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postgraduate thesis: Optimization models and computational methods for systems biology
Title  Optimization models and computational methods for systems biology 

Authors  
Advisors  
Issue Date  2012 
Publisher  The University of Hong Kong (Pokfulam, Hong Kong) 
Citation  Cong, Y. [丛阳]. (2012). Optimization models and computational methods for systems biology. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4775284 
Abstract  Systems biology is a comprehensive quantitative analysis of the manner in
which all the components of a biological system interact functionally along with
time. Mathematical modeling and computational methods are indispensable
in such kind of studies, especially for interpreting and predicting the complex
interactions among all the components so as to obtain some desirable system
properties. System dynamics, system robustness and control method are three
crucial properties in systems biology. In this thesis, the above properties are
studied in four different biological systems.
The outbreak and spread of infectious diseases have been questioned and
studied for years. The spread mechanism and prediction about the disease could
enable scientists to evaluate isolation plans to have significant effects on a particular
epidemic. A differential equation model is proposed to study the dynamics
of HIV spread in a network of prisons. In prisons, screening and quarantining
are both efficient control manners. An optimization model is proposed to study
optimal strategies for the control of HIV spread in a prison system.
A primordium (plural: primordia) is an organ or tissue in its earliest recognizable
stage of development. Primordial development in plants is critical to the
proper positioning and development of plant organs. An optimization model and
two control mechanisms are proposed to study the dynamics and robustness of primordial systems.
Probabilistic Boolean Networks (PBNs) are mathematical models for studying
the switching behavior in genetic regulatory networks. An algorithm is proposed
to identify singleton and small attractors in PBNs which correspond to
cell types and cell states. The captured problem is NPhard in general. Our
algorithm is theoretically and computationally demonstrated to be much more
efficient than the naive algorithm that examines all the possible states.
The goal of studying the longterm behavior of a genetic regulatory network is
to study the control strategies such that the system can obtain desired properties.
A control method is proposed to study multiple external interventions meanwhile
minimizing the control cost.
Robustness is a paramount property for living organisms. The impact degree
is a measure of robustness of a metabolic system against the deletion of single
or multiple reaction(s). An algorithm is proposed to study the impact degree
in Escherichia coli metabolic system. Moreover, approximation method based
on Branching process is proposed for estimating the impact degree of metabolic
networks. The effectiveness of our method is assured by testing with realworld
Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae and Homo Sapiens
metabolic systems. 
Degree  Doctor of Philosophy 
Subject  Systems biology  Mathematical models. 
Dept/Program  Mathematics 
DC Field  Value  Language 

dc.contributor.advisor  Ching, WK   
dc.contributor.advisor  Tsing, NK   
dc.contributor.author  Cong, Yang.   
dc.contributor.author  丛阳.   
dc.date.issued  2012   
dc.identifier.citation  Cong, Y. [丛阳]. (2012). Optimization models and computational methods for systems biology. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4775284   
dc.description.abstract  Systems biology is a comprehensive quantitative analysis of the manner in which all the components of a biological system interact functionally along with time. Mathematical modeling and computational methods are indispensable in such kind of studies, especially for interpreting and predicting the complex interactions among all the components so as to obtain some desirable system properties. System dynamics, system robustness and control method are three crucial properties in systems biology. In this thesis, the above properties are studied in four different biological systems. The outbreak and spread of infectious diseases have been questioned and studied for years. The spread mechanism and prediction about the disease could enable scientists to evaluate isolation plans to have significant effects on a particular epidemic. A differential equation model is proposed to study the dynamics of HIV spread in a network of prisons. In prisons, screening and quarantining are both efficient control manners. An optimization model is proposed to study optimal strategies for the control of HIV spread in a prison system. A primordium (plural: primordia) is an organ or tissue in its earliest recognizable stage of development. Primordial development in plants is critical to the proper positioning and development of plant organs. An optimization model and two control mechanisms are proposed to study the dynamics and robustness of primordial systems. Probabilistic Boolean Networks (PBNs) are mathematical models for studying the switching behavior in genetic regulatory networks. An algorithm is proposed to identify singleton and small attractors in PBNs which correspond to cell types and cell states. The captured problem is NPhard in general. Our algorithm is theoretically and computationally demonstrated to be much more efficient than the naive algorithm that examines all the possible states. The goal of studying the longterm behavior of a genetic regulatory network is to study the control strategies such that the system can obtain desired properties. A control method is proposed to study multiple external interventions meanwhile minimizing the control cost. Robustness is a paramount property for living organisms. The impact degree is a measure of robustness of a metabolic system against the deletion of single or multiple reaction(s). An algorithm is proposed to study the impact degree in Escherichia coli metabolic system. Moreover, approximation method based on Branching process is proposed for estimating the impact degree of metabolic networks. The effectiveness of our method is assured by testing with realworld Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae and Homo Sapiens metabolic systems.   
dc.language  eng   
dc.publisher  The University of Hong Kong (Pokfulam, Hong Kong)   
dc.relation.ispartof  HKU Theses Online (HKUTO)   
dc.rights  The author retains all proprietary rights, (such as patent rights) and the right to use in future works.   
dc.rights  Creative Commons: Attribution 3.0 Hong Kong License   
dc.source.uri  http://hub.hku.hk/bib/B47752841   
dc.subject.lcsh  Systems biology  Mathematical models.   
dc.title  Optimization models and computational methods for systems biology   
dc.type  PG_Thesis   
dc.identifier.hkul  b4775284   
dc.description.thesisname  Doctor of Philosophy   
dc.description.thesislevel  Doctoral   
dc.description.thesisdiscipline  Mathematics   
dc.description.nature  published_or_final_version   
dc.identifier.doi  10.5353/th_b4775284   
dc.date.hkucongregation  2012   