

[20]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[21]  H. Erickson,et al.  Kinetics of protein-protein association explained by Brownian dynamics computer simulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[22]  N. Agmon,et al.  Brownian dynamics simulations of reversible reactions in one dimension , 1993 .

[23]  C. Lumsden,et al.  Stochastic Simulation of Coupled Reaction-Diffusion Processes , 1996 .

[24]  Berend Smit,et al.  Understanding molecular simulation: from algorithms to applications , 1996 .

[25]  Tânia Tomé,et al.  Spatial instabilities and local oscillations in a lattice gas Lotka–Volterra model , 1997 .

[26]  S. Leibler,et al.  Robustness in simple biochemical networks , 1997, Nature.

[27]  K. Mukhopadhyay,et al.  Conformation induction in melanotropic peptides by trifluoroethanol: fluorescence and circular dichroism study. , 1998, Biophysical chemistry.

[28]  D. Bray,et al.  Origins of individual swimming behavior in bacteria. , 1998, Biophysical journal.

[29]  J. M. G. Vilar,et al.  Effects of Noise in Symmetric Two-Species Competition , 1998, cond-mat/9801260.

[30]  M. Sikorski,et al.  The kinetics of fast fluorescence quenching processes , 1998 .

[31]  M. Elowitz,et al.  Protein Mobility in the Cytoplasm ofEscherichia coli , 1999, Journal of bacteriology.

[32]  T. Bartol,et al.  Monte Carlo Methods for Simulating Realistic Synaptic Microphysiology Using MCell , 2000 .

[33]  Noam Agmon,et al.  Non-Exponential Smoluchowski Dynamics in Fast Acid−Base Reaction , 2000 .

[34]  A. Arkin Synthetic cell biology. , 2001, Current opinion in biotechnology.

[35]  A. V. Popov,et al.  Three-dimensional simulations of reversible bimolecular reactions: The simple target problem , 2001 .

[36]  N Le Novère,et al.  Conformational spread in a ring of proteins: a stochastic approach to allostery. , 2001, Journal of molecular biology.

[37]  C. Rao,et al.  Control, exploitation and tolerance of intracellular noise , 2002, Nature.

[38]  B. Spagnolo,et al.  Role of the noise on the transient dynamics of an ecosystem of interacting species , 2002 .

[39]  D. Frenkel,et al.  Understanding molecular simulation : from algorithms to applications. 2nd ed. , 2002 .

[40]  Leslie M Loew,et al.  Computational cell biology: spatiotemporal simulation of cellular events. , 2002, Annual review of biophysics and biomolecular structure.

[41]  Masaru Tomita,et al.  Computational Challenges in Cell Simulation: A Software Engineering Approach , 2002, IEEE Intell. Syst..

[42]  Kathleen R Ryan,et al.  Temporal and spatial regulation in prokaryotic cell cycle progression and development. , 2003, Annual review of biochemistry.

[43]  S. Schnell,et al.  Reaction kinetics in intracellular environments with macromolecular crowding: simulations and rate laws. , 2004, Progress in biophysics and molecular biology.