Our research group uses a combination of interdisciplinary approaches including chemical engineering, synthetic biology, systems biology, molecular biology, microbiology, metabolic engineering and computational biology to address key global challenges including medical and energy needs. We are interested in adopting an integrated mathematical modeling and experimental approach to investigate fundamental and medically relevant issues such as understanding the molecular mechanisms responsible for antibiotic and antiviral resistance, and for developing “next-generation smart antimicrobials” by rationally engineering novel therapeutics that target essential bacterial/viral genes in a potentially resistance-free manner. We study genetic regulatory networks that control propagation of infectious diseases, with the goal of discovering novel drug targets for therapy. Using synthetic biology tools we design, construct and engineer modular synthetic genetic devices that can achieve higher-order biological computation, for variety of biotechnological and bioenergy applications. To this end, we engineer biological parts such as transcription factors, promoter sequences, receptors, feedback loops, and regulatory RNA to build complex genetic networks that can be used to optimize cellular machinery for production of bio-fuels and pharmaceuticals, and for gene therapy applications. Using these genetic devices, we apply systems biology approaches to understand functioning of complex genetic networks and to build rules to manipulate such networks.
In the Spotlight
New Metabolic Flux Analysis tool "Constrictor" developed by our lab is available on Opensource
Advances in computational methods that allow for exploration of the combinatorial mutation space are needed to realize the potential of synthetic biology based strain engineering efforts. We have developed a computational framework called Constrictor that uses flux balance analysis (FBA) to analyze inhibitory effects of genetic mutations on the performance of biochemical networks. This algorithm is designed to easily implement finely tuned reductions in flux through individual or multiple reactions in a combinatorial manner. We demonstrate the use of this tool by modeling the overproduction of ethylene in Escherichia coli. Our method uncovers novel gene targets that improve in silico ethylene yield. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants, select gene expression levels, and troubleshoot metabolic networks. Link
"CONSTRICTOR: Constraint modification provides insight into design of biochemical networks." PLoS ONE 9(11):e113820. doi:10.1371/journal.pone.0113820.
CU recognizes Chatterjee and Nagpal labs for developing a new way of sequencing and detecting drug-resistant bacteria
CU Technology Transfer Office has recongnized Quantum Sequencing technology developed by our and Nagpal lab with the New Inventor of the Year award. We are developing a platform technology for fast, reliable, high-throughput and cost effective single molecule sequencing of nulciec acids.This kind of sequencing is an important step in the developement of new diagnostic tools for personalized
medicine, as well as in rapid identification of DNA sequences that allow bacteria to develop drug resistance. This approach can potentially transform how we detect drug resistant pathogens in the clinical setting, will allow faster diagnostics and early detection of drug resistant strains that can prevent future spread of resistance, and will also reduce the cost of diagnostics.Read more | The Denver post | ChBE