Control In 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.

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.

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.

Probabilistic Boolean Networks

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Author : Ilya Shmulevich
language : en
Publisher: SIAM
Release Date : 2010-01-21

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 2010-01-21 with Mathematics categories.

The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.

On Construction And Control Of Probabilistic Boolean Networks

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Author : Chen, Xi (mathematician.)
language : en
Publisher:
Release Date : 2012

On Construction And Control Of Probabilistic Boolean Networks written by Chen, Xi (mathematician.) 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.

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

Mathematical Models For Control Of Probabilistic Boolean Networks

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Author : Yue Jiao
language : en
Publisher:
Release Date : 2008

Mathematical Models For Control Of Probabilistic Boolean Networks written by Yue Jiao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Algebra, Boolean categories.

Mathematical Models For Control Of Probabilistic Boolean Networks

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Author :
language : en
Publisher:
Release Date : 2009

Mathematical Models For Control Of Probabilistic Boolean Networks written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with 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.