Publisher's Synopsis
Encyclopaedia of Theory and Applications of Monte Carlo Simulations is a comprehensive guide introducing researchers and experts to recent advances and applications of Monte Carlo Simulation (MCS). Innovative applications of MCS are revealed in various fields such as financial systems modeling, estimation of tran¬sition behavior of organic molecules, chemical reaction, particle diffusion, kinetic simulation of biophysics and biological data, and healthcare practices. The intention of the book is to integrate the knowledge of MCS from diverse fields to assist research and new applications of MCS. Monte Carlo simulation allows people to account for risk in quantitative analysis and decision making. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil & gas, transportation, and the environment. Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action. It shows the extreme possibilities-the out¬comes of going for broke and for the most conservative decision-along with all possible consequences for middle-of-the-road decisions. Monte Carlo simulation is now an established technique to solve prob¬lems in a wide variety of disciplines. It is an essential tool in statistical mechanics and has been frequently used for problems in material science and engineering. Monte Carlo method relies on selecting an initial configuration state and then iterate through a series of steps during which one or more moves are selected and evaluated for acceptance. The number of moves per step is often taken to be the number of available degrees of freedom in that step so as to ensure probing (on the average) of each degree of freedom once per step. However, other types of implementations (such as a rejection-free algorithm) are also possible. Use of Monte Carlo method to solve biological problems has its unique challenges, but if successfully addressed, it can become a powerful tool. Monte Carlo models (for biological systems) can be developed based on statistical mechanics and thus it is expected that it would work well for probing the underlying biophysics of a complex biological process. Of particular interest is a class of Monte Carlo simulations that are based on a set of pre-defined probabilistic rate constants and can be called kinetic Monte Carlo mod¬els. Probabilistic rate constant based kinetic Monte Carlo simulations can be developed for a wide variety of biological processes that span multiple length and time scales. We demonstrate that controlled kinetic Monte Carlo experiments are capable of revealing fundamental regulatory mechanisms in a complex bio¬logical system. Encyclopaedia of Theory and Applications of Monte Carlo Simulations is a comprehensive guide introducing researchers and experts to recent advances and applications of Monte Carlo Simulation (MCS). Innovative applications of MCS are revealed in various fields such as financial systems modeling, estimation of tran¬sition behavior of organic molecules, chemical reaction, particle diffusion, kinetic simulation of biophysics and biological data, and healthcare practices. The intention of the book is to integrate the knowledge of MCS from diverse fields to assist research and new applications of MCS. Monte Carlo simulation allows people to account for risk in quantitative analysis and decision making. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil & gas, transportation, and the environment. Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action. It shows the extreme possibilities-the out¬comes of going for broke and for the most conservative decision-along with all possible consequences for middle-of-the-road decisions. Monte Carlo simulation is now an established technique to solve prob¬lems in a wide variety of disciplines. It is an essential tool in statistical mechanics and has been frequently used for problems in material science and engineering. Monte Carlo method relies on selecting an initial configuration state and then iterate through a series of steps during which one or more moves are selected and evaluated for acceptance. The number of moves per step is often taken to be the number of available degrees of freedom in that step so as to ensure probing (on the average) of each degree of freedom once per step. However, other types of implementations (such as a rejection-free algorithm) are also possible. Use of Monte Carlo method to solve biological problems has its unique challenges, but if successfully addressed, it can become a powerful tool. Monte Carlo models (for biological systems) can be developed based on statistical mechanics and thus it is expected that it would work well for probing the underlying biophysics of a complex biological process. Of particular interest is a class of Monte Carlo simulations that are based on a set of pre-defined probabilistic rate constants and can be called kinetic Monte Carlo mod¬els. Probabilistic rate constant based kinetic Monte Carlo simulations can be developed for a wide variety of biological processes that span multiple length and time scales. We demonstrate that controlled kinetic Monte Carlo experiments are capable of revealing fundamental regulatory mechanisms in a complex bio¬logical system.
