Fundamental and applied synthetic biology
Through the careful application of genetic engineering, synthetic biological systems can be constructed to harness biotransformation for useful purposes. In an era of genome-scale DNA assembly, the lack of technologies to design and rapidly implement biological devices and systems with predictable behaviors is striking. We are interested in developing design-driven engineering approaches to build bigger and more complex systems. In our work, we combine biochemical and biophysical modeling, computational design and analysis, in vitro selection, and genetic engineering in microbial hosts and synthetic cell systems. We organize our efforts around applications to address unmet needs for renewable chemicals and to produce drugs, and other materials for health.
Metabolic control system design
Metabolic engineering introduces stresses to the host resulting from the unregulated consumption of resources, accumulation of toxic products, and non-specific substrate utilization. In nature, metabolic control problems are solved with dynamic regulatory functions that couple chemical inputs to specific outputs that modulate enzymatic activities and program responses to changing cellular and environmental conditions. In principle, it should be possible to increase the sizes and complexities of engineered biological systems by engineering dynamic genetic controls that automatically balance resource supplies and demands. In practice, genetic control system design spaces are enormous and very little is known about the functional constraints on dynamic metabolic control circuitry. We have developed computational approaches to perform large scale simulation analysis that can be used to identify control system designs predicted to improve production titers, illustrating how techniques applied from data science can drive the design of complex genetic control systems. Looking ahead, integrating advanced computational simulations with massively-scaled experimental analysis will allow us to test RNA-based genetic control system design limits and uncover architectures to improve biosynthesis titers, rates and yields.
CRISPR-Cas transcriptional programs
Dramatic successes in bioproduction have been achieved through extensive and laborious efforts to optimize pathway functions and re-engineer multi-gene regulatory networks in microbial hosts. To increase the speed and efficacy with which complex, multi-gene expression programs can be created, we need methods to easily target and manipulate individual genes. The recent development of the CRISPR-Cas system as an engineering platform has resulted in widely-used tools for programmable transcriptional inhibition (CRISPRi) and activation (CRISPRa). Importantly, these approaches can be applied to target multiple genes based on predictable Watson-Crick base pairing by expressing guide RNAs (gRNAs) for each gene of interest. We have developed architectures for expressing multiple gRNAs to build synthetic information processing circuits and created strategies to perform CRISPRa and CRISPRi concurrently and independently in bacteria the same way as in yeast. We have also established new design rules suggesting that dynamic, multi-gene CRISPR-Cas transcriptional networks and systems will soon be achievable. Combining such tools with our aptamer-based technologies for high-throughput screens and for dynamic genetic control will create capabilities for rapidly identifying efficient bioproduction programs.
Quantitative RNA aptamer device design
In an era of genome-scale DNA assembly, there remains a striking lack of technologies for designing biological systems to meet targeted functional criteria. We have formulated design-driven approaches that combine mechanistic modeling and kinetic RNA folding simulations to engineer static and dynamic, metabolite-controlled genetic regulators with quantitatively-predictable functions. We have also shown that RNA aptamer-based biosensors can be engineered using a novel approach for multi-state RNA folding design. The resulting devices can measure metabolic production directly from engineered microbial cultures. This new class of biosensors can be used to optimize bioproduction using high-throughput analysis and data driven workflows. Ultimately, we hope the further development of our work in RNA engineering may permit the fully-rational design of RNA aptamer devices for customized biosensing or genetic control
Cell-free synthetic biology
The bottom-up construction of synthetic cell systems (SCSs) that mimic specific biological functions is arising as a promising approach for reproducible biomanufacturing. Cell-free transcription-translation (TXTL) has emerged as one of the most robust and popular experimental technologies to prototype SCSs capable of recapitulating enzymatic reaction cascades. The major advantage of the TXTL-based SCS approach is to enable the construction of SCSs that are genetically programmable, facilitating the integration of biological functions, especially those that require membrane proteins. Working with collaborators, we are integrating emerging technologies for membrane-based TXTL and CRISPR-Cas genetic control with enzyme discovery and multi-gene pathway engineering. Entirely new classes of bio-nano material functions would be enabled by the development of cell-free membrane-based systems, including for drug delivery, fundamental studies, and biocatalysis. The immediate result of our work will be the development of cell-free platforms that emulate plant cell environments and enable rapid prototyping, bioprospecting, and optimization of plant natural product biosynthesis. Eventually, we believe that these efforts could generate foundational knowledge that transforms the field of biomanufacturing.