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Title: Systematic design of reaction systems with prescribed behaviors : deterministic and stochastic methods
Author: Plesa, Tomislav
ISNI:       0000 0004 8507 1819
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2018
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Synthetic biology is a growing interdisciplinary field, with far-reaching applications, which aims to design biochemical systems that behave in a desired manner. With the advancements in nucleic-acid-based technology in general, and strand-displacement DNA computing in particular, a large class of abstract biochemical networks may be physically realized using nucleic acids, which may be integrated into a variety of environments, ranging from test-tubes, cell-sized compartments, to living cells. Mathematical methods for a systematic design of the abstract systems with prescribed behaviors, used as blueprints for the nucleic-acid-based ones, have received less attention, and form the focus of this thesis. At the (less-detailed) deterministic level, suitable when the abstract reaction networks involve only high-abundance species, and applicable when the nucleic-acid-based physical networks are integrated into test-tubes, the so-called kinetic transformations are developed. The transformations map ordinary differential equations with polynomial right-hand sides to reaction-rate equations, allowing one to design biochemical reaction networks with desired deterministic dynamics. At the (more-detailed) stochastic level, suitable when the abstract reaction networks involve some low-abundance species, and applicable when the nucleic-acid-based physical networks are integrated into vesicles or living cells, the so-called noise approximation algorithm is developed. The algorithm structurally modifies any given reaction network under mass-action kinetics, in such a way that (i) controllable state-dependent noise is introduced into the stochastic dynamics, while (ii) the deterministic dynamics are preserved. Combining the developed deterministic and stochastic methods, together with the result that any reaction with more than two reactants may be stochastically approximated by up-to bi-molecular reactions, a deterministic-stochastic hybrid approach of designing reaction networks is put forward. In particular, the kinetic transformations may be used to construct networks with desired deterministic dynamics, and the noise approximation algorithm may then be used to favorably reprogram the intrinsic noise in the stochastic dynamics, while preserving the deterministic skeleton. The capabilities of this approach are demonstrated by designing reaction networks displaying biochemically relevant exotic dynamics, such as (noise-induced) multistability and oscillations, and specific bifurcation structures.
Supervisor: Erban, Radek Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available