Download or read book Cell Population Balance Modeling of Gene Regulatory Network Dynamics written by Nikos Manzaris and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multiscale Modeling of Cell Populations and Intracellular Gene Regulatory Network written by Yuyu Luke Peng and published by . This book was released on 2011 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation systematically studies the multiscale cell population balance model's development and evolution. Most importantly, it looks in depth at the applications of the model in terms of intracellular gene network regulations and cell mass control. First the analytical solution of the two dimensional system is derived. The WENO (Weighted Essentially Non-Oscillatory) scheme is adapted to approximate the flux terms in the model and combined with 3rd order Total Variation Diminishing Runge-Kutta for time evolution to provide numerically stable and robust solutions with 3rd order of accuracy. A threshold dependent differentiation model is developed to investigate the roles of critical genes such as c-Myc, Ovol1 and Ovol2 in controlling the differentiation and proliferation of cells in the epidermal system. Multiscale cell population models of intracellular gene network constructed from hyperbolic PDEs of up to three dimensions are studied thoroughly. By comparing the effects of periodic, single positive feedback and double negative feedbacks on maintaining stable cell populations and homeostasis of the system, we can show the important role of various gene regulations. We demonstrate that robust size regulation can be achieved through various means such as a population level signal's regulation and cell mass control. Hysteresis in the gene network and the dynamics of growth and differentiation in the cell populations are also investigated. Furthermore, multiscale modeling of cell cycle control reveals that steady states of the system such as the population distribution are sensitive to the competition and balance of the cell cycle and the intercellular gene network. Also, it is demonstrated that bistability is neither a sufficient nor necessary condition for the population to exhibit a bimodal distribution. These results can hopefully provide insight into the cause of uncontrolled proliferation and diseases including cancer. Combined with the experimental findings, we use the model to fill in gaps in our current biological knowledge and to provide an integrative view of the system.
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 Modeling the Gene Regulatory Dynamics in Neural Differentiation with Single Cell Data Using a Machine Learning Approach written by Yixing Hu and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Cellular differentiation is an important process where progenitor cells progressively develop into mature cells with specialized functions. Understanding the molecular characteristics and underlying regulatory mechanisms of cell fate is a central goal in biological research. Advances in single-cell sequencing technology enable the exploration of cellular differentiation at unprecedented resolution. In this thesis, I focus on characterizing and modeling of cellular differentiation using machine learning approaches. First, I present a random forest approach to identify the most discriminant genes for different cell populations in the developing brain. This method was able to identify key gene markers that revealed dorsal-ventral patterning in a heterogeneous class of progenitors present in a mouse developmental time-series dataset. Next, as cellular differentiation is marked by continuous changes in gene expression and is not well described by static cell populations, I present a framework to model the dynamics of cell fate decisions based on ordinary differential equations (ODE). I train this model on previously reported trajectory data for neural differentiation, and show that the model is able to interpolate and predict the gene expression dynamics across unobserved regions in this trajectory and extend the system dynamics for neural differentiation data. Finally, by training the model on datasets that contain rate of change information for each gene (RNA velocity), I demonstrate that the model has the capacity to predict the effects of gene deletions to the cell's overall gene expression profile with a prediction accuracy of 90%. In summary, the Neural ODE method has the ability to learn the gene regulatory dynamics from single cell data and predict the dynamics of individual genes as well as perturbation response"--
Download or read book Models of Cellular Regulation written by Baltazar Aguda and published by OUP Oxford. This book was released on 2008-07-31 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: The human genome of three billion letters has been sequenced. So have the genomes of thousands of other organisms. With unprecedented resolution, modern technologies are allowing us to peek into the world of genes, biomolecules, and cells - and flooding us with data of immense complexity that we are just barely beginning to understand. A huge gap separates our knowledge of the components of a cell and what is known from our observations of its physiology. The authors have written this graduate textbook to explore what has been done to close this gap of understanding between the realms of molecules and biological processes. They have gathered together illustrative mechanisms and models of gene regulatory networks, DNA replication, the cell cycle, cell death, differentiation, cell senescence, and the abnormal state of cancer cells. The mechanisms are biomolecular in detail, and the models are mathematical in nature. The interdisciplinary presentation will be of interest to both biologists and mathematicians, and every discipline in between.
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 A mathematical modeling framework to simulate and analyze cell type transitions written by Daniella Schittler and published by Logos Verlag Berlin GmbH. This book was released on 2015-03-20 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: The quantitative understanding of changes in cell types, referred to as cell type transitions, is fundamental to advance fields such as stem cell research, immunology, and cancer therapies. This thesis provides a mathematical modeling framework to simulate and analyze cell type transitions. The novel methodological approaches and models presented here address diverse levels which are essential in this context: Gene regulatory network models represent the cell type-determining gene expression dynamics. Here, a novel construction method for gene regulatory network models is introduced, which allows to transfer results from generic low-dimensional to realistic high-dimensional gene regulatory network models. For populations of cells, a generalized model class is proposed that accounts for multiple cell types, division numbers, and the full label distribution. Analysis and solution methods are presented for this new model class, which cover common cell population experiments and allow to exploit the full information from data. The modeling and analysis methods presented here connect formerly isolated approaches, and thereby contribute to a holistic framework for the quantitative understanding of cell type transitions.
Download or read book Population Dynamics of Gene Regulatory Networks written by Silvia Behar and published by . This book was released on 2006 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book On the Dynamics of Boolean Gene Regulatory Networks with Stochasticity written by Yuezhe Li and published by . This book was released on 2016 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genes are responsible for producing proteins that are essential to the construction of complex biological systems. The mechanisms by which this production is regulated have long been the center of wide spread research efforts. Deterministic Boolean gene regulatory models have been a particularly effective avenue of research in this field. However these models fall short of accounting for variations in the gene functionality due to the uncertain internal or external environmental conditions. One of the recent attempts to overcome this weakness is by (Murrugarra, 2012), in which a probabilistic component is introduced as the fixed activation/degradation propensities at the cellular level. This approach still falls short of accounting for cell-to-cell variability as well as the variability at the molecular level. With this study we introduce an additional stochastic element by modeling the activation/degradation propensities using statistical distributions. This in turn allows us to quantify the variability of the different connections within the dynamical system formed by the gene activation/degradation behavior. Finally we present a converse method of determining the most likely propensity set for a given stochastic gene regulatory network.
Download or read book Modeling Biological Systems from Heterogeneous Data written by Allister P. Bernard and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decades have seen rapid development of numerous high-throughput technologies to observe biomolecular phenomena. High-throughput biological data are inherently heterogeneous, providing information at the various levels at which organisms integrate inputs to arrive at an observable phenotype. Approaches are needed to not only analyze heterogeneous biological data, but also model the complex experimental observation procedures. We first present an algorithm for learning dynamic cell cycle transcriptional regulatory networks from gene expression and transcription factor binding data. We learn regulatory networks using dynamic Bayesian network inference algorithms that combine evidence from gene expression data through the likelihood and evidence from binding data through an informative structure prior. We next demonstrate how analysis of cell cycle measurements like gene expression data are obstructed by sychrony loss in synchronized cell populations. Due to synchrony loss, population-level cell cycle measurements are convolutions of the true measurements that would have been observed when monitoring individual cells. We introduce a fully parametric, probabilistic model, CLOCCS, capable of characterizing multiple sources of asynchrony in synchronized cell populations. Using CLOCCS, we formulate a constrained convex optimization deconvolution algorithm that recovers single cell estimates from observed population-level measurements. Our algorithm offers a solution for monitoring individual cells rather than a population of cells that lose synchrony over time. Using our deconvolution algorithm, we provide a global high resolution view of cell cycle gene expression in budding yeast, right from an initial cell progressing through its cell cycle, to across the newly created mother and daughter cell. Proteins, and not gene expression, are responsible for all cellular functions, and we need to understand how proteins and protein complexes operate. We introduce PROCTOR, a statistical approach capable of learning the hidden interaction topology of protein complexes from direct protein-protein interaction data and indirect co-complexed protein interaction data. We provide a global view of the budding yeast interactome depicting how proteins interact with each other via their interfaces to form macromolecular complexes. We conclude by demonstrating how our algorithms, utilizing information from heterogeneous biological data, can provide a dynamic view of regulatory control in the budding yeast cell cycle.
Download or read book Dynamics and Control of Process Systems 2004 written by Sirish Shah and published by Elsevier. This book was released on 2005-06-10 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Systems Biology in Practice written by Edda Klipp and published by John Wiley & Sons. This book was released on 2008-07-15 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presenting the main concepts, this book leads students as well as advanced researchers from different disciplines to an understanding of current ideas in the complex field of comprehensive experimental investigation of biological objects, analysis of data, development of models, simulation, and hypothesis generation. It provides readers with guidance on how a specific complex biological question may be tackled: - How to formulate questions that can be answered - Which experiments to perform - Where to find information in databases and on the Internet - What kinds of models are appropriate - How to use simulation tools - What can be learned from the comparison of experimental data and modeling results - How to make testable predictions. The authors demonstrate how mathematical concepts can illuminate the principles underlying biology at a genetic, molecular, cellular and even organism level, and how to use mathematical tools for analysis and prediction.
Download or read book Genomic Regulatory Systems written by Eric H. Davidson and published by Elsevier. This book was released on 2001-01-24 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: The interaction between biology and evolution has been the subject of great interest in recent years. Because evolution is such a highly debated topic, a biologically oriented discussion will appeal not only to scientists and biologists but also to the interested lay person. This topic will always be a subject of controversy and therefore any breaking information regarding it is of great interest.The author is a recognized expert in the field of developmental biology and has been instrumental in elucidating the relationship between biology and evolution. The study of evolution is of interest to many different kinds of people and Genomic Regulatory Systems: In Development and Evolution is written at a level that is very easy to read and understand even for the nonscientist. * Contents Include* Regulatory Hardwiring: A Brief Overview of the Genomic Control Apparatus and Its Causal Role in Development and Evolution * Inside the Cis-Regulatory Module: Control Logic and How the Regulatory Environment Is Transduced into Spatial Patterns of Gene Expression* Regulation of Direct Cell-Type Specification in Early Development* The Secret of the Bilaterians: Abstract Regulatory Design in Building Adult Body Parts* Changes That Make New Forms: Gene Regulatory Systems and the Evolution of Body Plans
Download or read book Plant Systems Biology written by Sacha Baginsky and published by Springer Science & Business Media. This book was released on 2007-06-25 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume aims to provide a timely view of the state-of-the-art in systems biology. The editors take the opportunity to define systems biology as they and the contributing authors see it, and this will lay the groundwork for future studies. The volume is well-suited to both students and researchers interested in the methods of systems biology. Although the focus is on plant systems biology, the proposed material could be suitably applied to any organism.
Download or read book Logical Modeling of Cellular Processes From Software Development to Network Dynamics written by Matteo Barberis and published by Frontiers Media SA. This book was released on 2019-08-16 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical models have become invaluable tools for understanding the intricate dynamic behavior of complex biochemical and biological systems. Among computational strategies, logical modeling has been recently gaining interest as an alternative approach to address network dynamics. Due to its advantages, including scalability and independence of kinetic parameters, the logical modeling framework is becoming increasingly popular to study the dynamics of highly interconnected systems, such as cell cycle progression, T cell differentiation and gene regulation. Novel tools and standards have been developed to increase the interoperability of logical models, which can now be employ to respond a variety of biological questions. This Research Topic brings together the most recent and cutting-edge approaches in the area of logical modeling including, among others, novel biological applications, software development and model analysis techniques.
Download or read book Population Balances written by Doraiswami Ramkrishna and published by Elsevier. This book was released on 2000-08-08 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: Engineers encounter particles in a variety of systems. The particles are either naturally present or engineered into these systems. In either case these particles often significantly affect the behavior of such systems. This book provides a framework for analyzing these dispersed phase systems and describes how to synthesize the behavior of the population particles and their environment from the behavior of single particles in their local environments. Population balances are of key relevance to a very diverse group of scientists, including astrophysicists, high-energy physicists, geophysicists, colloid chemists, biophysicists, materials scientists, chemical engineers, and meteorologists. Chemical engineers have put population balances to most use, with applications in the areas of crystallization; gas-liquid, liquid-liquid, and solid-liquid dispersions; liquid membrane systems; fluidized bed reactors; aerosol reactors; and microbial cultures. Ramkrishna provides a clear and general treatment of population balances with emphasis on their wide range of applicability. New insight into population balance models incorporating random particle growth, dynamic morphological structure, and complex multivariate formulations with a clear exposition of their mathematical derivation is presented. Population Balances provides the only available treatment of the solution of inverse problems essential for identification of population balance models for breakage and aggregation processes, particle nucleation, growth processes, and more. This book is especially useful for process engineers interested in the simulation and control of particulate systems. Additionally, comprehensive treatment of the stochastic formulation of small systems provides for the modeling of stochastic systems with promising new areas of applications such as the design of sterilization systems and radiation treatment of cancerous tumors. A clear and general treatment of population balances with emphasis on their wide range of applicability. Thus all processes involving solid-fluid and liquid-liquid dispersions, biological populations, etc. are encompassed Provides new insight into population balance models incorporating random particle growth, dynamic morphological structure, and complex multivariate formulations with a clear exposition of their mathematical derivation Presents a wide range of solution techniques, Monte Carlo simulation methods with a lucid exposition of their origin and scope for enhancing computational efficiency An account of self-similar solutions of population balance equations and their significance to the treatment of data on particulate systems The only available treatment of the solution of inverse problems essential for identification of population balance models for breakage and aggregation processes, particle nucleation and growth processes and so on A comprehensive treatment of the stochastic formulation of small systems with several new applications
Download or read book Modeling Transcriptional Regulation written by SHAHID MUKHTAR and published by Humana. This book was released on 2022-07-27 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides methods and techniques used in construction of global transcriptional regulatory networks in diverse systems, various layers of gene regulation and mathematical as well as computational modeling of transcriptional gene regulation. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Modeling Transcriptional Regulation: Methods and Protocols aims to provide an in depth understanding of new techniques in transcriptional gene regulation for specialized audience.
