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Title: On a thermodynamic approach to biomolecular interaction networks
Author: Honorato-Zimmer, Ricardo
ISNI:       0000 0004 7224 9227
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2017
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We explore the direct and inverse problem of thermodynamics in the context of rule-based modelling. The direct problem can be concisely stated as obtaining a set of rewriting rules and their rates from the description of the energy landscape such that their asymptotic behaviour when t → ∞ coincide. To tackle this problem, we describe an energy function as a finite set of connected patterns P and an energy cost function e which associates real values to each of these energy patterns. We use a finite set of reversible graph rewriting rules G to define the qualitative dynamics by showing which transformations are possible. Given G and P, we construct a finite set of rules Gp which i) has the same qualitative transition system as G and ii) when equipped with rates according to e, defines a continuous-time Markov chain that has detailed balance with respect to the invariant probability distribution determined by the energy function. The construction relies on a technique for rule refinement described in earlier work and allows us to represent thermodynamically consistent models of biochemical interaction networks in a concise manner. The inverse problem, on the other hand, is to i) check whether a rule-based model has an energy function that describes its asymptotic behaviour and if so ii) obtain the energy function from the graph rewriting rules and their rates. Although this problem is known to be undecidable in the general case, we find two suitable subsets of Kappa, our rule-based modelling framework of choice, were this question can be answer positively and the form of their energy functions described analytically.
Supervisor: Danos, Vincent ; Plotkin, Gordon Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: rule-based modelling ; Kappa language ; generator rules ; thermodynamics ; rule-based modelling