Micro-and-Nano-Scale Robotics

Miniature and energy efficient propulsion systems hold the key to maturing the technology of swimming microrobots. In this paper, two new methods of propulsion inspired by the motility mechanism of prokaryotic and eukaryotic microorganisms are proposed. Hydrodynamic models for each of the two methods are developed and the optimized design paramteres for each of the two propulsion modes are demonstrated. To validate the theoretical result for the prokaryotic flagellar motion, a scaled up prototype of the robot is fabricated and tested in silicone oil using the Buckingham PI theorem for scaling. The proposed propulsion methods are appropriate for the swimming robots which are intended to swim in low velocity fluids. Keywords–Microrobotics, biomimetic robotics, prokaryotic flagellar motion, eukaryotic flagellar motion

IMECE2008-68032 DRAFT: DESIGN AND NUMERICAL MODELING OF AN ON-BOARD CHEMICAL RELEASE MODULE FOR MOTION CONTROL OF BACTERIA-PROPELLED SWIMMING MICRO-ROBOTS mine the number, size, and location of the required micro-valves

ABSTRACT On/off motion control of bacteria-propelled synthetic bodies was previously achieved using a chemical switching technique. A chemical agent (CuSO 4 ) directly binds to the rotor of the flagellar motor inhibiting it. When desired, a second chemical agent (EDTA) is introduced, which binds to the CuSO 4 molecules, freeing the motor and allowing the bacteria to resume its motio

Quantitative Investigation of the Role of Intra-/Intercellular Dynamics in Bacterial Quorum Sensing

Bacteria utilize diffusible signals to regulate population density-dependent coordinated gene expression in a process called quorum sensing (QS). While the intracellular regulatory mechanisms of QS are well-understood, the effect of spatiotemporal changes in the population configuration on the sensitivity and robustness of the QS response remains largely unexplored. Using a microfluidic device, we quantitatively characterized the emergent behavior of a population of swimming <i>E. coli</i> bacteria engineered with the <i>lux</i> QS system and a GFP reporter. We show that the QS activation time follows a power law with respect to bacterial population density, but this trend is disrupted significantly by microscale variations in population configuration and genetic circuit noise. We then developed a computational model that integrates population dynamics with genetic circuit dynamics to enable accurate (less than 7% error) quantitation of the bacterial QS activation time. Through modeling and experimental analyses, we show that changes in spatial configuration of swimming bacteria can drastically alter the QS activation time, by up to 22%. The integrative model developed herein also enables examination of the performance robustness of synthetic circuits with respect to growth rate, circuit sensitivity, and the population’s initial size and spatial structure. Our framework facilitates quantitative tuning of microbial systems performance through rational engineering of synthetic ribosomal binding sites. We have demonstrated this through modulation of QS activation time over an order of magnitude. Altogether, we conclude that predictive engineering of QS-based bacterial systems requires not only the precise temporal modulation of gene expression (intracellular dynamics) but also accounting for the spatiotemporal changes in population configuration (intercellular dynamics)

Quantitative Investigation of the Role of Intra-/Intercellular Dynamics in Bacterial Quorum Sensing

Bacteria utilize diffusible signals to regulate population density-dependent coordinated gene expression in a process called quorum sensing (QS). While the intracellular regulatory mechanisms of QS are well-understood, the effect of spatiotemporal changes in the population configuration on the sensitivity and robustness of the QS response remains largely unexplored. Using a microfluidic device, we quantitatively characterized the emergent behavior of a population of swimming <i>E. coli</i> bacteria engineered with the <i>lux</i> QS system and a GFP reporter. We show that the QS activation time follows a power law with respect to bacterial population density, but this trend is disrupted significantly by microscale variations in population configuration and genetic circuit noise. We then developed a computational model that integrates population dynamics with genetic circuit dynamics to enable accurate (less than 7% error) quantitation of the bacterial QS activation time. Through modeling and experimental analyses, we show that changes in spatial configuration of swimming bacteria can drastically alter the QS activation time, by up to 22%. The integrative model developed herein also enables examination of the performance robustness of synthetic circuits with respect to growth rate, circuit sensitivity, and the population’s initial size and spatial structure. Our framework facilitates quantitative tuning of microbial systems performance through rational engineering of synthetic ribosomal binding sites. We have demonstrated this through modulation of QS activation time over an order of magnitude. Altogether, we conclude that predictive engineering of QS-based bacterial systems requires not only the precise temporal modulation of gene expression (intracellular dynamics) but also accounting for the spatiotemporal changes in population configuration (intercellular dynamics)

Motion Enhanced Multi‐Level Tracker (MEMTrack): A Deep Learning‐Based Approach to Microrobot Tracking in Dense and Low‐Contrast Environments

Tracking microrobots is challenging due to their minute size and high speed. In biomedical applications, this challenge is exacerbated by the dense surrounding environments with feature sizes and shapes comparable to microrobots. Herein, Motion Enhanced Multi‐level Tracker (MEMTrack) is introduced for detecting and tracking microrobots in dense and low‐contrast environments. Informed by the physics of microrobot motion, synthetic motion features for deep learning‐based object detection and a modified Simple Online and Real‐time Tracking (SORT)algorithm with interpolation are used for tracking. MEMTrack is trained and tested using bacterial micromotors in collagen (tissue phantom), achieving precision and recall of 76% and 51%, respectively. Compared to the state‐of‐the‐art baseline models, MEMTrack provides a minimum of 2.6‐fold higher precision with a reasonably high recall. MEMTrack's generalizability to unseen (aqueous) media and its versatility in tracking microrobots of different shapes, sizes, and motion characteristics are shown. Finally, it is shown that MEMTrack localizes objects with a root‐mean‐square error of less than 1.84 μm and quantifies the average speed of all tested systems with no statistically significant difference from the laboriously produced manual tracking data. MEMTrack significantly advances microrobot localization and tracking in dense and low‐contrast settings and can impact fundamental and translational microrobotic research