HIL is widely used in industries where testing and optimizing the embedded controller in its final application setting is infeasible or very expensive, such as in the automotive or aerospace industries. However, when the target system to be controlled is not quantitatively defined, this approach can lead to large discrepancies between predictions and experimental outcomes.Īn engineering strategy commonly used to develop complex, real-time embedded controllers is hardware-in-the-loop (HIL), where the controller being designed is interfaced with a realistic simulation of the system it should steer. Mathematical modeling is usually employed to identify and alleviate these issues. The translation of circuit specifications to biomolecular realizations is non-trivial and depends on component availability, characterization, and cross-talk, among others 10, 32. Theoretical implementations of integral controllers solely employing chemical reactions (biomolecular controllers) have been proposed 20, 21, 22, 23, 24, 25, 26, but experimental demonstrations remain challenging, with only a few examples of integral (or quasi-integral) implementations (in vivo implementations in 27, 28, 29, 30 in vitro implementation in 31). To achieve this feature, integral action is required 19. However, it is worth noting that previous implementations of in vivo feedback regulation were not capable of perfect adaptation, i.e., convergence to a constant activity level regardless of external disturbances. The properties of feedback regulation have previously been exploited in synthetic circuits to increase bioprocess yields 15, 16, 17, or circuit robustness 18. A more general approach to engineer robustness is to control the circuit’s critical components with feedback regulation, a strategy commonly used in endogenous biological systems 12, 13, 14. Circuit reliability can be improved by means of network architecture 11. There are different strategies to mitigate the effect of host and environment context. Furthermore, synthetic circuits are usually developed in a context different to that of their end-application, and the circuit’s transplantation to new environments comes with complications: changes in culture conditions or host can significantly degrade circuit performance 10. However, synthetic circuit construction still presents serious challenges due to unanticipated cross-talk between parts, loading and burden effects, operation in a cellular environment which is inherently stochastic, and long design-cycle time periods, among other reasons. Recent years have seen a surge of synthetic circuits applied to biotechnology 8 and medicine 9. Propelled by advancements in DNA synthesis, laboratory automation, and a growing repository of characterized biological parts, synthetic biology is starting to bear fruit 1, 2, 3, 4, 5, 6, 7. From this analysis, we derive conditions for desirable biomolecular controller performance, thereby avoiding pitfalls during its biological implementation. Applying this framework to yeast cells engineered with optogenetic tools, we examine and characterize different biomolecular controllers, test the impact of non-ideal circuit behaviors such as dilution on their operation, and qualitatively demonstrate improvements in controller function with certain network modifications. Cellular fluorescence measurements are sent in real-time to a computer simulating candidate stochastic controllers, which in turn compute the control inputs and feed them back to the controlled cells via light stimulation. Here, we present the Cyberloop, a testing framework to accelerate the design process and implementation of biomolecular controllers.
However, their practical implementation presents challenges due to low predictability of synthetic circuit design and time-intensive troubleshooting. The design and implementation of synthetic circuits that operate robustly in the cellular context is fundamental for the advancement of synthetic biology.
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