Engineering biological complexity in yeast: synthetic pattern formation and emergence

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Engineering increased biological complexity is a key goal for biodesign engineering. Theoretical studies have uncovered simple mathematical rules that generate robust heterogeneity in spatial and temporal patterns within cell populations that can be considered as 'engineering principles'. Division of labour and other forms of heterogeneity also underlie the emergent properties of a multicellular entity, often enabling higher efficiency as a whole in important functions like metabolism, transport, structural stability, and feedback control. An ambitious and timely challenge for synthetic biology is to understand how to reverse engineer and rationally design symmetry-breaking events and the emergent interactions that then follow in a community of cells.This interdisciplinary project will develop synthetic biology tools to predictively design and instruct synthetic developmental decision-making in yeast. To control multicellular growth pattern and morphogenesis, we will use synthetic gene regulation to modulate the timing and frequency of cell division and growth, and to instruct orthogonal adhesion between sets of cells. Cells will be programmed to differentiate in response to cell-to-cell communication and to lateral activation/inhibition. In order to expand the modular DNA resources for engineering multicellular complexity, this project aims to create a novel molecular machine for a self-organising symmetry-breaking events.To do this, we will transfer a well-characterised morphogen system from plants that provides a concentration-dependent molecular switch. In plants, accumulation of the hormone auxin coordinates differential cell fate and growth regulation. Auxin moves between cells with the aid of the efflux carrier PIN proteins, which show polar localisation within a cell. Auxin is already a well-established chemical inducer of synthetic gene regulation in the yeast. By introducing the PIN module into cells with auxin-regulated gene control, we will be able to program intercellular auxin flow and asymmetrical concentration gradient within a yeast colony. Data analysis of the images, videos, and flow cytometry data, along with Bayesian statistical methods will be used to generate parameters to populate mathematical models of the intracellular gene circuits and complementary agent-based models describing how the different cells interact, communicate and adhere.