Data-driven statistical modeling of the emergent behavior of biohybrid microrobots

Abstract

Multi-agent biohybrid microrobotic systems, owing to their small size and distributed nature, offer powerful solutions to challenges in biomedicine, bioremediation, and biosensing. Synthetic biology enables programmed emergent behaviors in the biotic component of biohybrid machines, expounding vast potential benefits for building biohybrid swarms with sophisticated control schemes. The design of synthetic genetic circuits tailored toward specific performance characteristics is an iterative process that relies on experimental characterization of spatially homogeneous engineered cell suspensions. However, biohybrid systems often distribute heterogeneously in complex environments, which will alter circuit performance. Thus, there is a critically unmet need for simple predictive models that describe emergent behaviors of biohybrid systems to inform synthetic gene circuit design. Here, we report a data-driven statistical model for computationally efficient recapitulation of the motility dynamics of two types of Escherichia coli bacteria-based biohybrid swarms—NanoBEADS and BacteriaBots. The statistical model was coupled with a computational model of cooperative gene expression, known as quorum sensing (QS). We determined differences in timescales for programmed emergent behavior in BacteriaBots and NanoBEADS swarms, using bacteria as a comparative baseline. We show that agent localization and genetic circuit sensitivity strongly influence the timeframe and the robustness of the emergent behavior in both systems. Finally, we use our model to design a QS-based decentralized control scheme wherein agents make independent decisions based on their interaction with other agents and the local environment. We show that synergistic integration of synthetic biology and predictive modeling is requisite for the efficient development of biohybrid systems with robust emergent behaviors.

Biohybrid robots: recent progress, challenges, and perspectives

“…Moreover, hybrid control schemes combining centralized and decentralized control strategies can be advantageous. For instance, in the case of targeted drug delivery applications, a decentralized control scheme can be used to prevent off-target cargo release by agents that failed to respond to the centralized control signal to reach the site of interest [61].…”

“…When a critical signal concentration is reached, the circuit exhibits a switch-like behavior to an 'activated' state of high signal enzyme production, as well as the transcription of any other downstream genes [64][65][66][67][68][69]. We recently demonstrated that synthetic QS circuits could be used to address the need for engineering sensitive and robust decentralized emergent behaviors in populations of biohybrid microrobots [61,70]. Synthetic biology has also been used to impart other functions, such as creating biohybrid microrobotic sensors [71] and light-based control of cargo release by microrobots [72].…”

“…In modeling microorganism bots, design and modeling tools need to be able to capture not only the effect of actuation on an individual's motion but also the complex interactions observed in microorganism swarms. Data-driven methods are emerging as tools to provide statistical modeling of biohybrid robots but often require substantial datasets to generate [61].…”

“…chemotaxis or QS) in biohybrid robotic platforms, would be highly beneficial for the community. Such frameworks are particularly advantageous if they can operate on limited experimental data and are generalizable beyond the system for which they are developed [61].…”

“…Alternatively, biological closedloop control systems, e.g. QS in bacteria, can be engineered [197] to achieve robust and predictable response from swarms of microbial biohybrids [61]. These capabilities also rely on advancements in modeling and artificial intelligence, with one goal to create biohybrid robots driven by autonomous control strategies.…”