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Cafe Synthetique: Engineering Living Systems

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If you have a question about this talk, please contact Alexandra Ting.


Despite the rapid success of synthetic biology, synthetic circuits have been much less accurate and robust than natural ones, which has made it virtually impossible to connect synthetic modules into reliable networks. The stochastic nature of gene-expression, the unpredictable dynamics of the ‘unknown’ parts of intracellular genetic networks with which the circuit interacts, and the fluctuating environment in which cells live have made their operation fundamentally circumstantial.

In order to design circuits that perform reliably, we are developing platforms that enable quantitative evaluation of the circuits in vivo with single-cell resolution and near-perfect control of growth-conditions. These platforms are enabling us to analyze and mitigate the effects of stochasticity, cellular interference, and environmental perturbation. We are also extending our interference assay to identify reliable elements, and design autonomous directed evolution platforms to engineer parts with improved functionalities in the native context. Another major goal of the lab is to replicate such approaches towards building robust and resilient synthetic microbial communities, which are cellular equivalents to genetic circuits, and relate to microbial networks in a similar way as the gene-circuits do to genetic network. Building such microbial circuits as motifs for microbial interaction network not only paves the way for understanding the modes of interaction in natural communities, but also to create novel functions that will surpass the limits of biotechnological potentials of individual microbes.

DESIGN PRINCIPLES OF DEGENERACY Dhruva Raman (O’Leary Lab, Department of Engineering)

A key feature of many biological systems is that different system components display seemingly similar functional roles, and can thus be ‘knocked out’ without greatly hurting system function. A commonly cited purpose of this degeneracy is to robustify the system against component failure. In this talk we highlight a different functional benefit. We show how degeneracy allows adaptive systems to faster optimise their performance in a changing environment, when they only have limited, noisy information on how to do so. We use neural circuits learning to optimally process inputs as a specific case study, and hope to discuss more general biological implications with the audience.

—- Café Synthetique is the monthly meetup for the Cambridge synthetic biology community with informal talks, discussion and pub snacks. It is kindly sponsored by Cambridge Consultants.

This talk is part of the Engineering Biology Interdisciplinary Research Centre series.

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