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Our laboratory takes a systems and synthetic biology approach to understanding and designing biology at multiple scales: proteins, microbial transcriptional regulation and metabolic pathways, and bacteriophages. We are interested in understanding the molecular basis of protein allostery, designing allosteric small molecule biosensors, understanding fundamental principles of bacterial transcription regulation and designing new transcriptional systems, and designing bacteriophages with new host specificities and regulation. To address these questions, we leverage computational protein design (Rosetta), next-generation DNA synthesis and sequencing, and highly multiplexed selection and screening assays. We apply machine learning principles and structural modeling on these large datasets to elucidate underlying relationships between sequence, structure and function and to improve design of new function.


Small molecule inducible transcription factors are widely used as biosensors. Protein-based biosensors are autonomous, self-powered, miniaturizable, and programmable macromolecules that function in both in vivo and ex vivo environments. Within a cell, biosensors report on the real-time metabolic state, allow dynamic control of pathways, accelerate design-build-test cycles for microbial biomanufacturing, and enable the construction of complex genetic circuits. Outside the cell, biosensors integrate with other technologies such as lab-on-a-chip, bioelectronics, microfabrication, and paper-based tools facilitating the development of low-cost, portable, wearable, analytical sensors with high specificity and quantitative output. The ability to design new proteins to accurately detect small molecule ligands would transform synthetic biology, medicine, environmental monitoring, bioremediation, and bioenergy. We aim to expand the repertoire to many new classes of small molecules. Our approach combines computational protein design, multiplexed cell-based screening and deep sequencing to functionally characterize designs, followed by machine learning to improve future rounds of design. We are currently developing biosensors for bacteria and yeast.


Allostery has a central place in biology due to the myriad roles of allosteric proteins in cellular function, including signal transduction, catalysis, metabolism, gene regulation and transport. Allostery is regulation from a distance – perturbation at one site of a protein causes an effect at a distant site. Once a gene is transcribed and translated, allostery is the primary mode of regulation of a protein function inside the cell. Allostery can be understood in simple terms as follows: when a protein is perturbed by binding to an effector (Eg.: small molecule, nucleic acid or another protein) at a site called the allosteric site, it triggers a change in conformation from inactive to active state that then regulates a distally located active site. This property is remarkable because enables two sites within a protein to communicate with each other despite being separated by a large distance. Since the discovery of allostery several decades back, the question that remains unanswered is what is the molecular mechanism by which distal sites communicate with each other? The goal of our research is to develop a data-driven framework, to understand, quantify, and predict molecular drivers of protein allostery by deep mutational scanning. We integrate large scale mutational screens with targeted NMR relaxation experiments to ‘observe’ residues undergoing motion in the micro-to-millisecond timescales to map the allosteric pathway and investigate the existence of multiple allosteric pathways in a protein.


Allosteric transcriptional repressors and activators regulate gene expression in microbes. Microbial TFs are ideal candidates for biosensors as they bind to diverse ligands including sugars, amino acids, aromatics, polyketides, and linear chain molecules. They either block or make protein-protein contacts with RNA polymerase to regulate transcription. The strength TF-promoter and TF-RNA polymerase interactions controls the transcriptional properties such as dynamic range, maximum transcription and cooperativity of response. Native promoters suffer from cryptic internal regulation, weak promoters that limit portability to new organisms, and lack of programmable control. We are developing multiplexed methods to design programmable promoters that enable microbial TFs to be easily ported across different organisms. We use machine learning to design new promoters with user-defined properties and to understand the underlying functional landscape of transcription.


The widespread use of xenobiotics, such as herbicides, industrial chemicals, plastics, explosive agents and antibiotics, is a major cause of environmental destruction. Xenobiotics deteriorate soil quality, disrupt delicate ecosystems, and profoundly affect human health through the food chain or water. Xenobiotics may persist for years in the environment because they are not easily degraded by environmental changes in pH or temperature, or by common catabolic pathways. However, some microbes in the environment have evolved xenobiotic-degrading enzymes. Since the vast majority of soil microbes cannot be cultured in a laboratory, these enzymes remain unidentified. We are developing a powerful alternative strategy by directly assaying the function of each of millions of enzyme candidates from environmental microbiota with an ultra high-throughput in vivo screen using biosensors. We will study evolution promiscuous activity, new enzymatic routes for bioremediation, with the objective of making superior bioremediation strains by combining pathways.  


Solving the antibiotic resistance crisis is one of the grand challenges of our time. Antibiotics are among the most widely used drugs in human and animal medicine and their excessive use is responsible for the emergence of bacterial resistance to antibiotics, a global health epidemic. We need to fundamentally rethink our approach to treating bacterial infection. Ideally, we seek an antimicrobial solution that transcends these limitations and co-evolves with bacteria to remain effective as bacteria eventually evolve resistance to antibiotic drugs. Nature provides an elegant solution: bacteriophages (or ‘phages’). Phages are viruses that specifically invade and destroy bacteria without harming eukaryotic cells. Phages are ‘evolvable’ drugs. When bacteria acquire resistance to a phage, we can engineer the phage to continue to remain effective by modifying phage-host receptor interactions or by neutralizing other mechanisms that cause resistance. We are developing a broad technology platform for designing customized phages to kill pathogenic microbes using synthetic biology tools, with a focus on developing phages against urinary tract infection.


Microbiome is a community of microorganisms associated with every living creature and environment on earth, that play a crucial role in human health, agriculture, and global nutrient cycles. Microbial communities exist in complex and highly interconnected networks of interactions that is not fully understood. Advances in “omics” technologies have facilitated the survey of microbiomes under different environments. However, discerning the role of specific members in the community from omics data is difficult. Therefore, we need to develop tools precisely edit the composition of a microbial community in situ. Precision tools to edit a microbiome will enable a quantitative understanding of how interactions of individual microbes shape community structure, stability and response to perturbations. Current approaches to modifying the composition of a microbiome in situ (antibiotics, mobile DNA, nutrition and microbiota transplant) lack the precision necessary for species-specific editing. We are developing tools to design a panel of obligate lytic phages to precisely edit a microbial community by eliminating specific microbial species. This approach can be modified to deliver genetic payloads to specific members within a community.

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