Multiscale modelling and simulation in systems biology

Abstract

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.

Thesis is embargoed until 31st October 2018

Date of Award	Oct 2016
Original language	English
Supervisor	Paul Thompson (Supervisor) & Werner Dubitzky (Supervisor)