Curated Optogenetic Publication Database

Abstract: The field of synthetic biology focuses on creating modular components which can be used to generate complex and controllable synthetic biological systems. Unfortunately, the intrinsic noise of gene regulation can be large enough to break these systems. Noise is largely treated as a nuisance and much past effort has been spent to create robust components that are less influenced by noise. However, extensive analysis of noise combined with ‘smart’ microscopy tools and optognenetic actuators can create control opportunities that would be difficult or impossible to achieve in the deterministic setting. In previous work, we proposed an Optogenetic Maxwell’s Demons (OMD) control problem and found that deep understanding and manipulation of noise could create controllers that break symmetry between cells, even when those cells share the same optogenetic input and identical gene regulation circuitry. In this paper, we extend those results to analyze (in silico) the robustness of the OMD control under changes in system volume, with time observation/actuation delays, and subject to parametric model uncertainties.

Abstract: Optogenetics is a powerful technology to control synthetic gene circuits using external and computer-programmable light inputs. Like all biological processes, these systems are subject to intrinsic noise that arises from the stochastic process of gene regulation at the single-cell level. Many engineers have sought to mitigate this noise by developing more complex embedded bio-circuits, but recent work has shown that noise-exploiting stochastic controllers could enable new control strategies that take advantage of noise, rather than working against it. These noise-exploiting controllers were initially proposed to solve a single-input-multi-output stationary control problem, where symmetry was broken in a means reminiscent to the concept of Maxwell’s Demon. In this paper, we extend those results and show through computation that transient, asymmetric, and asynchronous stochastic control of the single-input-multi-output (SIMO) control problem is posible to achieve by cycling through different controllers in time. We show that such a method is able control two cells to two different periodic fates with different frequencies and different phases despite the use of only one control input.

Abstract: Synthetic biology seeks to develop modular biocircuits that combine to produce complex, controllable behaviors. These designs are often subject to noisy fluctuations and uncertainties, and most modern synthetic biology design processes have focused to create robust components to mitigate the noise of gene expression and reduce the heterogeneity of single-cell responses. However, a deeper understanding of noise can achieve control goals that would otherwise be impossible. We explore how an "Optogenetic Maxwell Demon" could selectively amplify noise to control multiple cells using single-input-multiple-output (SIMO) feedback. Using data-constrained stochastic model simulations and theory, we show how an appropriately selected stochastic SIMO controller can drive multiple different cells to different user-specified configurations irrespective of initial conditions. We explore how controllability depends on cells' regulatory structures, the amount of information available to the controller, and the accuracy of the model used. Our results suggest that gene regulation noise, when combined with optogenetic feedback and non-linear biochemical auto-regulation, can achieve synergy to enable precise control of complex stochastic processes.