Download or read book Kinetic Monte Carlo Simulation in Biophysics and Systems Biology written by Subhadip Raychaudhuri and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Kinetic Monte Carlo Simulation in Biophysics and Systems Biology.

Download or read book Monte Carlo Simulation in Systems Biology written by Jan Schellenberger and published by . This book was released on 2010 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Constraint Based Reconstruction and Analysis (COBRA) is a framework within the field of Systems Biology which aims to understand cellular metabolism through the analysis of large scale metabolic models. These models are based on meticulously curated reconstructions of all chemical reactions in an organism. Instead of attempting to predict the exact state of the biological system, COBRA describes the physiological constraints that the system must satisfy and studies the range of solutions satisfying these constraints. Monte Carlo Sampling is one of the COBRA methods used to study how biological properties are distributed over the entire solution space. A set of randomly distributed solutions is generated and serves as a proxy for the entire space. Various aspects of Monte Carlo Sampling in Systems Biology are illustrated : 1) Monte Carlo Sampling has been used historically (Chapter 1), 2) A faster and more efficient procedure for generating Monte Carlo Samples is developed (Chapter 2); 3) Carbon 13 tracing experiments are an important tool for measuring reaction rates through a network. Monte Carlo Sampling was used to optimize the choice of label and explain and measure the dimensionality of output data (Chapter 3); 4) It is possible to incorporate the thermodynamic "loop-law'' into many COBRA methods including sampling (Chapter 4). Additionally two software projects are presented which assist in analyzing COBRA models : 1) the BiGG knowledgebase of reconstructions (Chapter 5) and 2) the COBRA Matlab toolbox v. 2.0 (Chapter 6). These two projects make COBRA methods available to the scientific community.

Download or read book Multiscale Monte Carlo Methods to Cope with Separation of Scales in Stochastic Simulation of Biological Networks written by Asawari Samant and published by ProQuest. This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The emerging paradigm of systems biology aims at gaining a quantitative understanding of the organization, dynamics and control of biological phenomena, via an iterative process of experimentation and computation. Building a close link between the system-level physiology and the underlying molecular machinery has been made possible only by the recent advances in genomic and proteomic technologies. The idea of mathematical modeling is not new to biology; however, the formal introduction of computational biology as a scientific discipline was driven by the need for an efficient and systematic way of organizing and analyzing the vast amount of information generated by high throughput experimental platforms. Currently, the field of computational biology is in its infancy and is plagued by numerous challenges. One of the key challenges facing computational biology is building and simulating hierarchical models that span multiple length and time scales. Biological systems are inherently multiscale; not only in terms of time and length scales of intracellular processes, but also in the terms of the populations of species participating in these processes. Separation of scales reduces the efficiency and speed of most dynamic and spatial simulation techniques. In this thesis, we develop a multiscale approach to circumvent the problem of numerical stiffness in stochastic simulation of well-mixed reaction networks. The focus on a stochastic framework was motivated by two factors--firstly, the presence and role of stochasticity in biological systems is a well-established experimental fact, and secondly, accelerated stochastic algorithms to deal with numerical stiffness are currently unavailable. In this work, we develop a multiscale Monte Carlo (MSMC) method to efficiently deal with computational challenges stemming from the disparity of time scales in well-mixed stochastic networks. Broadly speaking, the developed multiscale framework extends the deterministic quasi-equilibrium (QE) approximation, computational singular perturbation (CSP), and low-dimensional manifold (LDM) concepts to stochastic simulation. We address various issues to enable a seamless and probabilistically accurate algorithm. Specifically, dynamic network partitioning, on-the-fly relaxation of the fast network and numerical approximation and generation of the QE probability distribution function are some key issues addressed. Finally, incorporating the hybrid solvers enables us to deal with systems having mixed population scales in an efficient way. The modified method, called the hybrid multiscale Monte Carlo (HyMSMC) method, represents a significant improvement over the MSMC method. Especially, for stiff systems involving large populations the HyMSMC method is significantly faster than the MSMC method, as demonstrated with several examples.

Download or read book Applications of Monte Carlo Methods in Biology Medicine and Other Fields of Science written by Charles J. Mode and published by BoD – Books on Demand. This book was released on 2011-02-28 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is an eclectic mix of applications of Monte Carlo methods in many fields of research should not be surprising, because of the ubiquitous use of these methods in many fields of human endeavor. In an attempt to focus attention on a manageable set of applications, the main thrust of this book is to emphasize applications of Monte Carlo simulation methods in biology and medicine.

Download or read book Theory and Applications of Monte Carlo Simulations written by Victor Chan and published by . This book was released on 2013 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to introduce researchers and practitioners to recent advances and applications of Monte Carlo Simulation (MCS). Random sampling is the key of the MCS technique. The 11 chapters of this book collectively illustrates how such a sampling technique is exploited to solve difficult problems or analyze complex systems in various engineering and science domains. Issues related to the use of MCS including goodness-of-fit, uncertainty evaluation, variance reduction, optimization, and statistical estimation are discussed and examples of solutions are given. Novel applications of MCS are demonstrated in financial systems modeling, estimation of transition behavior of organic molecules, chemical reaction, particle diffusion, kinetic simulation of biophysics and biological data, and healthcare practices. To enlarge the accessibility of this book, both field-specific background materials and field-specific usages of MCS are introduced in most chapters. The aim of this book is to unify knowledge of MCS from different fields to facilitate research and new applications of MCS.

Download or read book Monte Carlo Simulation for the Pharmaceutical Industry written by Mark Chang and published by CRC Press. This book was released on 2010-09-29 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: Helping you become a creative, logical thinker and skillful "simulator," Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies provides broad coverage of the entire drug development process, from drug discovery to preclinical and clinical trial aspects to commercialization. It presents the theories and metho

Download or read book Computational Approaches in Molecular Radiation Biology written by Matesh N. Varma and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Office of Health and Environmental Research (OHER) has supported and continues to support development of computational approaches in biology and medicine. OHER's Radiological and Chemical Physics Program initiated development of computational approaches to determine the effects produced by radiation of different quality (such as high energy electrons, protons, helium and other heavy ions, etc. ) in a variety of materials of biological interest-such as water, polymers and DNA; these include molecular excitations and sub-excitations and the production of ionization and their spatial and temporal distribution. In the past several years, significant advances have been made in computational methods for this purpose. In particular, codes based on Monte Carlo techniques have ·been developed that provide a realistic description of track-structure produced by charged particles. In addition, the codes have become sufficiently sophisticated so that it is now possible to calculate the spatial and temporal distribution of energy deposition patterns in small volumes of subnanometer and nanometer dimensions. These dimensions or resolution levels are relevant for our understanding of mechanisms at the molecular level by which radiations affect biological systems. Since the Monte Carlo track structure codes for use in radiation chemistry and radiation biology are still in the developmental stage, a number of investigators have been exploring different strategies for improving these codes.

Download or read book Rare Event Simulation using Monte Carlo Methods written by Gerardo Rubino and published by John Wiley & Sons. This book was released on 2009-03-18 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a probabilistic model, a rare event is an event with a very small probability of occurrence. The forecasting of rare events is a formidable task but is important in many areas. For instance a catastrophic failure in a transport system or in a nuclear power plant, the failure of an information processing system in a bank, or in the communication network of a group of banks, leading to financial losses. Being able to evaluate the probability of rare events is therefore a critical issue. Monte Carlo Methods, the simulation of corresponding models, are used to analyze rare events. This book sets out to present the mathematical tools available for the efficient simulation of rare events. Importance sampling and splitting are presented along with an exposition of how to apply these tools to a variety of fields ranging from performance and dependability evaluation of complex systems, typically in computer science or in telecommunications, to chemical reaction analysis in biology or particle transport in physics. Graduate students, researchers and practitioners who wish to learn and apply rare event simulation techniques will find this book beneficial.

Download or read book Mean Field Simulation for Monte Carlo Integration written by Pierre Del Moral and published by CRC Press. This book was released on 2013-05-20 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last three decades, there has been a dramatic increase in the use of interacting particle methods as a powerful tool in real-world applications of Monte Carlo simulation in computational physics, population biology, computer sciences, and statistical machine learning. Ideally suited to parallel and distributed computation, these advanced particle algorithms include nonlinear interacting jump diffusions; quantum, diffusion, and resampled Monte Carlo methods; Feynman-Kac particle models; genetic and evolutionary algorithms; sequential Monte Carlo methods; adaptive and interacting Markov chain Monte Carlo models; bootstrapping methods; ensemble Kalman filters; and interacting particle filters. Mean Field Simulation for Monte Carlo Integration presents the first comprehensive and modern mathematical treatment of mean field particle simulation models and interdisciplinary research topics, including interacting jumps and McKean-Vlasov processes, sequential Monte Carlo methodologies, genetic particle algorithms, genealogical tree-based algorithms, and quantum and diffusion Monte Carlo methods. Along with covering refined convergence analysis on nonlinear Markov chain models, the author discusses applications related to parameter estimation in hidden Markov chain models, stochastic optimization, nonlinear filtering and multiple target tracking, stochastic optimization, calibration and uncertainty propagations in numerical codes, rare event simulation, financial mathematics, and free energy and quasi-invariant measures arising in computational physics and population biology. This book shows how mean field particle simulation has revolutionized the field of Monte Carlo integration and stochastic algorithms. It will help theoretical probability researchers, applied statisticians, biologists, statistical physicists, and computer scientists work better across their own disciplinary boundaries.

Download or read book Prediction and Optimal Experimental Design in Systems Biology Models written by Fergal P. Casey and published by . This book was released on 2007 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Stochastic Methods for Parameter Estimation and Design of Experiments in Systems Biology written by Andrei Kramer and published by Logos Verlag Berlin GmbH. This book was released on 2016-02-11 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Chain Monte Carlo (MCMC) methods are sampling based techniques, which use random numbers to approximate deterministic but unknown values. They can be used to obtain expected values, estimate parameters or to simply inspect the properties of a non-standard, high dimensional probability distribution. Bayesian analysis of model parameters provides the mathematical foundation for parameter estimation using such probabilistic sampling. The strengths of these stochastic methods are their robustness and relative simplicity even for nonlinear problems with dozens of parameters as well as a built-in uncertainty analysis. Because Bayesian model analysis necessarily involves the notion of prior knowledge, the estimation of unidentifiable parameters can be regularised (by priors) in a straight forward way. This work draws the focus on typical cases in systems biology: relative data, nonlinear ordinary differential equation models and few data points. It also investigates the consequences of parameter estimation from steady state data; consequences such as performance benefits. In biology the data is almost exclusively relative, the raw measurements (e.g. western blot intensities) are normalised by control experiments or a reference value within a series and require the model to do the same when comparing its output to the data. Several sampling algorithms are compared in terms of effective sampling speed and necessary adaptations to relative and steady state data are explained.

Download or read book Theory and Applications of Monte Carlo Simulations written by Wai Kin (Victor) Chan and published by BoD – Books on Demand. This book was released on 2013-03-06 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to introduce researchers and practitioners to recent advances and applications of Monte Carlo Simulation (MCS). Random sampling is the key of the MCS technique. The 11 chapters of this book collectively illustrates how such a sampling technique is exploited to solve difficult problems or analyze complex systems in various engineering and science domains. Issues related to the use of MCS including goodness-of-fit, uncertainty evaluation, variance reduction, optimization, and statistical estimation are discussed and examples of solutions are given. Novel applications of MCS are demonstrated in financial systems modeling, estimation of transition behavior of organic molecules, chemical reaction, particle diffusion, kinetic simulation of biophysics and biological data, and healthcare practices. To enlarge the accessibility of this book, both field-specific background materials and field-specific usages of MCS are introduced in most chapters. The aim of this book is to unify knowledge of MCS from different fields to facilitate research and new applications of MCS.

Download or read book Markov chain Monte Carlo methods for parameter identification in systems biology models written by Theresa Niederberger and published by . This book was released on 2012 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Interacting Multiagent Systems written by Lorenzo Pareschi and published by Oxford University Press, USA. This book was released on 2014 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical modelling of systems constituted by many agents using kinetic theory is a new tool that has proved effective in predicting the emergence of collective behaviours and self-organization. This idea has been applied by the authors to various problems which range from sociology to economics and life sciences.

Download or read book Dynamic Systems Biology Modeling and Simulation written by Joseph DiStefano III and published by Academic Press. This book was released on 2015-01-10 with total page 886 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic Systems Biology Modeling and Simuation consolidates and unifies classical and contemporary multiscale methodologies for mathematical modeling and computer simulation of dynamic biological systems – from molecular/cellular, organ-system, on up to population levels. The book pedagogy is developed as a well-annotated, systematic tutorial – with clearly spelled-out and unified nomenclature – derived from the author's own modeling efforts, publications and teaching over half a century. Ambiguities in some concepts and tools are clarified and others are rendered more accessible and practical. The latter include novel qualitative theory and methodologies for recognizing dynamical signatures in data using structural (multicompartmental and network) models and graph theory; and analyzing structural and measurement (data) models for quantification feasibility. The level is basic-to-intermediate, with much emphasis on biomodeling from real biodata, for use in real applications. - Introductory coverage of core mathematical concepts such as linear and nonlinear differential and difference equations, Laplace transforms, linear algebra, probability, statistics and stochastics topics - The pertinent biology, biochemistry, biophysics or pharmacology for modeling are provided, to support understanding the amalgam of "math modeling with life sciences - Strong emphasis on quantifying as well as building and analyzing biomodels: includes methodology and computational tools for parameter identifiability and sensitivity analysis; parameter estimation from real data; model distinguishability and simplification; and practical bioexperiment design and optimization - Companion website provides solutions and program code for examples and exercises using Matlab, Simulink, VisSim, SimBiology, SAAMII, AMIGO, Copasi and SBML-coded models - A full set of PowerPoint slides are available from the author for teaching from his textbook. He uses them to teach a 10 week quarter upper division course at UCLA, which meets twice a week, so there are 20 lectures. They can easily be augmented or stretched for a 15 week semester course - Importantly, the slides are editable, so they can be readily adapted to a lecturer's personal style and course content needs. The lectures are based on excerpts from 12 of the first 13 chapters of DSBMS. They are designed to highlight the key course material, as a study guide and structure for students following the full text content - The complete PowerPoint slide package (~25 MB) can be obtained by instructors (or prospective instructors) by emailing the author directly, at: [email protected]

Download or read book Monte Carlo Strategies in Scientific Computing written by Jun S. Liu and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as a textbook for a graduate-level course on Monte Carlo methods.

Download or read book Simulation Algorithms for Computational Systems Biology written by Luca Marchetti and published by Springer. This book was released on 2017-09-27 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the state-of-the-art algorithms used to simulate biological dynamics. Each technique is theoretically introduced and applied to a set of modeling cases. Starting from basic simulation algorithms, the book also introduces more advanced techniques that support delays, diffusion in space, or that are based on hybrid simulation strategies. This is a valuable self-contained resource for graduate students and practitioners in computer science, biology and bioinformatics. An appendix covers the mathematical background, and the authors include further reading sections in each chapter.