Probabilistic Boolean Networks
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Probabilistic Boolean Networks
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Author : Ilya Shmulevich
language : en
Publisher: SIAM
Release Date : 2010-01-01
Probabilistic Boolean Networks written by Ilya Shmulevich and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-01-01 with Mathematics categories.
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.
On Construction And Control Of Probabilistic Boolean Networks
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Author : XI Chen, (Ch
language : en
Publisher: Open Dissertation Press
Release Date : 2017-01-26
On Construction And Control Of Probabilistic Boolean Networks written by XI Chen, (Ch and has been published by Open Dissertation Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-26 with categories.
This dissertation, "On Construction and Control of Probabilistic Boolean Networks" by Xi, Chen, 陈曦, 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: Modeling gene regulation is an important problem in genomic research. The Boolean network (BN) and its generalization Probabilistic Boolean network (PBN) have been proposed to model genetic regulatory interactions. BN is a deterministic model while PBN is a stochastic model. In a PBN, on one hand, its stationary distribution gives important information about the long-run behavior of the network. On the other hand, one may be interested in system synthesis which requires the construction of networks from the observed stationary distribution. This results in an inverse problem of constructing PBNs from a given stationary distribution and a given set of Boolean Networks (BNs), which is ill-posed and challenging, because there may be many networks or no network having the given properties and the size of the inverse problem is huge. The inverse problem is first formulated as a constrained least squares problem. A heuristic method is then proposed based on the conjugate gradient (CG) algorithm, an iterative method, to solve the resulting least squares problem. An estimation method for the parameters of the PBNs is also discussed. Numerical examples are then given to demonstrate the effectiveness of the proposed methods. However, the PBNs generated by the above algorithm depends on the initial guess and is not unique. A heuristic method is then proposed for generating PBNs from a given transition probability matrix. Unique solution can be obtained in this case. Moreover, these algorithms are able to recover the dominated BNs and therefore the major structure of the network. To further evaluate the feasible solutions, a maximum entropy approach is proposed using entropy as a measure of the fitness. Newton's method in conjunction with the CG method is then applied to solving the inverse problem. The convergence rate of the proposed method is demonstrated. Numerical examples are also given to demonstrate the effectiveness of our proposed method. Another important problem is to find the optimal control policy for a PBN so as to avoid the network from entering into undesirable states. By applying external control, the network is desired to enter into some state within a few time steps. For PBN CONTROL, people propose to find a control sequence such that the network will terminate in the desired state with a maximum probability. Also, the problem of minimizing the maximum cost is considered. Integer linear programming (ILP) and dynamic programming (DP) in conjunction with hard constraints are then employed to solve the above problems. Numerical experiments are given to demonstrate the effectiveness of our algorithms. A hardness result is demonstrated and suggests that PBN CONTROL is harder than BN CONTROL. In addition, deciding the steady state probability in PBN for a specified global state is demonstrated to be NP-hard. However, due to the high computational complexity of PBNs, DP method is computationally inefficient for a large size network. Inspired by the state reduction strategies studied in [86], the DP method in conjunction with state reduction approach is then proposed to reduce the computational cost of the DP method. Numerical examples are given to demonstrate both the effectiveness and the efficiency of our proposed method. DOI: 10.5353/th_b4832960 Subjects: Genetic regulation - Mathematical models Algebra, Boo
Control In Probabilistic Boolean Networks
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Author : Ashish Choudhary
language : en
Publisher:
Release Date : 2003
Control In Probabilistic Boolean Networks written by Ashish Choudhary and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.
Algorithms For Analysis Inference And Control Of Boolean Networks
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Author : Tatsuya Akutsu
language : en
Publisher: World Scientific
Release Date : 2018-02-14
Algorithms For Analysis Inference And Control Of Boolean Networks written by Tatsuya Akutsu and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-14 with Computers categories.
The Boolean network (BN) is a mathematical model of genetic networks and other biological networks. Although extensive studies have been done on BNs from a viewpoint of complex systems, not so many studies have been undertaken from a computational viewpoint. This book presents rigorous algorithmic results on important computational problems on BNs, which include inference of a BN, detection of singleton and periodic attractors in a BN, and control of a BN. This book also presents algorithmic results on fundamental computational problems on probabilistic Boolean networks and a Boolean model of metabolic networks. Although most contents of the book are based on the work by the author and collaborators, other important computational results and techniques are also reviewed or explained.
Boolean Models And Methods In Mathematics Computer Science And Engineering
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Author : Yves Crama
language : en
Publisher: Cambridge University Press
Release Date : 2010-06-28
Boolean Models And Methods In Mathematics Computer Science And Engineering written by Yves Crama and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-28 with Computers categories.
A collection of papers written by prominent experts that examine a variety of advanced topics related to Boolean functions and expressions.
Probabilistic Methods For Bioinformatics
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Author : Richard E. Neapolitan
language : en
Publisher: Morgan Kaufmann
Release Date : 2009-06-12
Probabilistic Methods For Bioinformatics written by Richard E. Neapolitan and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-12 with Mathematics categories.
The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics. Rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and Bayesian networks are explained in an accessible way using applications and case studies. The many useful applications of Bayesian networks that have been developed in the past 10 years are discussed. Forming a review of all the significant work in the field that will arguably become the most prevalent method in biological data analysis. - Unique coverage of probabilistic reasoning methods applied to bioinformatics data--those methods that are likely to become the standard analysis tools for bioinformatics. - Shares insights about when and why probabilistic methods can and cannot be used effectively; - Complete review of Bayesian networks and probabilistic methods with a practical approach.
On Construction And Control Of Probabilistic Boolean Networks
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Author : 陈曦
language : en
Publisher:
Release Date : 2012
On Construction And Control Of Probabilistic Boolean Networks written by 陈曦 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Algebra, Boolean categories.
An Introduction To Semi Tensor Product Of Matrices And Its Applications
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Author : Dai-Zhan Cheng
language : en
Publisher: World Scientific
Release Date : 2012
An Introduction To Semi Tensor Product Of Matrices And Its Applications written by Dai-Zhan Cheng and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Mathematics categories.
A generalization of Conventional Matrix Product (CMP), called the Semi-Tensor Product (STP), is proposed. It extends the CMP to two arbitrary matrices and maintains all fundamental properties of CMP. In addition, it has a pseudo-commutative property, which makes it more superior to CMP. The STP was proposed by the authors to deal with higher-dimensional data as well as multilinear mappings. After over a decade of development, STP has been proven to be a powerful tool in dealing with nonlinear and logical calculations.This book is a comprehensive introduction to the theory of STP and its various applications, including logical function, fuzzy control, Boolean networks, analysis and control of nonlinear systems, amongst others.
Probabilistic Databases
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Author : Dan Suciu
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2011
Probabilistic Databases written by Dan Suciu and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques