Systems biology approaches to the computational modelling of trypanothione metabolism in Trypanosoma brucei

Gu, Xu (2010) Systems biology approaches to the computational modelling of trypanothione metabolism in Trypanosoma brucei. PhD thesis, University of Glasgow.

Abstract

This work presents an advanced modelling procedure, which applies both structural
modelling and kinetic modelling approaches to the trypanothione metabolic network
in the bloodstream form of Trypanosoma brucei, the parasite responsible for African
Sleeping sickness. Trypanothione has previously been identified as an essential
compound for parasitic protozoa, however the underlying metabolic processes are
poorly understood. Structural modelling allows the study of the network metabolism in
the absence of sufficient quantitative information of target enzymes. Using this approach
we examine the essential features associated with the control and regulation of
intracellular trypanothione level. The first detailed kinetic model of the trypanothione
metabolic network is developed, based on a critical review of the relevant scientific papers.
Kinetic modelling of the network focuses on understanding the effect of anti-trypanosomal
drug DFMO and examining other enzymes as potential targets for
anti-trypanosomal chemotherapy.

We also consider the inverse problem of parameter
estimation when the system is defined with non-linear differential equations.
The performance of a recently developed population-based
PSwarm algorithm that has not yet been widely applied to biological problems is investigated
and the problem of parameter estimation under conditions such as experimental noise
and lack of information content is illustrated using the ERK signalling pathway.
We propose a novel multi-objective optimization algorithm (MoPSwarm) for the
validation of perturbation-based models of biological systems, and perform a
comparative study to determine the factors crucial to the performance of the algorithm.
By simultaneously taking several, possibly conflicting aspects into
account, the problem of parameter estimation arising from non-informative
experimental measurements can be successfully overcome.
The reliability and efficiency of MoPSwarm is also tested using the ERK signalling pathway
and demonstrated in model validation of the polyamine biosynthetic
pathway of the trypanothione network.

It is frequently a problem that models of biological systems are based on a relatively small
amount of experimental information and that extensive in vivo observations
are rarely available. To address this problem, we propose a new and generic methodological
framework guided by the principles of Systems Biology. The proposed methodology
integrates concepts from mathematical modelling and system identification to enable
physical insights about the system to be accounted for in the modelling procedure.
The framework takes advantage of module-based representation and employs PSwarm
and our proposed multi-objective optimization algorithm as the core of this framework.
The methodological framework is employed in the study of the trypanothione metabolic network, specifically, the validation of the model of the polyamine biosynthetic pathway.
Good agreements with several existing data sets are obtained and new predictions about enzyme kinetics
and regulatory mechanisms are generated, which could be tested by in vivo approaches.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Systems Biology, Mathematical Modelling, Metabolic Modelling, Global Optimization, Multi-objective Optimization
• Subjects: Q Science > QH Natural history > QH301 Biology; Q Science > QA Mathematics > QA75 Electronic computers. Computer science; Q Science > Q Science (General)
• Colleges/Schools: College of Science and Engineering > School of Computing Science
• Supervisor's Name: Welland, Prof. Ray, Higham, Prof. Des and Barrett, Prof. Mike
• Date of Award: 2010
• Depositing User: Dr. Xu Gu
• Unique ID: glathesis:2010-1618
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 11 Mar 2010
• Last Modified: 10 Dec 2012 13:44
• URI: https://theses.gla.ac.uk/id/eprint/1618