Negatively Competitive Incoherent Feedforward Loops Mitigate Winner-Take-All Resource Competition

Abstract

The effects of host resource limitations on the function of synthetic gene circuits have gained significant attention over the past years. Hosts, having evolved resource capacities optimal for their own genome, have been repeatedly demonstrated to suffer from the added burden of synthetic genetic programs, which may in return pose deleterious effects on the circuit’s function. Three resource controller archetypes have been proposed previously to mitigate resource distribution problems in dynamic circuits: the local controller, the global controller, and a “negatively competitive” regulatory (NCR) controller that utilizes synthetic competition to combat resource competition. The dynamics of negative feedback forms of these controllers have been previously investigated, and here we extend the analysis of these resource allocation strategies to the incoherent feedforward loop (iFFL) topology. We demonstrate that the three iFFL controllers can attenuate Winner-Take-All resource competition between two bistable switches. We uncover that the parameters associated with the synthetic competition in the NCR iFFL controller are paramount to its increased efficacy over the local controller type, while the global controllers demonstrate to be relatively ineffectual. Interestingly, unlike the negative feedback counterpart topologies, iFFL controllers exhibit a unique coupling of switch activation thresholds which we term the “coactivation threshold shift” effect. Finally, we demonstrate that a nearly fully orthogonal set of bistable switches could be achieved by pairing an NCR controller with an appropriate level of controller resource consumption.

Walk This Way: Modeling Foraging Ant Dynamics in Multiple Food Source Environments

“…Such systems are called “toggles” [71], as varying key biophysical parameters can shift the equilibrium to a state where the other protein persists. Winner-take-all mechanisms are also present in learning in neural networks [44, 46] and synthetic gene circuits [67], among many other systems.…”

Noise Reduction in Resource-Coupled Multi-Module Gene Circuits through Antithetic Feedback Control

“…The first terms in these are the production rates of mRNA, controller mRNA, and protein with maximum rate v mj , v cj , v pj respectively. Here P F m and P F p define the effects of resource competition in transcription and translational processes as in our previous works [12,26,27],…”

“…The first terms in these are the production rates of mRNA, controller mRNA, and protein with maximum rate v mj , v cj , v pj respectively. Here PF m and PF p define the effects of resource competition in transcription and translational processes as in our previous works [12,26,27], R mj and R cj are the fractions of active promoters for the mRNA and controller mRNA respec-tively. While R mj is a constant, as the controller mRNA is regulated by protein, where n is the Hill coefficient and K cj represents the protein binding affinity.…”

“…These works have demonstrated the effectiveness of these mechanisms in controlling one of the single modules in the system. Interestingly, multi-module control strategies have been proposed to alleviate winner-take-all resource competition [26, 27].…”