Deepanwita Banerjee, PhD, is currently working on computationally driven host engineering for growth-coupled production of biofuels and bioproducts using the Product Substrate Pairing (PSP) approach. She uses computational strain optimization methods and genome scale metabolic models (GSM) to predict gene targets for engineering Pseudomonas putida. She is also working towards predicting PSP strategies in P. putida for utilization of lignocellulose derived carbon sources for bioenergy and biomanufacturing applications. The goal is to test and learn to better design robust and sustainable microbial hosts that maintain desirable phenotype across industrially relevant scales and conditions.
Deepanwita Banerjee’s research interests include understanding microbial systems for desirable phenotypes using multi-omics integrated systems biology approach. The applications are diverse ranging from improving production of value-added products or assessing microenvironments for a redox imbalance in vivo. She is also interested in understanding the emergent properties of a microbial hosts that are a result of the complex relationship between metabolism and transcriptional regulation. Currently there are digital resources available for model microbes that help inform such relationships but is lacking for non-model microbial systems. Genome-scale network reconstruction of a microbial system requires integration of more than one layer of biological networks (e.g., metabolism, regulation or signaling) as constraints to improve prediction power of such computational models for desirable phenotypes such as growth coupled production or maximum bioconversion of carbon sources.