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 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 Stochastic Computational Models for Gene Regulatory Networks and Dynamic Fault Tree Analysis written by Peican Zhu and published by . This book was released on 2015 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Originally proposed in the 1960s, stochastic computation uses random binary bit streams to encode signal probabilities. Stochastic computation enables the implementation of basic arithmetic functions using simple logic elements. Here, the application of stochastic computation is extended to the domain of gene network models and the fault-tree analysis of system reliability. Initially, context-sensitive stochastic Boolean networks (CSSBNs) are developed to model the effect of context sensitivity in a genetic network. A CSSBN allows for a tunable tradeoff between accuracy and efficiency in a simulation. Studies of a simple p53-Mdm2 network reveal that random gene perturbation has a greater effect on the steady state distribution (SSD) compared to context switching activities. Secondly, stochastic multiple-valued networks (SMNs) are investigated to evaluate the effect of noise in a WNT5A network. Lastly, asynchronous stochastic Boolean networks (ASBNs) are proposed for investigating various asynchronous state updating strategies in a gene regulatory network (GRN). The dynamic behavior of a T helper network is investigated and the SSDs found by using ASBNs show the robustness of attractors of the network. In a long term, these results may help to accelerate drug discovery and develop effective drug intervention strategies for some genetic diseases. As another application of stochastic computation, the reliability analysis of dynamic fault trees (DFTs) is further pursued. Stochastic computational models are proposed for the priority AND (PAND) gate, the spare gate and probabilistic common cause failures (PCCFs). Subsequently, a phased-mission system (PMS) is analyzed by using a DFT to model each phase's failure conditions. The accuracy of a stochastic analysis increases with the length of random binary bit streams in stochastic computation. In addition, non-exponential failure distributions and repeated events are readily handled by the stochastic computational approach. The accuracy, efficiency and scalability of the stochastic approach are demonstrated by several case studies of DFT analysis.
Download or read book Annealed Dynamics of Boolean Networks written by Pauli Rämö and published by . This book was released on 2006 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Emerging Research in the Analysis and Modeling of Gene Regulatory Networks written by Ivanov, Ivan V. and published by IGI Global. This book was released on 2016-06-06 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: While technological advancements have been critical in allowing researchers to obtain more and better quality data about cellular processes and signals, the design and practical application of computational models of genomic regulation continues to be a challenge. Emerging Research in the Analysis and Modeling of Gene Regulatory Networks presents a compilation of recent and emerging research topics addressing the design and use of technology in the study and simulation of genomic regulation. Exploring both theoretical and practical topics, this publication is an essential reference source for students, professionals, and researchers working in the fields of genomics, molecular biology, bioinformatics, and drug development.
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 Perturbations in Boolean Networks as Model of Gene Regulatory Dynamics written by Fakhteh Ghanbarnejad and published by . This book was released on 2012 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Genomic Signal Processing written by Ilya Shmulevich and published by Princeton University Press. This book was released on 2014-09-08 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Situated at the crossroads of engineering, biology, mathematics, statistics, and computer science, GSP requires the development of both nonlinear dynamical models that adequately represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing rigorous mathematical definitions and propositions for the main elements of GSP and by paying attention to the validity of models relative to the data. Ilya Shmulevich and Edward Dougherty cover real-world situations and explain their mathematical modeling in relation to systems biology and systems medicine. Genomic Signal Processing makes a major contribution to computational biology, systems biology, and translational genomics by providing a self-contained explanation of the fundamental mathematical issues facing researchers in four areas: classification, clustering, network modeling, and network intervention.
Download or read book Computational Modeling Of Gene Regulatory Networks A Primer written by Hamid Bolouri and published by World Scientific Publishing Company. This book was released on 2008-08-13 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./a
Download or read book Effects of External Fluctuations on the Stochastic Dynamics of Gene Regulatory Networks written by Abhishek Gupta and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
Download or read book Boolean Models for Genetic Regulatory Networks written by Yufei Xiao and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation attempts to answer some of the vital questions involved in the genetic regulatory networks: inference, optimization and robustness of the mathe- matical models. Network inference constitutes one of the central goals of genomic signal processing. When inferring rule-based Boolean models of genetic regulations, the same values of predictor genes can correspond to different values of the target gene because of inconsistencies in the data set. To resolve this issue, a consistency-based inference method is developed to model a probabilistic genetic regulatory network, which consists of a family of Boolean networks, each governed by a set of regulatory functions. The existence of alternative function outputs can be interpreted as the result of random switches between the constituent networks. This model focuses on the global behavior of genetic networks and reflects the biological determinism and stochasticity. When inferring a network from microarray data, it is often the case that the sample size is not sufficiently large to infer the network fully, such that it is neces- sary to perform model selection through an optimization procedure. To this end, the network connectivity and the physical realization of the regulatory rules should be taken into consideration. Two algorithms are developed for the purpose. One algo- rithm finds the minimal realization of the network constrained by the connectivity, and the other algorithm is mathematically proven to provide the minimally connected network constrained by the minimal realization. Genetic regulatory networks are subject to modeling uncertainties and perturbations, which brings the issue of robustness. From the perspective of network stability, robustness is desirable; however, from the perspective of intervention to exert influence on network behavior, it is undesirable. A theory is developed to study the impact of function perturbations in Boolean networks: It finds the exact number of affected state transitions and attractors, and predicts the new state transitions and robust/fragile attractors given a specific perturbation. Based on the theory, one algorithm is proposed to structurally alter the network to achieve a more favorable steady-state distribution, and the other is designed to identify function perturbations that have caused changes in the network behavior, respectively.
Download or read book Evolutionary Computation in Gene Regulatory Network Research written by Hitoshi Iba and published by John Wiley & Sons. This book was released on 2016-01-20 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. • Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) • Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications • Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology • Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.
Download or read book Information Processing and Biological Systems written by Samuli Niiranen and published by Springer Science & Business Media. This book was released on 2011-03-10 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Living beings require constant information processing for survival. In cells, information is being processed and propagated at various levels, from the gene regulatory network to chemical pathways, to the interaction with the environment. How this is achieved and how information is coded is still poorly understood. For example, what a cell interprets as information in the temporal level of an mRNA and what is interpreted as noise remains an open question. Recently, information theoretical methods and other tools, developed in the context of engineering and natural sciences, have been applied to study diverse biological processes. This book covers the latest findings on how information is processed in various biological processes, ranging from information processing and propagation in gene regulatory networks to information processing in natural language. An overview is presented of the state-of-the-art in information processing in biological systems and the opinion of current leaders in this research field on future research directions.
Download or read book Stochastic Modeling and Inference of Large scale Gene Regulatory Networks written by Haseong Kim and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene regulatory networks (GRNs) consist of thousands of genes and proteins which are dynamically interacting with each other. Researchers have investigated how to uncover these unknown interactions by observing expressions of biological molecules with various statistical/mathematical methods. Once these regulatory structures are revealed, it is necessary to understand their dynamical behaviors since pathway activities could be changed by their given conditions. Therefore, both the regulatory structure estimation and dynamics modeling of GRNs are essential for biological research. Generally, GRN dynamics are usually investigated via stochastic models since molecular interactions are basically discrete and stochastic processes. However, this stochastic nature requires heavy simulation time to find the steady-state solution of the GRNs where thousands of genes are involved. This large number of genes also causes difficulties such as dimensionality problem in estimating their regulatory structure. This thesis mainly focuses on developing methodologies for large-scale GRN analyses. It includes applications of a stochastic process theory called G-networks and a reverse engineering technique for large-scale GRNs. Additionally a series of bioinformatics techniques was applied in brain tumor data to detect disease candidate genes along with their large-scale GRNs. The proposed techniques such as stochastic modeling (bottom-up) and reverse engineering (top-down) could provide a systematic view of a complex system and an efficient guideline to identify candidate genes or pathways triggering a specific phenotype of a cell. As further work, the combinatorial use of the modeling and reverse engineering approaches would be helpful in obtaining a reliable mathematical model and even in developing a synthetic biological system.
Download or read book Handbook on Biological Networks written by Stefano Boccaletti and published by World Scientific. This book was released on 2010 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networked systems are all around us. The accumulated evidence of systems as complex as a cell cannot be fully understood by studying only their isolated constituents, giving rise to a new area of interest in research OCo the study of complex networks . In a broad sense, biological networks have been one of the most studied networks, and the field has benefited from many important contributions. By understanding and modeling the structure of a biological network, a better perception of its dynamical and functional behavior is to be expected. This unique book compiles the most relevant results and novel insights provided by network theory in the biological sciences, ranging from the structure and dynamics of the brain to cellular and protein networks and to population-level biology. Sample Chapter(s). Chapter 1: Introduction (61 KB). Contents: Networks at the Cellular Level: The Structural Network Properties of Biological Systems (M Brilli & P Li); Dynamics of Multicellular Synthetic Gene Networks (E Ullner et al.); Boolean Networks in Inference and Dynamic Modeling of Biological Systems at the Molecular and Physiological Level (J Thakar & R Albert); Complexity of Boolean Dynamics in Simple Models of Signaling Networks and in Real Genetic Networks (A D az-Guilera & R ulvarez-Buylla); Geometry and Topology of Folding Landscapes (L Bongini & L Casetti); Elastic Network Models for Biomolecular Dynamics: Theory and Application to Membrane Proteins and Viruses (T R Lezon et al.); Metabolic Networks (M C Palumbo et al.); Brain Networks: The Human Brain Network (O Sporns); Brain Network Analysis from High-Resolution EEG Signals (F De Vico Fallani & F Babiloni); An Optimization Approach to the Structure of the Neuronal layout of C elegans (A Arenas et al.); Cultured Neuronal Networks Express Complex Patterns of Activity and Morphological Memory (N Raichman et al.); Synchrony and Precise Timing in Complex Neural Networks (R-M Memmesheimer & M Timme); Networks at the Individual and Population Levels: Ideas for Moving Beyond Structure to Dynamics of Ecological Networks (D B Stouffer et al.); Evolutionary Models for Simple Biosystems (F Bagnoli); Evolution of Cooperation in Adaptive Social Networks (S Van Segbroeck et al.); From Animal Collectives and Complex Networks to Decentralized Motion Control Strategies (A Buscarino et al.); Interplay of Network State and Topology in Epidemic Dynamics (T Gross). Readership: Advanced undergraduates, graduate students and researchers interested in the study of complex networks in a wide range of biological processes and systems."
Download or read book Analysis and Control of Boolean Networks written by Daizhan Cheng and published by Springer Science & Business Media. This book was released on 2010-11-23 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis and Control of Boolean Networks presents a systematic new approach to the investigation of Boolean control networks. The fundamental tool in this approach is a novel matrix product called the semi-tensor product (STP). Using the STP, a logical function can be expressed as a conventional discrete-time linear system. In the light of this linear expression, certain major issues concerning Boolean network topology – fixed points, cycles, transient times and basins of attractors – can be easily revealed by a set of formulae. This framework renders the state-space approach to dynamic control systems applicable to Boolean control networks. The bilinear-systemic representation of a Boolean control network makes it possible to investigate basic control problems including controllability, observability, stabilization, disturbance decoupling etc.
