CityU Institutional Repository >
CityU Electronic Theses and Dissertations >
ETD - Dept. of Manufacturing Engineering and Engineering Management >
MEEM - Doctor of Philosophy >
Please use this identifier to cite or link to this item:
|Title: ||Dynamic analysis and modeling of biochemical regulatory networks|
|Other Titles: ||Sheng hua tiao jie wang luo de jian mo ji dong li xue fen xi|
|Authors: ||Sun, Yonghui (孫永輝)|
|Department: ||Department of Manufacturing Engineering and Engineering Management|
|Degree: ||Doctor of Philosophy|
|Issue Date: ||2010|
|Publisher: ||City University of Hong Kong|
|Subjects: ||Genetic regulation|
|Notes: ||CityU Call Number: QH450 .S86 2010|
x, 158 leaves : ill. 30 cm.
Thesis (Ph.D.)--City University of Hong Kong, 2010.
Includes bibliographical references (leaves -156)
|Abstract: ||Dynamic analysis and modeling are two of the most active research topics in the
study of biochemical networks, and which have a great potential in gene therapy and
medicine development. Due to the advances of molecular biology, genomics, computer science, modern control theory, nonlinear dynamics theory and other relevant
fields, a system-level understanding of biological systems has become possible. This
has thus led to a "new" research field, systems biology, with the goal of unraveling
basic dynamic processes, feedback control loops and signal processing mechanisms
underlying life at system level.
It is worth noting that a molecular event is a noisy process due to significant
thermal fluctuations with transcriptional control, alternative splicing, translation,
diffusion and chemical modification reaction. Moreover, robustness is generally believed as a fundamental property of biological systems. Complex human diseases such
as cancers and diabetes can be classified as failures in robustness mechanisms of biological systems. Furthermore, building a dynamic model for biochemical networks is
a key step of systems biology, and there are still many critical issues to be addressed.
Therefore, this thesis is focused on dynamic analysis and modeling of biochemical
networks. In particular, it will address the issues such as stochastic stability and disturbance attenuation analysis, (constrained) bio-circuits design, entrained collective
rhythms and dynamic fuzzy modeling.
Firstly, we consider robust stochastic stability for genetic regulatory networks
with both intrinsic and extrinsic stochastic noises. A novel delay-dependent robust
stability condition with an optimal disturbance attenuation level, in the form of
linear matrix inequalities (LMIs), is derived for uncertain stochastic genetic networks with time-varying delays and intrinsic and extrinsic noises. Furthermore, considering
structure variations governed by a Markov process, robust stochastic stability and
disturbance attenuation analysis are then considered for Markovian genetic networks.
These stability conditions can be tested efficiently by available commercial software
packages such as Matlab LMI Control Toolbox.
Then, attention goes to positive stability analysis and bio-circuits design for
nonlinear biochemical networks. A fuzzy interpolation approach is employed to approximate nonlinear biochemical networks. Based on the Lyapunov stability theory,
sufficient conditions are developed to guarantee the equilibrium points of nonlinear
biochemical networks to be positive and asymptotically stable. In addition, a constrained bio-circuits design with positive control input is also considered. It is shown
that these conditions can be formulated as a solution to a convex optimization problem, which can be easily facilitated by using the Matlab LMI control toolbox.
Furthermore, it has been widely recognized that a complicated living organism
cannot be fully understood by merely analyzing individual components. Thus the
next topic of study is the collective rhythms of multicellular systems, which are central
to life. The problem of entrained collective rhythms of multicellular systems by using
partial impulsive control strategy is addressed. The objective is to design an impulsive controller based only on those partially available cell states so that the entrained
collective rhythms are guaranteed for multicellular systems with the cell-to-cell communication mechanism. By using a newly developed impulsive integro-differential inequality, sufficient conditions are derived to achieve the entrained collective rhythms
of multicellular systems.
Finally, the dynamic fuzzy modeling approach is applied for modeling genetic
regulatory networks from gene expression data. The parameters of the dynamic
fuzzy model and the optimal number of fuzzy rules for the fuzzy gene network can
be obtained via the proposed modeling approach from the measured gene expression
data. One of the main features of the proposed approach is that the prior qualitative
knowledge on the network structure can be easily incorporated in the proposed identification algorithm so that the faster learning convergence of the algorithm can be achieved. Two sets of data, one the synthetic data, and the other the experimental
SOS DNA repair network data with structural knowledge, are used to validate the
proposed modeling approach. It is shown that the proposed approach is effective in
modeling genetic regulatory networks.|
|Online Catalog Link: ||http://lib.cityu.edu.hk/record=b3947903|
|Appears in Collections:||MEEM - Doctor of Philosophy |
Items in CityU IR are protected by copyright, with all rights reserved, unless otherwise indicated.