Papers by N. Nolan and E.D. Sontag

The development of genetic memory devices in synthetic biology is a challenging process that requires extensive analysis and characterization. In mammalian systems, this complexity is compounded by the need for a small DNA payload for efficient delivery into the cell. Previous genetic memory devices have relied exclusively on protein-based regulation, which are limited by their large size; in this paper, we propose a microRNA-based multistable network, which effectively halves the payload size for more efficient delivery. We demonstrate that the system can be multistable, and use formal methods to characterize constraints on design parameters that guarantee multistability. Our results provide a new genetic network topology that can achieve multistability and demonstrate the use of formal methods in the design of sophisticated genetic network architectures against non-convex top-level specifications.

The identification of constraints on system parameters that will ensure that a system achieves desired requirements remains a challenge in synthetic biology, where components unintendedly affect one another by perturbing the cellular environment in which they operate. This paper shows how to solve this problem optimally for a class of input/output system-level specifications, and for unintended interactions due to resource sharing. Specifically, we show how to solve the problem based on the input/output properties of the subsystems and on the unintended interaction map. Our approach is based on the elimination of quantifiers in monotone properties of the system. We illustrate applications of this methodology to guaranteeing system-level performance of multiplexed and sequential biosensing and of bistable genetic circuits.