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Simple Mathematical Models Of Gene Regulatory Dynamics
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Book Synopsis Simple Mathematical Models of Gene Regulatory Dynamics by : Michael C. Mackey
Download or read book Simple Mathematical Models of Gene Regulatory Dynamics written by Michael C. Mackey and published by Springer. This book was released on 2016-11-09 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a short and self-contained introduction to the field of mathematical modeling of gene-networks in bacteria. As an entry point to the field, we focus on the analysis of simple gene-network dynamics. The notes commence with an introduction to the deterministic modeling of gene-networks, with extensive reference to applicable results coming from dynamical systems theory. The second part of the notes treats extensively several approaches to the study of gene-network dynamics in the presence of noise—either arising from low numbers of molecules involved, or due to noise external to the regulatory process. The third and final part of the notes gives a detailed treatment of three well studied and concrete examples of gene-network dynamics by considering the lactose operon, the tryptophan operon, and the lysis-lysogeny switch. The notes contain an index for easy location of particular topics as well as an extensive bibliography of the current literature. The target audience of these notes are mainly graduates students and young researchers with a solid mathematical background (calculus, ordinary differential equations, and probability theory at a minimum), as well as with basic notions of biochemistry, cell biology, and molecular biology. They are meant to serve as a readable and brief entry point into a field that is currently highly active, and will allow the reader to grasp the current state of research and so prepare them for defining and tackling new research problems.
Book Synopsis Probabilistic Boolean Networks by : Ilya Shmulevich
Download or read book Probabilistic Boolean Networks written by Ilya Shmulevich and published by SIAM. This book was released on 2010-01-01 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first comprehensive treatment of probabilistic Boolean networks (PBNs), an important model class for studying genetic regulatory networks. This book covers basic model properties, including the relationships between network structure and dynamics, steady-state analysis, and relationships to other model classes." "Researchers in mathematics, computer science, and engineering are exposed to important applications in systems biology and presented with ample opportunities for developing new approaches and methods. The book is also appropriate for advanced undergraduates, graduate students, and scientists working in the fields of computational biology, genomic signal processing, control and systems theory, and computer science.
Book Synopsis Modeling with Nonsmooth Dynamics by : Mike R. Jeffrey
Download or read book Modeling with Nonsmooth Dynamics written by Mike R. Jeffrey and published by Springer Nature. This book was released on 2020-02-22 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume looks at the study of dynamical systems with discontinuities. Discontinuities arise when systems are subject to switches, decisions, or other abrupt changes in their underlying properties that require a ‘non-smooth’ definition. A review of current ideas and introduction to key methods is given, with a view to opening discussion of a major open problem in our fundamental understanding of what nonsmooth models are. What does a nonsmooth model represent: an approximation, a toy model, a sophisticated qualitative capturing of empirical law, or a mere abstraction? Tackling this question means confronting rarely discussed indeterminacies and ambiguities in how we define, simulate, and solve nonsmooth models. The author illustrates these with simple examples based on genetic regulation and investment games, and proposes precise mathematical tools to tackle them. The volume is aimed at students and researchers who have some experience of dynamical systems, whether as a modelling tool or studying theoretically. Pointing to a range of theoretical and applied literature, the author introduces the key ideas needed to tackle nonsmooth models, but also shows the gaps in understanding that all researchers should be bearing in mind. Mike Jeffrey is a researcher and lecturer at the University of Bristol with a background in mathematical physics, specializing in dynamics, singularities, and asymptotics.
Download or read book Systems Biology written by Jinzhi Lei and published by Springer Nature. This book was released on 2021-05-13 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the mathematical simulation of biological systems, with a focus on the modeling of gene expression, gene regulatory networks and stem cell regeneration. The diffusion of morphogens is addressed by introducing various reaction-diffusion equations based on different hypotheses concerning the process of morphogen gradient formation. The robustness of steady-state gradients is also covered through boundary value problems. The introduction gives an overview of the relevant biological concepts (cells, DNA, organism development) and provides the requisite mathematical preliminaries on continuous dynamics and stochastic modeling. A basic understanding of calculus is assumed. The techniques described in this book encompass a wide range of mechanisms, from molecular behavior to population dynamics, and the inclusion of recent developments in the literature together with first-hand results make it an ideal reference for both new students and experienced researchers in the field of systems biology and applied mathematics.
Book Synopsis Computational Modeling of Gene Regulatory Networks by : Hamid Bolouri
Download or read book Computational Modeling of Gene Regulatory Networks written by Hamid Bolouri and published by Imperial College Press. This book was released on 2008 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. Mathematical jargon is avoided and explanations are given in intuitive terms. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an intuitive description is provided. Extensive examples and a large number of model descriptions are provided for use in both classroom exercises as well as self-guided exploration and learning. As such, the book is ideal for self-learning and also as the basis of a semester-long course for undergraduate and graduate students in molecular biology, bioengineering, genome sciences, or systems biology.
Book Synopsis Topology and Dynamics of an Artificial Genetic Regulatory Network Model by : P. Dwight Kuo
Download or read book Topology and Dynamics of an Artificial Genetic Regulatory Network Model written by P. Dwight Kuo and published by . This book was released on 2005 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays by : Mehmet Eren Ahsen
Download or read book Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays written by Mehmet Eren Ahsen and published by Birkhäuser. This book was released on 2015-02-25 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then reviewed, in particular Hill functions and Schwarzian derivatives. One chapter is devoted to the analysis of GRNs under negative feedback with time delays and a special case of a homogenous GRN is considered. Asymptotic stability analysis of GRNs under positive feedback is then considered in a separate chapter, in which conditions leading to bi-stability are derived. Graduate and advanced undergraduate students and researchers in control engineering, applied mathematics, systems biology and synthetic biology will find this brief to be a clear and concise introduction to the modeling and analysis of GRNs.
Book Synopsis Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics by : Rubem P. Mondaini
Download or read book Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics written by Rubem P. Mondaini and published by Springer Nature. This book was released on 2023-07-24 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume gathers together selected peer-reviewed works presented at the BIOMAT 2022 International Symposium, which was virtually held on November 7-11, 2022, with an organization staff based in Rio de Janeiro, Brazil. Topics touched on in this volume include infection spread in a population described by an agent-based approach; the study of gene essentiality via network-based computational modeling; stochastic models of neuronal dynamics; and the modeling of a statistical distribution of amino acids in protein domain families. The reader will also find texts in epidemic models with dynamic social distancing; with no vertical transmission; and with general incidence rates. Aspects of COVID-19 dynamics: the use of an SEIR model to analyze its spread in Brazil; the age-dependent manner of modeling its spread pattern; the impact of media awareness programs; and a web-based computational tool for Non-invasive hemodynamics evaluation of coronary stenosis are also covered. Held every year since 2001, The BIOMAT International Symposium gathers together, in a single conference, researchers from Mathematics, Physics, Biology, and affine fields to promote the interdisciplinary exchange of results, ideas and techniques, promoting truly international cooperation for problem discussion. BIOMAT volumes published from 2017 to 2021 are also available by Springer.
Book Synopsis Mathematical Modelling & Computing in Biology and Medicine by : V. Capasso (Ed)
Download or read book Mathematical Modelling & Computing in Biology and Medicine written by V. Capasso (Ed) and published by Società Editrice Esculapio. This book was released on 2003 with total page 659 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Identification, Analysis and Control of Discrete and Continuous Models of Gene Regulation Networks by : Christian Breindl
Download or read book Identification, Analysis and Control of Discrete and Continuous Models of Gene Regulation Networks written by Christian Breindl and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A systems biological approach towards cellular networks promises a better understanding of how these systems work. The development of mathematical models is however inherently complicated, as the involved molecules and their interactions are mostly difficult to measure. Focusing on gene regulation networks, this work therefore intends to provide systems theoretic tools that support the process of model development and analysis in presence of such incomplete knowledge. The contributions are threefold. First, the problem of identifying interconnections between genes from noisy data is addressed. Existing solutions formulated in a discrete framework are reviewed and simplified significantly with the help of tools from convex optimization theory. Second, a novel method for model verification and discrimination is introduced. It is based on concepts from robust control theory and allows to quantify the capability of a model to reproduce experimentally observed stationary behaviors. As the proposed formalism only requires a vague knowledge about the interactions between the molecules, the method is intended to test and compare early modeling hypotheses. Third, the problem of controlling gene regulation networks in presence of qualitative information only is studied. Methods from discrete event systems theory are adapted to obtain stimulation strategies that will steer the network toward a desired attractor. The benefits of all contributions are illustrated with examples in the individual chapters.
Book Synopsis Boolean Networks as Predictive Models of Emergent Biological Behaviors by : Jordan C. Rozum
Download or read book Boolean Networks as Predictive Models of Emergent Biological Behaviors written by Jordan C. Rozum and published by Cambridge University Press. This book was released on 2024-03-28 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions – from molecules in gene regulatory networks to species in ecological networks – and the often-incomplete state of system knowledge, such as the unknown values of kinetic parameters for biochemical reactions. Boolean networks have emerged as a powerful tool for modeling these systems. This Element provides a methodological overview of Boolean network models of biological systems. After a brief introduction, the authors describe the process of building, analyzing, and validating a Boolean model. They then present the use of the model to make predictions about the system's response to perturbations and about how to control its behavior. The Element emphasizes the interplay between structural and dynamical properties of Boolean networks and illustrates them in three case studies from disparate levels of biological organization.
Book Synopsis Mathematical Modeling in Systems Biology by : Brian P. Ingalls
Download or read book Mathematical Modeling in Systems Biology written by Brian P. Ingalls and published by MIT Press. This book was released on 2022-06-07 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the mathematical concepts and techniques needed for the construction and analysis of models in molecular systems biology. Systems techniques are integral to current research in molecular cell biology, and system-level investigations are often accompanied by mathematical models. These models serve as working hypotheses: they help us to understand and predict the behavior of complex systems. This book offers an introduction to mathematical concepts and techniques needed for the construction and interpretation of models in molecular systems biology. It is accessible to upper-level undergraduate or graduate students in life science or engineering who have some familiarity with calculus, and will be a useful reference for researchers at all levels. The first four chapters cover the basics of mathematical modeling in molecular systems biology. The last four chapters address specific biological domains, treating modeling of metabolic networks, of signal transduction pathways, of gene regulatory networks, and of electrophysiology and neuronal action potentials. Chapters 3–8 end with optional sections that address more specialized modeling topics. Exercises, solvable with pen-and-paper calculations, appear throughout the text to encourage interaction with the mathematical techniques. More involved end-of-chapter problem sets require computational software. Appendixes provide a review of basic concepts of molecular biology, additional mathematical background material, and tutorials for two computational software packages (XPPAUT and MATLAB) that can be used for model simulation and analysis.
Book Synopsis Stochastic Dynamics for Systems Biology by : Christian Mazza
Download or read book Stochastic Dynamics for Systems Biology written by Christian Mazza and published by CRC Press. This book was released on 2016-04-19 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Dynamics for Systems Biology is one of the first books to provide a systematic study of the many stochastic models used in systems biology. The book shows how the mathematical models are used as technical tools for simulating biological processes and how the models lead to conceptual insights on the functioning of the cellular processing
Book Synopsis Handbook of Research on Computational Methodologies in Gene Regulatory Networks by : Das, Sanjoy
Download or read book Handbook of Research on Computational Methodologies in Gene Regulatory Networks written by Das, Sanjoy and published by IGI Global. This book was released on 2009-10-31 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book focuses on methods widely used in modeling gene networks including structure discovery, learning, and optimization"--Provided by publisher.
Book Synopsis Dynamic Models in Biology by : Stephen P. Ellner
Download or read book Dynamic Models in Biology written by Stephen P. Ellner and published by Princeton University Press. This book was released on 2011-09-19 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: From controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universities are starting undergraduate programs in computational biology to introduce students to this rapidly growing field. In Dynamic Models in Biology, the first text on dynamic models specifically written for undergraduate students in the biological sciences, ecologist Stephen Ellner and mathematician John Guckenheimer teach students how to understand, build, and use dynamic models in biology. Developed from a course taught by Ellner and Guckenheimer at Cornell University, the book is organized around biological applications, with mathematics and computing developed through case studies at the molecular, cellular, and population levels. The authors cover both simple analytic models--the sort usually found in mathematical biology texts--and the complex computational models now used by both biologists and mathematicians. Linked to a Web site with computer-lab materials and exercises, Dynamic Models in Biology is a major new introduction to dynamic models for students in the biological sciences, mathematics, and engineering.
Download or read book Models of Life written by Kim Sneppen and published by Cambridge University Press. This book was released on 2014-10-02 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Reflecting the major advances that have been made in the field over the past decade, this book provides an overview of current models of biological systems. The focus is on simple quantitative models, highlighting their role in enhancing our understanding of the strategies of gene regulation and dynamics of information transfer along signalling pathways, as well as in unravelling the interplay between function and evolution. The chapters are self-contained, each describing key methods for studying the quantitative aspects of life through the use of physical models. They focus, in particular, on connecting the dynamics of proteins and DNA with strategic decisions on the larger scale of a living cell, using E. coli and phage lambda as key examples. Encompassing fields such as quantitative molecular biology, systems biology and biophysics, this book will be a valuable tool for students from both biological and physical science backgrounds.
Book Synopsis Model Reduction Using Multiple Time Scales in Stochastic Gene Regulatory Networks by :
Download or read book Model Reduction Using Multiple Time Scales in Stochastic Gene Regulatory Networks written by and published by . This book was released on 2006 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene network dynamics often involves processes that take place on widely differing time scales -- from the order of nanoseconds to the order of several days. Multiple time scales in mathematical models often lead to serious computational difficulties, such as numerical stiffness in the case of differential equations or excessively redundant Monte Carlo simulations in the case of stochastic processes. We present a method that takes advantage of multiple time scales and dramatically reduces the computational time for a broad class of problems arising in stochastic gene regulatory networks. We illustrate the efficiency of our method in two gene network examples, which describe two substantially different biological processes -- cellular heat shock response and expression of the pap gene in Escherichia coli bacteria.
