Rosa, Luca; (2021) Computation Using Patterning of Bacterial Colonies. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

The functional complexity that is possible to engineer in single cells is limited by different aspects, such as metabolic burden or protein cross talk. For these reasons, in recent years the field of synthetic biology is moving away from intracellular systems to intercellular systems. This allows to distribution of computation across different strains mimicking real biological systems, where complex behaviours emerge from population level interactions. One area of interest of the field is the development of biosensors. Different whole-cell biosensors have been developed where a system detecting an analyte is integrated in a living organism. The limitation of this approach is that they usually sense a single metabolite and a new strain needs to be engineered if a new function is required. There is therefore a need for a platform where different biosensors can be integrated to perform multiplexed computation with different inputs. The focus of this thesis was to explore new types of biocomputation. In particular, the main focus was to demonstrate how emergent computation can be performed using spatial patterning. In the first part I demonstrated that it is possible to perform spatial computation using the emergent behaviours of an existing bistable system. Here, I successfully computed an OR and XOR gate. In the second part, I developed a platform for the integration of biosensor signals using the patterning of bacterial colonies. The platform's architecture consists of two strains arranged in a grid structure: the ``sender'' strain integrates the biosensor system and produces diffusible molecules, and a ``receiver'' strain that expresses a reporter protein in response to the diffusible molecules. This is integrated with a dispensing robot and a custom imaging setup. Finally, this platform was successfully used to compute a spatial AND and OR logic gate, aided with simulations performed using a reaction-diffusion mathematical model. Overall, the work presented here contributes to the advancement of the field towards the construction of complex artificial biological systems and the engineering of emergent behaviours.

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