Team:William and Mary/Description

ATTRIBUTIONS

One of the main goals of synthetic biology is to create a modular genetic basis for the independent control of circuit behavior properties. Much progress has been made in achieving this aim for properties like gene expression strength (where well-characterized ribosome binding sites (RBSs) can be swapped within a genetic part), circuit architecture (where promoters can be swapped out to introduce connections and feedback architectures), and even gene expression noise (through a combination of the above two modulations). However, in order to move into the next phase of synthetic biology, we need to be able to control the dynamical properties of circuits— we want to move beyond circuits that focus on endpoint, steady-state values and explore the rich variety of dynamical systems. Fundamentally, gaining controlling of dynamical systems implies gaining control of temporal dynamics. Currently, there is no good way to control the temporal dynamics of gene expression. Current control strategies require either a rewiring of the circuit architecture to achieve different time-dependent dynamics [1, 2] or a complete circumvention of transcriptional circuitry altogether, relying on post-translational dynamics like phosphorylation [3] or protein-protein interactions [4] to transmit information through a circuit. These approaches are often inaccessible to iGEM teams because they require too drastic an overhaul of existing circuit implementations. To alleviate this issue, and to enable future iGEM teams to create robust dynamical circuits, we created a protein degradation based ‘plug-and-play’ style system that allows modular and predictable control of the gene expression speed of a given circuit without requiring a fundamental redesign of existing circuit architecture.

General Support

We would like to extend our sincerest thanks to our team advisor and PI, Dr. Margaret Saha. Her endless dedication and enthusiasm towards our research and well-being could not be more greatly appreciated. We would like to thank Dr. Gregory Smith, Co-PI, for his mathematical modeling expertise and his general advisement of our math team. We also want to thank Dr. Eric Bradley for his tireless assistance with lab facilities and management.

Modeling Support

John Marken, Graduate Research Student, provided us with mathematical modeling advice and invaluable assistance with data analysis. His guidance during brainstorming was also pivotal in determining our project idea.