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postgraduate thesis: On construction and identification problems in probabilistic Boolean networks
Title  On construction and identification problems in probabilistic Boolean networks 

Authors  
Issue Date  2016 
Publisher  The University of Hong Kong (Pokfulam, Hong Kong) 
Citation  Cheng, X. [程晓青]. (2016). On construction and identification problems in probabilistic Boolean networks. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. 
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 projectionbased 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.
Semitensor 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, steadystate distribution and the inverse problem. Numerical examples are provided to show the validity of our proposed theory. 
Degree  Doctor of Philosophy 
Subject  Algebra, Boolean Genetic regulation  Mathematical models 
Dept/Program  Mathematics 
Persistent Identifier  http://hdl.handle.net/10722/233932 
DC Field  Value  Language 

dc.contributor.author  Cheng, Xiaoqing   
dc.contributor.author  程晓青   
dc.date.accessioned  20161007T01:44:34Z   
dc.date.available  20161007T01:44:34Z   
dc.date.issued  2016   
dc.identifier.citation  Cheng, X. [程晓青]. (2016). On construction and identification problems in probabilistic Boolean networks. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.   
dc.identifier.uri  http://hdl.handle.net/10722/233932   
dc.description.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 projectionbased 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. Semitensor 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, steadystate distribution and the inverse problem. Numerical examples are provided to show the validity of our proposed theory.   
dc.language  eng   
dc.publisher  The University of Hong Kong (Pokfulam, Hong Kong)   
dc.relation.ispartof  HKU Theses Online (HKUTO)   
dc.rights  Creative Commons: Attribution 3.0 Hong Kong License   
dc.rights  The author retains all proprietary rights, (such as patent rights) and the right to use in future works.   
dc.subject.lcsh  Algebra, Boolean   
dc.subject.lcsh  Genetic regulation  Mathematical models   
dc.title  On construction and identification problems in probabilistic Boolean networks   
dc.type  PG_Thesis   
dc.identifier.hkul  b5793625   
dc.description.thesisname  Doctor of Philosophy   
dc.description.thesislevel  Doctoral   
dc.description.thesisdiscipline  Mathematics   
dc.description.nature  published_or_final_version   