Dr Somenath Bakshi

Associate Professor at Department of Engineering, University of Cambridge

Share
Associate Professor, Department of Engineering, University of Cambridge

Somenath leads the Bakshi Laboratory of Systems and Synthetic Microbiology, which focuses on understanding antibiotic response and resistance in bacteria and developing diagnostic and therapeutic strategies to combat antibiotic resistance.

The lab's approach integrates systems biology methods with synthetic biology techniques. We conduct model-driven experiments and utilise data-driven models to explore how various physiological factors influence antibiotic response and resistance. Specifically, we investigate how physiological differences among microbes affect the population-level response and contribute to the emergence of resistance. To gather data, we employ high-throughput time-resolved microscopy coupled with machine learning algorithms. Machine learning techniques further aid in identifying relevant features for model development. In parallel, we employ synthetic biology tools, high-throughput screening, and laboratory evolution experiments to design and optimise bacterial and viral systems for combating resistant infections. By leveraging our systems-level understanding of the problem and synthetic biology tools, we aim to develop innovative solutions for addressing antibiotic resistance challenges.

Somenath completed his PhD in University of Wisconsin Madison under Professor James Weisshaar – developing super-resolution imaging technologies to study central cellular processes in microbes. Next, he moved to Harvard University for his postdoc with Professor Johan Paulsson in the Department of Systems Biology. During his postdoc Somenath developed high-throughput timelapse imaging technologies to study stress-response in bacteria. In 2019, he moved to Cambridge, and established the Laboratory of Systems and Synthetic Microbiology. He is also the head of the recently established Smart Microscopy Laboratory, which is a cross-school platform for bringing together engineers, computer scientists, and biologists to develop targeted imaging solutions for specific biological problems.