Probabilistic Boolean Networks

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.

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

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. Contents: Preliminaries Boolean Networks Detection of Attractors Detection of Singleton Attractors Detection of Periodic Attractors Identification of Boolean Networks Control of Boolean Networks Predecessor and Observability Problems Semi-Tensor Product Approach Analysis of Metabolic Networks Probabilistic Boolean Networks Identification of Probabilistic Boolean Networks Control of Probabilistic Boolean Networks Readership: Graduate students and researchers working on string theory and related topics. Keywords: Boolean Networks;Bioinformatics;Systems Biology;Combinatorial Algorithms;AttractorsReview: Key Features: Unique book focusing on computational aspects of Boolean networks Provide computational foundations on Boolean networks Contain recent and up-to-date results on algorithms for Boolean networks

This dissertation, "On Construction and Identification Problems in Probabilistic Boolean Networks" by Xiaoqing, Cheng, 程晓青, 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: In recent decades, rapidly evolving genomic technologies provide a platform for exploring the massive amount of genomic data. At the same time, it also triggers dramatic development in systems biology. A number of mathematical models have been proposed to understand the dynamical behavior of the biological systems. Among them, Boolean Network (BN) and its stochastic extension Probabilistic Boolean Network (PBN) have attracted much attention. Identification and construction problems are two kinds of vital problems in studying the behavior of a PBN. A novel problem of observability of singleton attractors was firstly proposed, which was defined as identifying the minimum number of consecutive nodes to discriminate different singleton attractors. It may help in finding biomarkers for different disease types, thus it plays a vital role in the study of signaling networks. The observability of singleton attractor problem can be solved in O(n) time, where n is the number of genes in a BN. Later, the problem was extended to discriminating periodical attractors. For the periodical case, one has to consider multiple time steps and a new algorithm was proposed. Moreover, one may also curious about identifying the minimum set of nodes that can determine uniquely the attractor cycles from the others in the network, this problem was also addressed. In order to study realistic PBNs, inference on the structure of PBNs from gene expression time series data was investigated. The number of samples required to uniquely determine the structure of a PBN was studied. Two models were proposed to study different classes of PBNs. Using theoretical analysis and computational experiments the structure of a PBN can be exactly identified with high probability from a relatively small number of samples for some classes of PBNs having bounded indegree. Furthermore, it is shown that there exist classes of PBNs for which it is impossible to uniquely determine their structure from samples under these two models. Constructing the structure of a PBN from a given probability transition matrix is another key problem. A projection-based gradient descent method was proposed for solving huge size constrained least square problems. It is a matrixfree iterative scheme for solving the minimizer of the captured problem. A convergence analysis of the scheme is given, and the algorithm is then applied to the construction of a PBN given its probability transition matrix. Efficiency and effectiveness of the proposed method are verified through numerical experiments. Semi-tensor product approach is another powerful tool in constructing of BNs. However, to our best knowledge, there is no result on the relationship of the structure matrix and transition matrix of a BN. It is shown that the probability structure matrix and probability transition matrix are similar matrices. Three main problems in PBN were discussed afterward: dynamics, steady-state distribution and the inverse problem. Numerical examples are provided to show the validity of our proposed theory. Subjects: Algebra, Boolean Genetic regulation - Mathematical models

This dissertation, "On Construction and Identification Problems in Probabilistic Boolean Networks" by Xiaoqing, Cheng, 程晓青, 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: In recent decades, rapidly evolving genomic technologies provide a platform for exploring the massive amount of genomic data. At the same time, it also triggers dramatic development in systems biology. A number of mathematical models have been proposed to understand the dynamical behavior of the biological systems. Among them, Boolean Network (BN) and its stochastic extension Probabilistic Boolean Network (PBN) have attracted much attention. Identification and construction problems are two kinds of vital problems in studying the behavior of a PBN. A novel problem of observability of singleton attractors was firstly proposed, which was defined as identifying the minimum number of consecutive nodes to discriminate different singleton attractors. It may help in finding biomarkers for different disease types, thus it plays a vital role in the study of signaling networks. The observability of singleton attractor problem can be solved in O(n) time, where n is the number of genes in a BN. Later, the problem was extended to discriminating periodical attractors. For the periodical case, one has to consider multiple time steps and a new algorithm was proposed. Moreover, one may also curious about identifying the minimum set of nodes that can determine uniquely the attractor cycles from the others in the network, this problem was also addressed. In order to study realistic PBNs, inference on the structure of PBNs from gene expression time series data was investigated. The number of samples required to uniquely determine the structure of a PBN was studied. Two models were proposed to study different classes of PBNs. Using theoretical analysis and computational experiments the structure of a PBN can be exactly identified with high probability from a relatively small number of samples for some classes of PBNs having bounded indegree. Furthermore, it is shown that there exist classes of PBNs for which it is impossible to uniquely determine their structure from samples under these two models. Constructing the structure of a PBN from a given probability transition matrix is another key problem. A projection-based gradient descent method was proposed for solving huge size constrained least square problems. It is a matrixfree iterative scheme for solving the minimizer of the captured problem. A convergence analysis of the scheme is given, and the algorithm is then applied to the construction of a PBN given its probability transition matrix. Efficiency and effectiveness of the proposed method are verified through numerical experiments. Semi-tensor product approach is another powerful tool in constructing of BNs. However, to our best knowledge, there is no result on the relationship of the structure matrix and transition matrix of a BN. It is shown that the probability structure matrix and probability transition matrix are similar matrices. Three main problems in PBN were discussed afterward: dynamics, steady-state distribution and the inverse problem. Numerical examples are provided to show the validity of our proposed theory. Subjects: Algebra, Boolean Genetic regulation - Mathematical models

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.

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.

A collection of papers written by prominent experts that examine a variety of advanced topics related to Boolean functions and expressions.