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Title: An integrated computational & mammalian synthetic biology framework for engineering Turing patterns
Author: Scholes, Natalie
ISNI:       0000 0004 7659 0203
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2019
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Turing patterns are thought to underlie the formation of many tissue structures during development. First proposed by Alan Turing, two key properties are known to affect the formation of patterns: the fine balance of the interaction parameters and the differential diffusion of an activating and inhibiting species. Despite many experimental examples, it has been difficult to determine if Turing patterns alone suffice to generate patterns in vivo. Engineering Turing patterns, in the absence of any endogenous patterning networks, may shed light on how they function in nature. Here I combined a computational and mammalian synthetic biology approach into a unified framework for engineering Turing patterns. Through theoretical analysis of a wide range of networks (15,514 networks with two and three interacting species), I found that ≈60% of all networks tested can achieve Turing patterns. However, features beyond structure determine a network's Turing patterning capability. Even though individual networks with three interacting molecules are more likely to form Turing patterns than two species networks, the parameter space always remains very limited (< 1% of the explored parameter space). To enable the precise parameter tuning required, I therefore developed an in vivo platform to integrate and exchange DNA in a pre-defined genomic locus, using CRISPR/Cas9. This platform ensures the functionality of the regulatory circuits by making use of endogenous gene regulatory elements. Moreover, the theoretical analysis revealed that differential diffusion remains advantageous, albeit not absolutely necessary, for Turing pattern formation. Hence, I engineered a 90-fold slower diffusing activator variant. Factors such as intracellular negative feedbacks complicated the system and ultimately led to the conclusion that these components are unsuitable for supporting Turing patterns. Nonetheless, the general results from the theoretical analysis, and the generation of a novel platform for generating inducible systems in mammalian cell lines, show promise for future Turing pattern generation.
Supervisor: Isalan, Mark ; Stumpf, Michael Sponsor: Biotechnology and Biological Sciences Research Council ; Boehringer Ingelheim Fonds
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral