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Title: Multiscale modelling and simulation in systems biology
Author: Mizeranschi, Alexandru E.
ISNI:       0000 0004 7226 2130
Awarding Body: Ulster University
Current Institution: Ulster University
Date of Award: 2016
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The focus of this thesis was determined by the FP7-funded e-infrastructure project Multiscale Applications on European e-Infrastructures (MAPPER). The main goal of MAPPER was to develop a distributed multiscale computing frame­work facilitating the development, deployment and use of multiscale modelling and simulation applications in various domains. MAPPER was strongly in­volved with the computing aspects of multiscale modelling and simulation. Within the MAPPER project, the research described in this thesis was focused on the development of novel (a) general multiscale modelling and simulation methods and technologies, and (b) multiscale computational systems biology methods and tools. We chose gene regulation as the main biological problem domain to drive our R&D efforts. An important way to investigate gene regulation is through automated reverse ­engineering of mechanistic dynamic GRN models from gene expression time- series data. This, however, is limited by the quality and amount of available data and the computational complexity of the reverse-engineering process. The specific objective of this thesis was to develop and assess novel solutions for reverse-engineering GRN models from gene expression data. This objective was explored from three main perspectives. First, to facilitate the development of improved approaches to GRN model reverse-engineering, we explored the representational and computational as­pects of various GRN rate laws. Second, we explored how the computational aspects of the GRN model reverse-engineering problem could be viewed as a distributed multiscale computing problem. A major piece of R&D that res­ulted from this was the development of MultiGrain/MAPPER, a software tool that allows the multiscale modelling and simulation of GRNs. Third, based on MultiGrain/MAPPER and other software we created, we developed and assessed various new reverse-engineering algorithms and investigated their performance in terms of effectiveness and efficiency.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available