Microfluidics are widely used in research ranging from bioengineering and biomedical disciplines to chemistry and nanotechnology. As such, there are a large number of options for the devices used to drive and control flow through microfluidic channels. Commercially available syringe pumps are probably the most commonly used instruments for this purpose, but are relatively high-cost and have inherent limitations due to their flow profiles when they are run open-loop. Here, we present a low-cost ($110) syringe pressure pump that uses feedback control to regulate the pressure into microfluidic chips. Using an open-source microcontroller board (Arduino), we demonstrate an easily operated and programmable syringe pump that can be run using either a PID or bang-bang control method. Through feedback control of the pressure at the inlets of two microfluidic geometries, we have shown stability of our device to within ±1% of the set point using a PID control method and within ±5% of the set point using a bang-bang control method with response times of less than 1 second. This device offers a low-cost option to drive and control well-regulated pressure-driven flow through microfluidic chips.
We have developed synthetic gene networks that enable engineered cells to selectively program surface chemistry. E. coli were engineered to upregulate biotin synthase, and therefore biotin synthesis, upon biochemical induction. Additionally, two different functionalized surfaces were developed that utilized binding between biotin and streptavidin to regulate enzyme assembly on programmable surfaces. When combined, the interactions between engineered cells and surfaces demonstrated that synthetic biology can be used to engineer cells that selectively control and modify molecular assembly by exploiting surface chemistry. Our system is highly modular and has the potential to influence fields ranging from tissue engineering to drug development and delivery.
The microbiome’s underlying dynamics play an important role in regulating the behavior and health of its host. In order to explore the details of these interactions, we created an in silico model of a living microbiome, engineered with synthetic biology, that interfaces with a biomimetic, robotic host. By analytically modeling and computationally simulating engineered gene networks in these commensal communities, we reproduced complex behaviors in the host. We observed that robot movements depended upon programmed biochemical network dynamics within the microbiome. These results illustrate the model’s potential utility as a tool for exploring inter-kingdom ecological relationships. These systems could impact fields ranging from synthetic biology and ecology to biophysics and medicine.
Synthetic biology holds significant potential in biomaterials science as synthetically engineered cells can produce new biomaterials, or alternately, can function as living components of new biomaterials. Here, we describe the creation of a new biomaterial that incorporates living bacterial constituents that interact with their environment using engineered surface display. We first developed a gene construct that enabled simultaneous expression of cytosolic mCherry and a surface-displayed, catalytically active enzyme capable of covalently bonding with benzylguanine (BG) groups. We then created a functional living material within a microfluidic channel using these genetically engineered cells. The material forms when engineered cells covalently bond to ambient BG-modified molecules upon induction. Given the wide range of materials amenable to functionalization with BG-groups, our system provides a proof-of-concept for the sequestration and assembly of BG-functionalized molecules on a fluid-swept, living biomaterial surface.
Biomimetic robots have been used to explore and explain natural phenomena ranging from the coordination of ants to the locomotion of lizards. Here, we developed a series of decision architectures inspired by the information exchange between a host organism and its microbiome. We first modeled the biochemical exchanges of a population of synthetically engineered E. coli. We then built a physical, differential drive robot that contained an integrated, onboard computer vision system. A relay was established between the simulated population of cells and the robot's microcontroller. By placing the robot within a target-containing a two-dimensional arena, we explored how different aspects of the simulated cells and the robot's microcontroller could be integrated to form hybrid decision architectures. We found that distinct decision architectures allow for us to develop models of computation with specific strengths such as runtime efficiency or minimal memory allocation. Taken together, our hybrid decision architectures provide a new strategy for developing bioinspired control systems that integrate both living and nonliving components.
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