Vinodh K. Kandavalli

This paper develops a vision transformer model for rapid bacterial species identification from time-lapse images without fluorescent labels, achieving high accuracy on clinical isolates.

This study uses microfluidics, time-lapse microscopy, and deep learning to rapidly identify seven bacterial species with high accuracy in one hour.

This paper develops a method to rapidly detect bacteria in blood using smart centrifugation, microfluidic trapping, and deep learning for sepsis diagnostics.

The paper measures association and dissociation rates of LacI binding sites in living cells and finds an anti-correlation between these rates, challenging the assumption that binding site strength is solely determined by dissociation rate.

This study uses live-cell imaging of fluorescently labeled loci in E. coli to inform a dynamic polymer model of chromosome organization across the cell cycle, revealing global reorganization of the chromosome structure.

The paper develops a library of fluorescent reporters for 16 global regulators in E. coli to track their activity under various growth and stress conditions.

The paper uses deep learning to classify bacteria species from phase-contrast microscopy images without staining or fixation, achieving over 98% accuracy.

The paper models gene promoters with dual σ factor preference in E. coli, showing sequence-dependent sensitivity to σ70 and σ38 and proposing a generalizable framework for synthetic biology applications.

The paper examines how changes in RNA polymerase concentration affect gene expression in E. coli, finding that mid-term responses correlate with transcription factor network topology.

The paper develops an analytical model of gene expression dynamics for tandem promoters in E. coli, showing that transcription interference depends on RNAP occupancy time and distance between transcription start sites.