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 128 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.
Download or read book A Mathematical Modeling and Approximation of Gene Expression Patterns by Linear and Quadratic Regulatory Relations and Analysis of Gene Networks written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis mainly concerns modeling, approximation and inference of gene regulatory dynamics on the basis of gene expression patterns. The dynamical behavior of gene expressions is represented by a system of ordinary di erential equations. We introduce a gene-interaction matrix with some nonlinear entries, in particular, quadratic polynomials of the expression levels to keep the system solvable. The model parameters are determined by using optimization. Then, we provide the time-discrete approximation of our time-continuous model. We analyze the approximating model under the aspect of stability. Finally, from the considered models we derive gene regulatory networks, discuss their qualitative features of the networks and provide a basis for analyzing networks with nonlinear connections.
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 Data Driven Models for Dynamics of Gene Expression and Single Cells written by Tao Peng and published by . This book was released on 2017 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis uses mathematical models to study the dynamics of biological systems under the single cell level. In the first chapter we study a minimal gene regulatory network permissive of multi-lineage mesenchymal stem cell differentiation into four cell fates. We present a continuous model that is able to describe the cell fate transitions that occur during differentiation, and analyze its dynamics with tools from multistability, bifurcation, and cell fate landscape analysis, and via stochastic simulation. In the second chapter we adapt a classical self-organizing-map approach to single-cell gene expression data, such as those based on qPCR and RNA-seq. In this method, a cellular state map (CSM) is derived and employed to identify cellular states inherited in a population of measured single cells. Cells located in the same basin of the CSM are considered as in one cellular state while barriers between the basins provide information on transitions among the cellular states. Consequently, paths of cellular state transitions (e.g. differentiation) and a temporal ordering of the measured single cells are obtained. In the third chapter on the basis of the functional mapping assays of primary visual cortex, we conducted a quantitative assessment of both excitatory and inhibitory synaptic laminar connections to excitatory cells at single cell resolution, establishing precise layer-by-layer synaptic wiring diagrams of excitatory and inhibitory neurons in the visual cortex inferred by the mathematical model. In the fourth chapter we constructed a multi-scale mathematical model integrating the gene regulatory network and cell lineage to study the functions of key genes in controlling mouse embryonic epidermis development. In the fifth chapter we studied the selections of models when prior information is provided to infer the gene regulatory network combining the expression data and ChIP-seq data.
Download or read book Modeling the Dynamics of Gene Regulatory Networks written by Aparna Das and published by . This book was released on 2012 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to describe the dynamic behavior of gene regulatory networks different formalisms have been introduced. In this thesis, we describe first the discrete approach of René Thomas and piecewise linear differential equations approach. Then we proposed a correspondence result between the two approaches and based on it we proposed an automatic computational technique to understand the global behavior of such complex systems using MAPLE programming language. The proposed code provides a way to compute the trajectories of the discrete version of a gene regulatory network model given an initial condition, in the same way as usual numerical algorithms give the "true" solution of a differential model from an initial condition. Knowing a discrete trajectory is less precise than knowing a true trajectory but correspondence theorems shows the link between the two approaches. Hence, it is a mathematical tool for analysing gene regulatory networks models. Finally, we illustrate both discrete and piecewise linear approaches, theircorrespondence and the use of our Maple code on a specific example: a mathematical model of the circadian clock. Our first two presented 8 and 4 variables models are the simplification of a model proposed by Leloup and Goldbeter. We deliberately choose to push the simplicity of the model as far as possible, focusing only on a few biological behaviors of interest. The hope is to get nevertheless the essential abstract causalities that govern these behaviors.
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.
Download or read book Mathematical Modelling and Parameter Inference of Genetic Regulatory Networks written by Qianqian Wu and published by . This book was released on 2015 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical modelling opens the door to a rich pathway to study the dynamic properties of biological systems. Among the many biological systems that would benefit from mathematical modelling, improving our understanding of gene regulatory networks has received much attention from the fields of computational biology and bioinformatics. To understand system dynamics of biological networks, mathematical models need to be constructed and studied. In spite of the efforts that have been given to explore regulatory mechanisms among gene net- works, accurate description of chemical events with multi-step chemical reactions still remains a challenge in biochemistry and biophysics. This dissertation is aimed at developing several novel methods for describing dynamics of multi-step chemical reaction systems. The main idea is introduced by a new concept for the location of molecules in the multi-step reactions, which is used as an additional indicator of system dynamics. Additionally, novel idea in the stochastic simulation algorithm is used to calculate time delay exactly, which shows that the value of time delay depends on the system states. All of these innovations alter the focus of originally complex multi-step structures towards defining novel simplified structures, which simplifies the modelling process significantly. Research results yield substantially more accurate results than published methods.Apart from the well-established knowledge for modelling techniques, there are still significant challenges in understanding the dynamics of systems biology. One of the major challenges in systems biology is how to infer unknown parameters in mathematical models based on experimental datasets, in particular, when data are sparse and networks are stochastic. To tackle this challenge, parameters estimation techniques using Approximate Bayesian Computation (ABC) for chemical reaction system and inference method for dynamic network have been investigated. This dissertation discusses developed ABC methods that have been tested on two stochastic systems. Results on artificial data show certain promising approximations for the unknown parameters in the systems. While unknown parameters are difficult and sometimes even impossible to measure with biological experiments, instead we can study the influence of parameter variation on system properties. Robustness and sensitivity are two major measurements to describe the dynamic properties of a system against the variation of model parameters. For stochastic models of discrete chemical reaction systems, although these two properties have been studied separately, no work has been done so far to investigate these two properties together. In this dissertation, An integrated framework has been proposed to study these two properties for the Nanog gene network simultaneously. It successfully identifies key coefficients that have more impacts on the network dynamics than the others. The proposed inference method to infer dynamic protein-gene interactions is applied to a case study of the human P53 protein, which is a well-known biological network for cancer study. Investigating the dynamics for such regulatory networks through high throughput experimental data has become more popular. To tackle the hindrances with large number of unknown parameters when building detailed mathematical models, a new integrated method is proposed by combining a top-down approach using probability graphical models and a bottom-up approach using differential equation models. Model simulation error, Akaike's information criterion, parameter identifiability and robustness properties are used as criteria to select the optimal network. Results based on random permutations of input gene network structures provide accurate prediction and robustness property. In addition, a comparison study suggests that the proposed approach has better simulation accuracy and robustness property than the earlier one. In particular, the computational cost is significantly reduced. Overall, the new integrated method is a promising approach for investigating the dynamics of genetic regulations.
Download or read book Systems Biology written by Jinzhi Lei and published by . This book was released on 2021 with total page 0 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.
Download or read book Mathematical Models and Algorithms for Genetic Regulatory Networks written by Shuqin Zhang and published by Open Dissertation Press. This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Mathematical Models and Algorithms for Genetic Regulatory Networks" by Shuqin, Zhang, 張淑芹, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled MATHEMATICAL MODELS AND ALGORITHMS FOR GENETIC REGULATORY NETWORKS submitted by ZHANG Shu-Qin for the degree of Doctor of Philosophy at The University of Hong Kong in August 2007 Genetic regulatory network is an important research topic in bioinformat- ics, which considers the on-o(R) switches and rheostats of a cell operating at the gene level. Mathematical modeling and computation are indispensable in such studies, especially for the complex patterns of behavior which needs high indus- trialpayo(R)sandisdiculttogettheinformationthroughexperimentalmethods. Booleannetworks(BNs)andprobabilisticBooleannetworks(PBNs)areproposed to model the interactions among the genes and have received much attention in the biophysics community. The study in this thesis is based on the BN and PBN models. With the BN model, several algorithms using gene ordering and feedback vertex sets are devel- opedtoidentifysingletonattractorsandsmallattractorswhichcorrespondtocell types and cell states. The average case time complexities of some proposed al- gorithms are analyzed. Extensive computational experiments are also performed which are in good agreement with the theoretical results. A simple and complete proofforshowingthatndinganattractorwiththeshortestperiodisNP-hardis given. Finding global states incoming to a specied global state is useful for the preprocessingofndingasequenceofcontrolactionsinBooleannetworksandfor identifying the basin of attraction for a given attractor. This problem is shown to be NP-hard in general. New algorithms based on the algorithms for ndingsmall attractors are developed, which are much faster than the naive exhaustive search-based algorithm. Based on the PBN model, an ecient method for the construction of the sparse transition probability matrix is proposed. Power method is then applied to compute the steady-state probability distribution. With this method, the sensitivity of the steady-state distribution to the inuences of input genes, gene connections and Boolean functions is studied. Simulation results are given to illustrate the method and to demonstrate the steady-state analysis. An approxi- mation method is proposed to further reduce the time complexity for computing the steady-state probability distribution by neglecting some BNs with very small probabilities during the construction of the transition probability matrix. An error analysis of this approximation method is givenand theoretical result on the distribution of BNs in a PBN with at most two Boolean functions for one gene is also presented. Numerical experiments are given to demonstrate the eciency of the proposed method. The ultimate goal of studying the long-term behavior of the genetic regula- tory network is to study the control strategies such that the system can go into the desirable states with larger probabilities. A control model is also proposed for gene intervention here. The problem is formulated as a minimization prob- lem with integer variables to minimize the amount of control cost for a genetic network over a given period of time such that the probabilities of obtaining the target states are as large as possible. Experimental results show that the pro- posed formulation is ecient and e(R)ective for solving the control problem of gene intervention. DOI: 10.5353/th_b3884282 Subjects: Genetics - Mathematical models Algorithms Bioinformatics
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:
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.
Download or read book Probabilistic Boolean Networks written by Ilya Shmulevich and published by SIAM. This book was released on 2010-01-21 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.
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:
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.
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.
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 353 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.
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 104 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.
