Anand V. Sastry

The paper develops a workflow to reconstruct dynamic transcriptional regulatory networks from public gene expression data using Bacillus subtilis as a case study.

The paper uses iModulon analysis to uncover the transcriptional regulatory network of Pseudomonas syringae across diverse conditions and interactions.

The paper develops deep learning models trained on synonymous variant datasets to predict and optimize protein expression in E. coli.

The paper uses adaptive laboratory evolution and transcriptomic analysis to identify six mechanisms of paraquat tolerance in E. coli.

This study uses machine learning to analyze RNA-Seq data from multiple Salmonella Typhimurium strains, revealing transcriptional modules and regulatory patterns across the pan-genome.

This study uses transcriptomic data to construct a regulatory network of Pseudomonas aeruginosa, revealing roles of efflux pumps and media composition in antibiotic responses.

Language models trained on high-throughput affinity data predict KD of unseen antibody variants across three orders of magnitude, and a "naturalness" score can be optimised alongside affinity.

This study uses machine learning to uncover the genome-scale transcriptional regulatory network in Pseudomonas putida using gene expression data.

The paper uses machine learning to analyze M. tuberculosis RNA-seq data, identifying gene modules and their roles in metabolism, stress response, and infection.

This paper introduces OptICA, a method for selecting optimal dimensionality in ICA of transcriptomic data, showing it outperforms existing methods across multiple datasets.

The paper uses machine learning to uncover a transcriptional regulatory network in Sulfolobus acidocaldarius, identifying new gene regulators and regulatory modules.

The paper uses machine learning to analyze Pseudomonas aeruginosa transcriptomes and identifies gene sets regulated by known transcriptional factors, revealing insights into biosynthetic clusters and metabolic regulation.

The paper constructs a transcriptional regulatory network for Escherichia coli using machine learning on a large RNA-seq compendium, identifying regulatory modules and novel regulons.

The paper uses matrix factorization to uncover consistent regulatory signals across diverse gene expression datasets, demonstrating its utility in multi-omics analysis.

The paper uses machine learning to identify gene modules in Bacillus subtilis, revealing known and novel regulatory roles and population-level sporulation states.

The paper identifies regulons of five two-component systems in E. coli using RNA-seq and ChIP-exo data, revealing novel regulatory relationships and non-cognate inducers.

iModulonDB provides a knowledgebase of microbial transcriptional regulation derived from machine learning, offering curated gene sets and their activity levels across three bacterial species.

The paper reconstructs the Fur pan-regulon in E. coli strains, revealing core, accessory, and unique regulons and their functional diversity.

The paper decomposes the Escherichia coli transcriptome into 70 independent regulatory signals, showing that 50 correspond to known transcriptional regulators and revealing a principle of signal summation in prokaryotic gene regulation.

This study uses adaptive laboratory evolution to generate E. coli strains with increased superoxide stress tolerance and identifies genetic and transcriptomic mechanisms underlying this adaptation.

The paper identifies 34% of Escherichia coli genes with no experimental evidence of function, termed the y-ome, and characterizes their properties.

The paper develops a quantitative framework to predict ROS stress responses in E. coli across molecular, network, and genetic levels.

The paper introduces ssbio, a Python framework for integrating structural biology data into genome-scale systems biology workflows.