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

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dc.contributor.authorLeaman, Eric J.en
dc.contributor.authorSahari, Alien
dc.contributor.authorTraore, Mahama Azizen
dc.contributor.authorGeuther, Brian Q.en
dc.contributor.authorMorrow, Carmen M.en
dc.contributor.authorBehkam, Baharehen
dc.contributor.departmentMechanical Engineeringen
dc.contributor.departmentSchool of Biomedical Engineering and Sciencesen
dc.date.accessioned2020-05-13T19:15:12Zen
dc.date.available2020-05-13T19:15:12Zen
dc.date.issued2020-03-01en
dc.description.abstractMulti-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.en
dc.description.notesThe authors would like to thank our colleagues in the MicroN BASE laboratory at Virginia Tech, especially SeungBeum Suh and Ying Zhan. This project was partially supported by the National Science Foundation (Nos. IIS-117519 and CAREER award, CBET-1454226) and the Institute for Critical Technology and Applied Science (ICTAS) at Virginia Tech.en
dc.description.sponsorshipNational Science FoundationNational Science Foundation (NSF) [IIS-117519]; National Science Foundation (CAREER award)National Science Foundation (NSF) [CBET-1454226]; Institute for Critical Technology and Applied Science (ICTAS) at Virginia Techen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1063/1.5134926en
dc.identifier.issn2473-2877en
dc.identifier.issue1en
dc.identifier.other16104en
dc.identifier.pmid32128471en
dc.identifier.urihttp://hdl.handle.net/10919/98250en
dc.identifier.volume4en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleData-driven statistical modeling of the emergent behavior of biohybrid microrobotsen
dc.title.serialAPL Bioengineeringen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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Scholarly Works, Mechanical Engineering
Destination Area: Data and Decisions (D&D)
Scholarly Works, School of Biomedical Engineering and Sciences