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Barua N, Herken AM, Melendez-Velador N, Platt TG, Hansen RR. Photo-addressable microwell devices for rapid functional screening and isolation of pathogen inhibitors from bacterial strain libraries. BIOMICROFLUIDICS 2024; 18:014107. [PMID: 38434239 PMCID: PMC10907074 DOI: 10.1063/5.0188270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 02/06/2024] [Indexed: 03/05/2024]
Abstract
Discovery of new strains of bacteria that inhibit pathogen growth can facilitate improvements in biocontrol and probiotic strategies. Traditional, plate-based co-culture approaches that probe microbial interactions can impede this discovery as these methods are inherently low-throughput, labor-intensive, and qualitative. We report a second-generation, photo-addressable microwell device, developed to iteratively screen interactions between candidate biocontrol agents existing in bacterial strain libraries and pathogens under increasing pathogen pressure. Microwells (0.6 pl volume) provide unique co-culture sites between library strains and pathogens at controlled cellular ratios. During sequential screening iterations, library strains are challenged against increasing numbers of pathogens to quantitatively identify microwells containing strains inhibiting the highest numbers of pathogens. Ring-patterned 365 nm light is then used to ablate a photodegradable hydrogel membrane and sequentially release inhibitory strains from the device for recovery. Pathogen inhibition with each recovered strain is validated, followed by whole genome sequencing. To demonstrate the rapid nature of this approach, the device was used to screen a 293-membered biovar 1 agrobacterial strain library for strains inhibitory to the plant pathogen Agrobacterium tumefaciens sp. 15955. One iterative screen revealed nine new inhibitory strains. For comparison, plate-based methods did not uncover any inhibitory strains from the library (n = 30 plates). The novel pathogen-challenge screening mode developed here enables rapid selection and recovery of strains that effectively suppress pathogen growth from bacterial strain libraries, expanding this microwell technology platform toward rapid, cost-effective, and scalable screening for probiotics, biocontrol agents, and inhibitory molecules that can protect against known or emerging pathogens.
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Affiliation(s)
- Niloy Barua
- Tim Taylor Department of Chemical Engineering, Kansas State University, 1701A Platt Street, Manhattan, Kansas 66506, USA
| | - Ashlee M. Herken
- Division of Biology, Kansas State University, 1717 Claflin Road, Manhattan, Kansas 66506, USA
| | | | - Thomas G. Platt
- Division of Biology, Kansas State University, 1717 Claflin Road, Manhattan, Kansas 66506, USA
| | - Ryan R. Hansen
- Tim Taylor Department of Chemical Engineering, Kansas State University, 1701A Platt Street, Manhattan, Kansas 66506, USA
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2
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Tanniche I, Behkam B. Engineered live bacteria as disease detection and diagnosis tools. J Biol Eng 2023; 17:65. [PMID: 37875910 PMCID: PMC10598922 DOI: 10.1186/s13036-023-00379-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/18/2023] [Indexed: 10/26/2023] Open
Abstract
Sensitive and minimally invasive medical diagnostics are essential to the early detection of diseases, monitoring their progression and response to treatment. Engineered bacteria as live sensors are being developed as a new class of biosensors for sensitive, robust, noninvasive, and in situ detection of disease onset at low cost. Akin to microrobotic systems, a combination of simple genetic rules, basic logic gates, and complex synthetic bioengineering principles are used to program bacterial vectors as living machines for detecting biomarkers of diseases, some of which cannot be detected with other sensing technologies. Bacterial whole-cell biosensors (BWCBs) can have wide-ranging functions from detection only, to detection and recording, to closed-loop detection-regulated treatment. In this review article, we first summarize the unique benefits of bacteria as living sensors. We then describe the different bacteria-based diagnosis approaches and provide examples of diagnosing various diseases and disorders. We also discuss the use of bacteria as imaging vectors for disease detection and image-guided surgery. We conclude by highlighting current challenges and opportunities for further exploration toward clinical translation of these bacteria-based systems.
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Affiliation(s)
- Imen Tanniche
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Bahareh Behkam
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA.
- School of Biomedical Engineered and Sciences, Virginia Tech, Blacksburg, VA, 24061, USA.
- Center for Engineered Health, Institute for Critical Technology and Applied Science, Virginia Tech, Blacksburg, VA, 24061, USA.
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Abstract
Microbial communities are complex living systems that populate the planet with diverse functions and are increasingly harnessed for practical human needs. To deepen the fundamental understanding of their organization and functioning as well as to facilitate their engineering for applications, mathematical modeling has played an increasingly important role. Agent-based models represent a class of powerful quantitative frameworks for investigating microbial communities because of their individualistic nature in describing cells, mechanistic characterization of molecular and cellular processes, and intrinsic ability to produce emergent system properties. This review presents a comprehensive overview of recent advances in agent-based modeling of microbial communities. It surveys the state-of-the-art algorithms employed to simulate intracellular biomolecular events, single-cell behaviors, intercellular interactions, and interactions between cells and their environments that collectively serve as the driving forces of community behaviors. It also highlights three lines of applications of agent-based modeling, namely, the elucidation of microbial range expansion and colony ecology, the design of synthetic gene circuits and microbial populations for desired behaviors, and the characterization of biofilm formation and dispersal. The review concludes with a discussion of existing challenges, including the computational cost of the modeling, and potential mitigation strategies.
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Affiliation(s)
- Karthik Nagarajan
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Congjian Ni
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Ting Lu
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,National Center for Supercomputing Applications, Urbana, Illinois 61801, United States
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Webster-Wood VA, Guix M, Xu NW, Behkam B, Sato H, Sarkar D, Sanchez S, Shimizu M, Parker KK. Biohybrid robots: recent progress, challenges, and perspectives. BIOINSPIRATION & BIOMIMETICS 2022; 18:015001. [PMID: 36265472 DOI: 10.1088/1748-3190/ac9c3b] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
The past ten years have seen the rapid expansion of the field of biohybrid robotics. By combining engineered, synthetic components with living biological materials, new robotics solutions have been developed that harness the adaptability of living muscles, the sensitivity of living sensory cells, and even the computational abilities of living neurons. Biohybrid robotics has taken the popular and scientific media by storm with advances in the field, moving biohybrid robotics out of science fiction and into real science and engineering. So how did we get here, and where should the field of biohybrid robotics go next? In this perspective, we first provide the historical context of crucial subareas of biohybrid robotics by reviewing the past 10+ years of advances in microorganism-bots and sperm-bots, cyborgs, and tissue-based robots. We then present critical challenges facing the field and provide our perspectives on the vital future steps toward creating autonomous living machines.
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Affiliation(s)
- Victoria A Webster-Wood
- Mechanical Engineering, Biomedical Engineering (by courtesy), McGowan Institute of Regenerative Medicine, Carnegie Mellon University, Pittsburgh, PA 15116, United States of America
| | - Maria Guix
- Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Baldiri-Reixac 10-12, 08028 Barcelona, Spain
- Departament de Ciència dels Materials i Química Física, Institut de Química Teòrica i Computacional Barcelona, Universitat de Barcelona, 08028 Barcelona, Spain
| | - Nicole W Xu
- Laboratories for Computational Physics and Fluid Dynamics, U.S. Naval Research Laboratory, Code 6041, Washington, DC, United States of America
| | - Bahareh Behkam
- Department of Mechanical Engineering, Institute for Critical Technology and Applied Science, Blacksburg, VA 24061, United States of America
| | - Hirotaka Sato
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 65 Nanyang Drive, Singapore, 637460, Singapore
| | - Deblina Sarkar
- MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Samuel Sanchez
- Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Baldiri-Reixac 10-12, 08028 Barcelona, Spain
- Catalan Institute for Research and Advanced Studies (ICREA), Avda. Lluis Companys 23, 08010 Barcelona, Spain
| | - Masahiro Shimizu
- Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-machi, Toyonaka, Osaka, Japan
| | - Kevin Kit Parker
- Disease Biophysics Group, Wyss Institute for Biologically Inspired Engineering and School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States of America
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Calibrating spatiotemporal models of microbial communities to microscopy data: A review. PLoS Comput Biol 2022; 18:e1010533. [PMID: 36227846 PMCID: PMC9560168 DOI: 10.1371/journal.pcbi.1010533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Spatiotemporal models that account for heterogeneity within microbial communities rely on single-cell data for calibration and validation. Such data, commonly collected via microscopy and flow cytometry, have been made more accessible by recent advances in microfluidics platforms and data processing pipelines. However, validating models against such data poses significant challenges. Validation practices vary widely between modelling studies; systematic and rigorous methods have not been widely adopted. Similar challenges are faced by the (macrobial) ecology community, in which systematic calibration approaches are often employed to improve quantitative predictions from computational models. Here, we review single-cell observation techniques that are being applied to study microbial communities and the calibration strategies that are being employed for accompanying spatiotemporal models. To facilitate future calibration efforts, we have compiled a list of summary statistics relevant for quantifying spatiotemporal patterns in microbial communities. Finally, we highlight some recently developed techniques that hold promise for improved model calibration, including algorithmic guidance of summary statistic selection and machine learning approaches for efficient model simulation.
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Vaiana CA, Kim H, Cottet J, Oai K, Ge Z, Conforti K, King AM, Meyer AJ, Chen H, Voigt CA, Buie CR. Characterizing chemical signaling between engineered "microbial sentinels" in porous microplates. Mol Syst Biol 2022; 18:e10785. [PMID: 35315586 PMCID: PMC8938921 DOI: 10.15252/msb.202110785] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/23/2022] [Accepted: 01/25/2022] [Indexed: 11/11/2022] Open
Abstract
Living materials combine a material scaffold, that is often porous, with engineered cells that perform sensing, computing, and biosynthetic tasks. Designing such systems is difficult because little is known regarding signaling transport parameters in the material. Here, the development of a porous microplate is presented. Hydrogel barriers between wells have a porosity of 60% and a tortuosity factor of 1.6, allowing molecular diffusion between wells. The permeability of dyes, antibiotics, inducers, and quorum signals between wells were characterized. A “sentinel” strain was constructed by introducing orthogonal sensors into the genome of Escherichia coli MG1655 for IPTG, anhydrotetracycline, L‐arabinose, and four quorum signals. The strain’s response to inducer diffusion through the wells was quantified up to 14 mm, and quorum and antibacterial signaling were measured over 16 h. Signaling distance is dictated by hydrogel adsorption, quantified using a linear finite element model that yields adsorption coefficients from 0 to 0.1 mol m−3. Parameters derived herein will aid the design of living materials for pathogen remediation, computation, and self‐organizing biofilms.
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Affiliation(s)
- Christopher A Vaiana
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hyungseok Kim
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jonathan Cottet
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Keiko Oai
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zhifei Ge
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kameron Conforti
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andrew M King
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Adam J Meyer
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Haorong Chen
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Christopher A Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cullen R Buie
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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Leaman EJ, Sahari A, Traore MA, Geuther BQ, Morrow CM, Behkam B. Data-driven statistical modeling of the emergent behavior of biohybrid microrobots. APL Bioeng 2020; 4:016104. [PMID: 32128471 PMCID: PMC7049295 DOI: 10.1063/1.5134926] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 02/10/2020] [Indexed: 12/19/2022] Open
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.
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Affiliation(s)
- Eric J. Leaman
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Ali Sahari
- School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Mahama A. Traore
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Brian Q. Geuther
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Carmen M. Morrow
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA
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Leaman EJ, Geuther BQ, Behkam B. Hybrid centralized/decentralized control of a network of bacteria-based bio-hybrid microrobots. JOURNAL OF MICRO-BIO ROBOTICS 2019. [DOI: 10.1007/s12213-019-00116-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Suh S, Jo A, Traore MA, Zhan Y, Coutermarsh‐Ott SL, Ringel‐Scaia VM, Allen IC, Davis RM, Behkam B. Nanoscale Bacteria-Enabled Autonomous Drug Delivery System (NanoBEADS) Enhances Intratumoral Transport of Nanomedicine. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2019; 6:1801309. [PMID: 30775227 PMCID: PMC6364498 DOI: 10.1002/advs.201801309] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 10/28/2018] [Indexed: 05/04/2023]
Abstract
Cancer drug delivery remains a formidable challenge due to systemic toxicity and inadequate extravascular transport of nanotherapeutics to cells distal from blood vessels. It is hypothesized that, in absence of an external driving force, the Salmonella enterica serovar Typhimurium could be exploited for autonomous targeted delivery of nanotherapeutics to currently unreachable sites. To test the hypothesis, a nanoscale bacteria-enabled autonomous drug delivery system (NanoBEADS) is developed in which the functional capabilities of the tumor-targeting S. Typhimurium VNP20009 are interfaced with poly(lactic-co-glycolic acid) nanoparticles. The impact of nanoparticle conjugation is evaluated on NanoBEADS' invasion of cancer cells and intratumoral transport in 3D tumor spheroids in vitro, and biodistribution in a mammary tumor model in vivo. It is found that intercellular (between cells) self-replication and translocation are the dominant mechanisms of bacteria intratumoral penetration and that nanoparticle conjugation does not impede bacteria's intratumoral transport performance. Through the development of new transport metrics, it is demonstrated that NanoBEADS enhance nanoparticle retention and distribution in solid tumors by up to a remarkable 100-fold without requiring any externally applied driving force or control input. Such autonomous biohybrid systems could unlock a powerful new paradigm in cancer treatment by improving the therapeutic index of chemotherapeutic drugs and minimizing systemic side effects.
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Affiliation(s)
- SeungBeum Suh
- Department of Mechanical EngineeringVirginia TechBlacksburgVA24061USA
| | - Ami Jo
- Department of Chemical EngineeringMacromolecules Innovation InstituteVirginia TechBlacksburgVA24061USA
| | - Mahama A. Traore
- Department of Mechanical EngineeringVirginia TechBlacksburgVA24061USA
| | - Ying Zhan
- Department of Mechanical EngineeringVirginia TechBlacksburgVA24061USA
| | | | | | - Irving C. Allen
- Department of Biomedical Sciences and PathobiologyVirginia TechBlacksburgVA24061USA
| | - Richey M. Davis
- Department of Chemical EngineeringMacromolecules Innovation InstituteVirginia TechBlacksburgVA24061USA
| | - Bahareh Behkam
- Department of Mechanical EngineeringVirginia TechBlacksburgVA24061USA
- Macromolecules Innovation InstituteSchool of Biomedical Engineering & SciencesVirginia TechBlacksburgVA24061USA
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