Rohan Maddamsetti

This paper reports the parallel evolution of chimeric genes in Escherichia coli through genomic deletions, suggesting they are more common and potentially adaptive than previously thought.

This paper shows that a synthetic gene circuit can break a growth rate plateau in E. coli by selectively suppressing slow-growing cells, leading to faster growth.

This paper retrained ProteinMPNN on halophilic proteomes to design salt-tolerant enzymes, showing HaloMPNN shifts protein properties toward halophilic traits.

The paper develops a machine learning model to predict plasmid copy numbers using protein domains and applies it to metagenomic and clinical plasmid data to identify ecological patterns.

myLLannotator uses a large language model to annotate microbial genomes, enabling rapid discovery of gene patterns in endosymbiotic bacteria.

This paper develops a foundation model for microbial growth dynamics using self-supervised learning on diverse datasets, enabling accurate reconstruction and prediction of growth patterns and antibiotic responses.

This study shows how transposon-plasmid coevolution enables Escherichia coli to dynamically respond to antibiotics through population-level feedback control.

The paper develops a new computational method to analyze plasmid copy numbers across microbial genomes and identifies three scaling laws relating plasmid length, gene content, and metabolic function.

The paper shows transposon-plasmid nesting allows microbes to rapidly respond to environmental changes by combining genetic mobility with copy number amplification.

The paper identifies a highly abundant circular RNA, Obelisk-S.s, in Streptococcus sanguinis SK36, despite its absence from the genome, and reports mutations in some samples.

The paper integrates mechanistic modeling with machine learning to accelerate the discovery of design rules for pattern formation in living cells.

This paper shows that horizontal gene transfer can enable dynamic division of labor in microbes to stabilize complex metabolic pathways.

The paper uses sequence coevolution to predict protein interactions in E. coli, identifying 620 new interactions and their structures.

This study identifies 38 loci and 250 epistatic pairs affecting antibiotic resistance in Neisseria gonorrhoeae using evolutionary couplings.