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An Algorithm For The Stochastic Simulation Of Gene Expression And Cell Population Dynamics
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Book Synopsis An Algorithm for the Stochastic Simulation of Gene Expression and Cell Population Dynamics by : Daniel A. Charlebois
Download or read book An Algorithm for the Stochastic Simulation of Gene Expression and Cell Population Dynamics written by Daniel A. Charlebois and published by . This book was released on 2010 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past few years, it has been increasingly recognized that stochastic mechanisms play a key role in the dynamics of biological systems. Genetic networks are one example where molecular-level fluctuations are of particular importance. Here stochasticity in the expression of gene products can result in genetically identical cells in the same environment displaying significant variation in biochemical or physical attributes. This variation can influence individual and population-level fitness. In this thesis we first explore the background required to obtain analytical solutions and perform simulations of stochastic models of gene expression. Then we develop an algorithm for the stochastic simulation of gene expression and heterogeneous cell population dynamics. The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte Carlo approach to simulate the statistical characteristics of growing cell populations. This approach permits biologically realistic and computationally feasible simulations of environment and time-dependent cell population dynamics. The algorithm is benchmarked against steady-state and time-dependent analytical solutions of gene expression models, including scenarios when cell growth, division, and DNA replication are incorporated into the modelling framework. Furthermore, using the algorithm we obtain the steady-state cell size distribution of a large cell population, grown from a small initial cell population undergoing stochastic and asymmetric division, to the size distribution of a small representative sample of this population simulated to steady-state. These comparisons demonstrate that the algorithm provides an accurate and efficient approach to modelling the effects of complex biological features on gene expression dynamics. The algorithm is also employed to simulate expression dynamics within 'bet-hedging' cell populations during their adaption to environmental stress. These simulations indicate that the cell population dynamics algorithm provides a framework suitable for simulating and analyzing realistic models of heterogeneous population dynamics combining molecular-level stochastic reaction kinetics, relevant physiological details, and phenotypic variability and fitness.
Book Synopsis Stochastic Modeling and Analysis of Pathway Regulation and Dynamics by : Chen Zhao
Download or read book Stochastic Modeling and Analysis of Pathway Regulation and Dynamics written by Chen Zhao and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: To effectively understand and treat complex diseases such as cancer, mathematical and statistical modeling is essential if one wants to represent and characterize the interactions among the different regulatory components that govern the underlying decision making process. Like in any other complex decision making networks, the regulatory power is not evenly distributed among its individual members, but rather concentrated in a few high power "commanders". In biology, such commanders are usually called masters or canalizing genes. Characterizing and detecting such genes are thus highly valuable for the treatment of cancer. Chapter II is devoted to this task, where we present a Bayesian framework to model pathway interactions and then study the behavior of master genes and canalizing genes. We also propose a hypothesis testing procedure to detect a "cut" in pathways, which is useful for discerning drugs' therapeutic effect. In Chapter III, we shift our focus to the understanding of the mechanisms of action (MOA) of cancer drugs. For a new drug, the correct understanding of its MOA is a key step for its application to cancer treatments. Using the Green Fluorescent Protein technology, researchers have been able to track various reporter genes from the same cell population for an extended period of time. Such dynamic gene expression data forms the basis for drug similarity comparisons. In Chapter III, we design an algorithm that can identify mechanistic similarities in drug responses, which leads to the characterization of their respective MOAs. Finally, in the course of drug MOA study, we observe that cells in a hypothetical homogeneous population do not respond to drug treatments in a uniform and synchronous way. Instead, each cell makes a large shift in its gene expression level independently and asynchronously from the others. Hence, to systematically study such behavior, we propose a mathematical model that describes the gene expression dynamics for a population of cells after drug treatments. The application of this model to dose response data provides us new insights of the dosing effects. Furthermore, the model is capable of generating useful hypotheses for future experimental design.
Book Synopsis Systems Modeling: Approaches and Applications - Volume II by : Alberto Jesus Martin
Download or read book Systems Modeling: Approaches and Applications - Volume II written by Alberto Jesus Martin and published by Frontiers Media SA. This book was released on 2022-11-25 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology by : David Holcman
Download or read book Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology written by David Holcman and published by Springer. This book was released on 2017-10-04 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the modeling and mathematical analysis of stochastic dynamical systems along with their simulations. The collected chapters will review fundamental and current topics and approaches to dynamical systems in cellular biology. This text aims to develop improved mathematical and computational methods with which to study biological processes. At the scale of a single cell, stochasticity becomes important due to low copy numbers of biological molecules, such as mRNA and proteins that take part in biochemical reactions driving cellular processes. When trying to describe such biological processes, the traditional deterministic models are often inadequate, precisely because of these low copy numbers. This book presents stochastic models, which are necessary to account for small particle numbers and extrinsic noise sources. The complexity of these models depend upon whether the biochemical reactions are diffusion-limited or reaction-limited. In the former case, one needs to adopt the framework of stochastic reaction-diffusion models, while in the latter, one can describe the processes by adopting the framework of Markov jump processes and stochastic differential equations. Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology will appeal to graduate students and researchers in the fields of applied mathematics, biophysics, and cellular biology.
Book Synopsis Quantitative Biosciences Companion in R by : Joshua S. Weitz
Download or read book Quantitative Biosciences Companion in R written by Joshua S. Weitz and published by Princeton University Press. This book was released on 2024-03-05 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on lab guide in the R programming language that enables students in the life sciences to reason quantitatively about living systems across scales This lab guide accompanies the textbook Quantitative Biosciences, providing students with the skills they need to translate biological principles and mathematical concepts into computational models of living systems. This hands-on guide uses a case study approach organized around central questions in the life sciences, introducing landmark advances in the field while teaching students—whether from the life sciences, physics, computational sciences, engineering, or mathematics—how to reason quantitatively in the face of uncertainty. Draws on real-world case studies in molecular and cellular biosciences, organismal behavior and physiology, and populations and ecological communities Encourages good coding practices, clear and understandable modeling, and accessible presentation of results Helps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scale Builds practical expertise in a range of methods, including sampling from probability distributions, stochastic branching processes, continuous time modeling, Markov chains, bifurcation analysis, partial differential equations, and agent-based simulations Bridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their own Stand-alone computational lab guides for Quantitative Biosciences also available in Python and MATLAB
Book Synopsis Stochastic Analysis of Gene Expression Noise by : Madeline Smith
Download or read book Stochastic Analysis of Gene Expression Noise written by Madeline Smith and published by . This book was released on 2021 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic variation in the level of gene product amongst cells of the same population is ubiquitous across cell types and organisms. The species involved in gene expression exist at low copy numbers which consequently amplifies the inherent probabilistic nature of the complex set of biochemical reactions associated with the gene expression process. Analytical expressions are developed to quantify noise in gene expression, allowing for systematic comparisons of different regimes and the role they play in noise attenuation. We incorporate stochastic time delays into gene expression models, where the delay is an independent and identically distributed random variable. After characterizing the effect of time delays, we further explore the effects of time delays when subject to extrinsic fluctuations. Here, we find counter-intuitive results in regards to the incorporation of an extrinsic factor. When it is incorporated, non-monotonic noise behavior emerges when the mRNA Fano factor is plotted as a function of increasing RNA transport time. We also find that for both low and high extrinsic factor timescale, gene expression noise is buffered, however, noise increases at mid-level extrinsic species timescale. The results in this thesis are obtained through moment dynamics and linear noise approximation to analyze noise at steady state. Next, we apply the analysis of gene expression noise to the specific context of the Human Immunodeficiency Virus (HIV). As previously mentioned, noise can have advantages and disadvantages for the cell. In the case of HIV, noise drives a key cell fate decision: between active replication and viral latency, known as a major obstacle in the eradication of HIV and HIV therapies. We study the bimodal distribution of protein level, corresponding to the alternate cell fate decisions, that result from three different circuits that incorporate feedback strategies. Using stochastic simulations from Gillespie algorithm, we ultimately reveal that Tat-mediated transcriptional positive feedback combined with precursor auto-depletion of nuclear RNA species results in the greatest stability in cell fate. Last, experimental data from live-cell imaging of gene switching in developmental enhancers is analyzed. Analysis of the active and inactive time intervals of the gene reveal memory in the time spent in the active state.
Book Synopsis Molecular Genetic Information Systems by : Klaus Bellmann
Download or read book Molecular Genetic Information Systems written by Klaus Bellmann and published by Walter de Gruyter GmbH & Co KG. This book was released on 1983-12-31 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Quantitative Biosciences Companion in MATLAB by : Joshua S. Weitz
Download or read book Quantitative Biosciences Companion in MATLAB written by Joshua S. Weitz and published by Princeton University Press. This book was released on 2024-03-05 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on lab guide in the MATLAB programming language that enables students in the life sciences to reason quantitatively about living systems across scales This lab guide accompanies the textbook Quantitative Biosciences, providing students with the skills they need to translate biological principles and mathematical concepts into computational models of living systems. This hands-on guide uses a case study approach organized around central questions in the life sciences, introducing landmark advances in the field while teaching students—whether from the life sciences, physics, computational sciences, engineering, or mathematics—how to reason quantitatively in the face of uncertainty. Draws on real-world case studies in molecular and cellular biosciences, organismal behavior and physiology, and populations and ecological communities Encourages good coding practices, clear and understandable modeling, and accessible presentation of results Helps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scale Builds practical expertise in a range of methods, including sampling from probability distributions, stochastic branching processes, continuous time modeling, Markov chains, bifurcation analysis, partial differential equations, and agent-based simulations Bridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their own Stand-alone computational lab guides for Quantitative Biosciences also available in Python and R
Book Synopsis Diffusion Models in Population Genetics by : Motoo Kimura
Download or read book Diffusion Models in Population Genetics written by Motoo Kimura and published by . This book was released on 1964 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Quantitative Biosciences Companion in Python by : Joshua S. Weitz
Download or read book Quantitative Biosciences Companion in Python written by Joshua S. Weitz and published by Princeton University Press. This book was released on 2024-01-09 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on lab guide in the Python programming language that enables students in the life sciences to reason quantitatively about living systems across scales This lab guide accompanies the textbook Quantitative Biosciences, providing students with the skills they need to translate biological principles and mathematical concepts into computational models of living systems. This hands-on guide uses a case study approach organized around central questions in the life sciences, introducing landmark advances in the field while teaching students—whether from the life sciences, physics, computational sciences, engineering, or mathematics—how to reason quantitatively in the face of uncertainty. Draws on real-world case studies in molecular and cellular biosciences, organismal behavior and physiology, and populations and ecological communities Encourages good coding practices, clear and understandable modeling, and accessible presentation of results Helps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scale Builds practical expertise in a range of methods, including sampling from probability distributions, stochastic branching processes, continuous time modeling, Markov chains, bifurcation analysis, partial differential equations, and agent-based simulations Bridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their own Stand-alone computational lab guides for Quantitative Biosciences also available in R and MATLAB
Book Synopsis Transactions on Computational Systems Biology XIII by : Ralph-Johan Back
Download or read book Transactions on Computational Systems Biology XIII written by Ralph-Johan Back and published by Springer Science & Business Media. This book was released on 2011-03-28 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers Computational Models for Cell Processes, featuring enhanced contributions from the CompMod workshop (2009). Covers a wide range of topics in systems biology, addressing the dynamics and the computational principles of this emerging field.
Book Synopsis Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology by : Paola Lecca
Download or read book Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology written by Paola Lecca and published by Elsevier. This book was released on 2013-04-09 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic kinetic methods are currently considered to be the most realistic and elegant means of representing and simulating the dynamics of biochemical and biological networks. Deterministic versus stochastic modelling in biochemistry and systems biology introduces and critically reviews the deterministic and stochastic foundations of biochemical kinetics, covering applied stochastic process theory for application in the field of modelling and simulation of biological processes at the molecular scale. Following an overview of deterministic chemical kinetics and the stochastic approach to biochemical kinetics, the book goes onto discuss the specifics of stochastic simulation algorithms, modelling in systems biology and the structure of biochemical models. Later chapters cover reaction-diffusion systems, and provide an analysis of the Kinfer and BlenX software systems. The final chapter looks at simulation of ecodynamics and food web dynamics. Introduces mathematical concepts and formalisms of deterministic and stochastic modelling through clear and simple examples Presents recently developed discrete stochastic formalisms for modelling biological systems and processes Describes and applies stochastic simulation algorithms to implement a stochastic formulation of biochemical and biological kinetics
Book Synopsis Statistical Analysis Techniques in Particle Physics by : Ilya Narsky
Download or read book Statistical Analysis Techniques in Particle Physics written by Ilya Narsky and published by John Wiley & Sons. This book was released on 2013-10-24 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students.
Book Synopsis Bio-inspired Information and Communication Technologies by : Adriana Compagnoni
Download or read book Bio-inspired Information and Communication Technologies written by Adriana Compagnoni and published by Springer. This book was released on 2019-07-23 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed conference proceedings of the 11th International Conference on Bio-Inspired Information and Communications Technologies, held in Pittsburgh, PA, USA, in March 2019. The 13 revised full papers and 2 short papers were selected from 29 submissions. Past iterations of the conference have attracted contributions in Direct Bioinspiration (physical biological materials and systems used within technology) as well as Indirect Bioinspiration (biological principles, processes and mechanisms used within the design and application of technology). This year, the scope has expanded to include a third thrust: Foundational Bioinspiration (bioinspired aspects of game theory, evolution, information theory, and philosophy of science).
Book Synopsis Quantitative Biosciences by : Joshua S. Weitz
Download or read book Quantitative Biosciences written by Joshua S. Weitz and published by Princeton University Press. This book was released on 2024-01-09 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on approach to quantitative reasoning in the life sciences Quantitative Biosciences establishes the quantitative principles of how living systems work across scales, drawing on classic and modern discoveries to present a case study approach that links mechanisms, models, and measurements. Each case study is organized around a central question in the life sciences: Are mutations dependent on selection? How do cells respond to fluctuating signals in the environment? How do organisms move in flocks given local sensing? How does the size of an epidemic depend on its initial speed of spread? Each question provides the basis for introducing landmark advances in the life sciences while teaching students—whether from the life sciences, physics, computational sciences, engineering, or mathematics—how to reason quantitatively about living systems given uncertainty. Draws on real-world case studies in molecular and cellular biosciences, organismal behavior and physiology, and populations and ecological communities Stand-alone lab guides available in Python, R, and MATLAB help students move from learning in the classroom to doing research in practice Homework exercises build on the lab guides, emphasizing computational model development and analysis rather than pencil-and-paper derivations Suitable for capstone undergraduate classes, foundational graduate classes, or as part of interdisciplinary courses for students from quantitative backgrounds Can be used as part of conventional, flipped, or hybrid instruction formats Additional materials available to instructors, including lesson plans and homework solutions
Book Synopsis An Introduction to Stochastic Modeling by : Howard M. Taylor
Download or read book An Introduction to Stochastic Modeling written by Howard M. Taylor and published by Academic Press. This book was released on 2014-05-10 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.
Book Synopsis Biomolecular Feedback Systems by : Domitilla Del Vecchio
Download or read book Biomolecular Feedback Systems written by Domitilla Del Vecchio and published by Princeton University Press. This book was released on 2014-10-26 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an accessible introduction to the principles and tools for modeling, analyzing, and synthesizing biomolecular systems. It begins with modeling tools such as reaction-rate equations, reduced-order models, stochastic models, and specific models of important core processes. It then describes in detail the control and dynamical systems tools used to analyze these models. These include tools for analyzing stability of equilibria, limit cycles, robustness, and parameter uncertainty. Modeling and analysis techniques are then applied to design examples from both natural systems and synthetic biomolecular circuits. In addition, this comprehensive book addresses the problem of modular composition of synthetic circuits, the tools for analyzing the extent of modularity, and the design techniques for ensuring modular behavior. It also looks at design trade-offs, focusing on perturbations due to noise and competition for shared cellular resources. Featuring numerous exercises and illustrations throughout, Biomolecular Feedback Systems is the ideal textbook for advanced undergraduates and graduate students. For researchers, it can also serve as a self-contained reference on the feedback control techniques that can be applied to biomolecular systems. Provides a user-friendly introduction to essential concepts, tools, and applications Covers the most commonly used modeling methods Addresses the modular design problem for biomolecular systems Uses design examples from both natural systems and synthetic circuits Solutions manual (available only to professors at press.princeton.edu) An online illustration package is available to professors at press.princeton.edu
