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

Eric J Leaman,Mahama A Traore,Carmen M Morrow,Bahareh Behkam,Brian Q Geuther,Ali Sahari

doi:10.1063/1.5134926

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.

Highlights

Microrobots defined as autonomous or semi-autonomous systems with characteristic dimensions O (1 lm) were envisioned as valuable non-invasive tools for medical intervention well before they became technologically feasible.1 Recent decades have seen a myriad of microrobotic concepts and prototypes developed, mainly for medical applications.2,3 The most significant challenges in developing such systems are incorporating effective mechanisms for actuation, sensing, and control, all without the need for an onboard power source
We show that synergistic integration of synthetic biology and predictive modeling is requisite for the efficient development of biohybrid systems with robust emergent behaviors
We integrated this computationally efficient model with an experimentally validated computational model of quorum sensing (QS) to simulate the emergent behavior in populations of biohybrid cargo-carrying agents, BacteriaBots and NanoBEADS, and compared the results to free-swimming engineered bacteria

Summary

Introduction

Microrobots defined as autonomous or semi-autonomous systems with characteristic dimensions O (1 lm) were envisioned as valuable non-invasive tools for medical intervention well before they became technologically feasible. Recent decades have seen a myriad of microrobotic concepts and prototypes developed, mainly for medical applications. The most significant challenges in developing such systems are incorporating effective mechanisms for actuation, sensing, and control, all without the need for an onboard power source. Recent decades have seen a myriad of microrobotic concepts and prototypes developed, mainly for medical applications.. The most significant challenges in developing such systems are incorporating effective mechanisms for actuation, sensing, and control, all without the need for an onboard power source. Biology has offered great solutions to these challenges both as a source for design inspiration and, more prominently, by the incorporation of biological materials themselves as part of the microrobotic systems.. Designs have incorporated eukaryotic cells or unicellular organisms, such as algae, spermatozoa, macrophages, and cardiomyocytes but more commonly have relied upon swimming bacteria as actuators.. Bacteria efficiently transduce chemical energy from their environment into kinetic energy for self-propulsion and possess robust mechanisms to sense a wide range of environmental stimuli including chemical, optical, thermal, or magnetic. Biology has offered great solutions to these challenges both as a source for design inspiration and, more prominently, by the incorporation of biological materials themselves as part of the microrobotic systems. Designs have incorporated eukaryotic cells or unicellular organisms, such as algae, spermatozoa, macrophages, and cardiomyocytes but more commonly have relied upon swimming bacteria as actuators. Bacteria efficiently transduce chemical energy from their environment into kinetic energy for self-propulsion and possess robust mechanisms to sense a wide range of environmental stimuli including chemical, optical, thermal, or magnetic.

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