Nishit Banka Successfully Defends His Thesis

Congratulations to our own Dr. Nishit Banka for successfully defending his thesis entitled “High throughput screening and ML-aided engineering of transcription factor based biosensors.”

Abstract

Biosensors are a promising technology for industrial applications, functioning as biological circuits that process an input signal to produce a measurable output. Transcription factors
are strong candidates for biosensors given their inherent ability to regulate gene expression upon binding to small molecules, or interacting with proteins. Native transcription factors serve as a good starting point but often require modifications for their use as biosensors. In this work, we engineered two different transcription factors (BmoR and CymR), using a traditional directed evolution workflow, combined with large-scale data generation and machine learning (ML)-aided mutation selection. With only a single round of engineering, facilitated by the positive-unlabeled (PU) learning algorithm developed in our lab, we synthesized a variant of BmoR containing 10 mutations, with increased sensitivity towards butyrate. In the second project, we applied a similar workflow to improve the sensitivity of CymR towards p-cumate for inducible control of bacterial nitrogen fixation in klebsiella variicola. We synthesized a combinatorial library of targeted CymR mutations to account for epistatic interactions, and screened it using fluorescence assisted cell sorting (FACS) to obtain highly sensitive variants