This project aims to increase the scalability of synthetic-biology debugging systems that could have broad applications in extending capabilities for cell-based sensors and actuators in medicine and agriculture, as well as for addition to new knowledge generation in molecular-biology research. This will be accomplished by building an end-to-end biomolecular-silicon hybrid system. Broadly, the resulting system will: (1) explore new ways to build hybrid bio-molecular silicon computer systems by integrating bio-molecular storage and computing with electronics, and (2) automate closed-loop synthetic biology design-build-test-learn processes. Importantly, this project will also train students and professionals in the intersection of biology and computing, which is a very promising new area of scientific and economic development. Results from this work will also be incorporated into new course materials at the intersection of embedded computing/fluidics, molecular biology and machine learning, with the goal of training students to quickly prototype ideas in molecular computing/synthetic biology.
The investigators will design and build scalable bio-molecular computing and debugging tools on top of a closed-loop system that integrates a nanopore sensor array for biomolecule-to-digital read-out (DNA and protein) with a DNA synthesizer for digital-to-biomolecule interface, automated with a digital/droplet microfluidic system and an integrated programming model (PurpleDrop+Puddle). The digital fluidics system will employ computer-vision techniques for reliable control of droplet movements. The investigators will develop machine-learning techniques to analyze raw nanopore sensor data for low-cost and high-throughput identification of molecular outputs. Specifically, the system will achieve two main objectives. The first objective will combine DNA synthesis and digital microfluidics for assembly of biological parts to implement automated cloning, in-line with DNA sequencing-based quality control. The second objective is transcriptional circuit auto-tuning, which will carry out automated characterization and parallel component debugging/tuning of designed in-vivo transcriptional circuits using a library of new genetic reporter proteins. For each objective, the nanopore sensor array will read-out the molecular results (DNA or protein); the control system (Puddle) will then interpret the data and determine the next fluidic manipulation actions as well as the production of additional synthetic DNA to guide the design or discovery process.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
