Karthik Raman

The paper characterizes microbial communities and AMR genes in Chennai Metro surfaces, finding distinct profiles and low AMR prevalence.

This study performs a pan-cancer multi-omic analysis of radiotherapy response, identifying genomic and transcriptomic features associated with radioresistance and poor survival.

The paper builds genome-scale metabolic models for eight cancer types to identify tissue-specific metabolic rewiring and synthetic lethal gene pairs supported by computational validation.

This study analyzes marine microbial community assembly and stability using 16S rRNA data, finding stochastic processes drive assembly globally, with polar communities showing high modularity but vulnerability to hub removal.

This study identifies 30 TIL-derived gene markers with prognostic value for chemotherapy response in TNBC, validated across multiple datasets with AUROC scores above 0.65.

The paper develops a computational framework to analyze robustness, plasticity, evolvability, and canalisation in three-node genetic circuits by simulating and classifying over 190 million circuits.

This study uses genome-scale metabolic modeling and 13C-based pathway mapping to identify key enzymes for enhancing camptothecin production in the endophyte Alternaria burnsii.

The paper identifies 182 bacterial strains, including 14 novel Gram-positive species, in NASA Mars 2020 cleanrooms, highlighting their biotechnological potential despite being undetected by metagenomic sequencing.

The paper presents a comprehensive genomic atlas of Indian populations, identifying novel variants and highlighting genetic diversity and pharmacogenomic implications.

This study characterizes the plasmidome, resistome, and virulence genes of Acinetobacter johnsonii from NASA cleanrooms and a clinical setting, revealing adaptability traits and antimicrobial resistance.

This study uses genome-scale metabolic modeling to analyze how carbamazepine affects tomato plant metabolism and identifies biostimulants that mitigate its stress effects.

This paper quantifies how retroactivity affects information flow in synthetic biological circuits and identifies strategies to mitigate its effects.

This study identifies chlorotonils as potent anti-Mtb agents and shows resistance is mediated by MmpR5-MmpL5.

This study uses metagenomic analysis to characterize drinking water microbiomes in an Indian city, identifying core bacterial species and widespread antibiotic resistance genes.

The paper introduces PURE, a method for structure-constrained molecular generation that avoids metric biases and achieves competitive performance through reinforcement learning and self-supervised learning.

The paper identifies arginine biosynthesis as a metabolic weakness in Candida albicans during host infection, highlighting ALT1 as a potential therapeutic target.

GViNC compares genome graphs to reveal ancestry-specific genetic diversity patterns and key variant regions in human populations.

COSMOS compares microbial monocultures and communities to identify optimal systems for specific carbon sources and environments, finding Shewanella oneidensis-Klebsiella pneumoniae community most efficient for 1,3-propanediol production under anaerobic conditions.

This paper benchmarks community detection algorithms on artificial and real-world networks, finding performance declines with lower modularity and trade-offs between accuracy and computational time.

The paper introduces an improved framework for grey-box modeling of biological processes to enhance predictability and parameter estimation with fewer iterations.

The paper introduces FROG analysis to standardize reproducibility assessments of genome-scale metabolic models, resulting in full reproducibility of all tested models.

The paper identifies alternate metabolic pathways rerouted during synthetic lethal reaction pairs using a constraint-based optimization approach and applies it to genome-scale models.

This paper identifies design principles for perfect adaptation in biological networks using nonlinear systems theory, showing that only two network architectures can achieve local adaptation across the state space.

Panera improves microbial community metabolic modeling by integrating 16S data with pan-genus models, reducing prediction error by 10%.

This paper introduces an algorithm to design minimal microbiomes capable of specific functions while maintaining community properties, demonstrating its application on gut model communities.

This paper introduces a Bayesian Optimal Experiment Design method for parameter estimation and model selection, demonstrating its effectiveness in systems biology models.

This study investigates genomic, functional, and metabolic adaptations of Enterobacter bugandensis isolated from the ISS, revealing resistance mechanisms and microbial interactions that contribute to its persistence in space.

This paper introduces a protocol to create a FAIR collection of machine learning models in life sciences and medicine, aiming to improve reproducibility and reuse through community curation.

This paper defines new theoretical concepts of sloppiness, develops a method to assess model goodness, and applies it to HIV pharmacokinetic models to identify biologically relevant parameters.

This paper develops a systems-theoretic framework to identify robust adaptation mechanisms in biological networks under deterministic and stochastic conditions.