Graph properties of biological interaction networks

Tonello, Elisa (2018) Graph properties of biological interaction networks. PhD thesis, University of Nottingham.

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This thesis considers two modelling frameworks for interaction networks in biology. The first models the interacting species qualitatively as discrete variables, with the regulatory graphs expressing their mutual influence. Circuits in the regulatory structure are known to be indicative of some asymptotic behaviours. We investigate the relationship between local negative circuits and sustained oscillations, presenting new examples of Boolean networks without local negative circuits and admitting a cyclic attractor. We then show how regulatory properties of Boolean networks can be investigated via satisfiability problems, and use the technique to examine the role of local negative circuits in networks of small dimension.

To enable the application of Boolean techniques to the study of multivalued networks, a mapping of discrete networks to Boolean can be considered. The Boolean version, however, is defined only on a subset of the Boolean states. We propose a method for extending the Boolean version that preserves both the attractors and the regulatory structure of the network.

Chemical reaction network theory models the dynamics of species concentrations via systems of ordinary differential equations, establishing connections between the network structure and the dynamics. Some results assume mass action kinetics, whereas biochemical models often adopt other rate forms. We propose algorithms for elimination of intermediate species, that can be used to find whether a mass action network simplifies to a given chemical system.

We then consider the problem of identification of generalised mass action networks that give rise to a given mass action dynamics, while displaying useful structural properties, such as weak reversibility. In particular, we investigate systems obtained by preserving the reaction vectors of the mass action network, and outline a new algorithmic approach.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Farcot, Etienne
Owen, Markus R.
Twycross, J.P.
Subjects: Q Science > QA Mathematics > QA150 Algebra
Q Science > QH Natural history. Biology > QH301 Biology (General)
Faculties/Schools: UK Campuses > Faculty of Science > School of Mathematical Sciences
Item ID: 52105
Depositing User: Tonello, Elisa
Date Deposited: 19 Jul 2018 04:40
Last Modified: 07 May 2020 18:30

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