About

Kandukuri Raman is an Adjunct Professor in the School of Integrative Plant Science at Cornell University and serves as a Faculty Fellow in the David R. Atkinson Center for a Sustainable Future. His work at Cornell began in 1993 and focuses on international agriculture, food production in developing countries, crop biotechnology, crop improvement, curriculum development, and capacity building. He has coordinated and led a multi-semester course in India involving faculty from both countries and over 1,000 students, and has played a leadership role in various Cornell programs in India aimed at capacity development. His research emphasizes global agricultural development, with specific focus on crop biotechnology, potato crop improvement, integrated pest management, and the safe transfer of proprietary applications in plant biotechnology to developing countries. He has contributed to promoting international collaborative research on potato late blight, developing genetically modified pest and disease resistant crops, and supporting sustainable development goals to achieve food security. His outreach work involves developing partnerships and capacity development projects across Latin America, Africa, and Asia, working with donors, researchers, and private sector entities to implement technology transfer, crop improvement, and international extension programs.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Biology
• Data Mining
• Library science
• Genetics
• Veterinary medicine
• Agronomy
• Horticulture
• World Wide Web
• Botany
• Bioinformatics

Selected publications

• Unveiling hidden microbial diversity in Mars 2020 mission assembly cleanrooms with molecular insights into the persistence and perseverance of novel species defying metagenome sequencing

  Microbiology Spectrum · 2026-05-07

  articleOpen access

  NASA cleanrooms, which are critical for assembling space mission components, are maintained under stringent decontamination protocols to minimize biological contamination. These environments are characterized by nutrient-poor and oligotrophic conditions, leading to low microbial loads. Despite extensive cleaning, oligotrophs capable of surviving in such conditions continue to persist, often remaining undetected due to their low abundance, resistance to environmental stresses, and difficulties in biomolecule extraction. Even with shotgun metagenome sequencing technologies, these microbes may go undetected or be underrepresented due to their robust cell walls and the absence of reference genomes in publicly available databases. Over a 6-month study of Mars 2020 mission cleanrooms, 182 bacterial strains belonging to 19 families were identified using a whole-genome sequencing (WGS) approach. Among these, 14 novel Gram-positive species were discovered, including eight spore formers. Though the novel species comprised only 0.001% of the sequencing data, their successful cultivation allowed for functional characterization. Through WGS data mining, genomic traits associated with resilience in extreme conditions were revealed. These species were found to be involved in nitrogen cycling, carbohydrate metabolism, and radiation resistance, traits essential for survival in extreme environments. Furthermore, 12 biosynthetic gene clusters were identified, including those linked to ectoine and [Formula: see text]-poly-L-lysine production, suggesting potential biotechnological applications. These findings highlight the hidden microbial diversity within cleanrooms and emphasize the necessity of advanced detection strategies. A better understanding of these microbes will provide insights into extremophiles with applications in biotechnology, medical research, and life support systems for future space exploration missions.IMPORTANCEDespite strict decontamination protocols, NASA cleanrooms harbor low-biomass microbial communities adapted to nutrient-poor environments. These oligotrophic microbes often go undetected in shotgun metagenomics methods due to their low abundance, resistance to lysis, and lack of reference genomes. Standard shotgun metagenome sequencing methods fail to retrieve them, as dominant microbial DNA overshadows rare species. Over 6 months of monitoring Mars 2020 mission cleanrooms, 182 bacterial strains from 19 families were identified, including 14 novel Gram-positive species, 8 of which were spore formers. Though present at 0.001% abundance in sequencing data, we successfully cultured them, enabling functional characterization. These microbes exhibited roles in nitrogen cycling, carbohydrate metabolism, and radiation resistance, with 12 biosynthetic gene clusters linked to ectoine and [Formula: see text]-poly-L-lysine production. These findings highlight the previously underestimated microbial diversity in cleanrooms and emphasize the need for advanced detection strategies to explore extremophiles with applications in biotechnology and space exploration.

  PublisherDOI

• Plasmidome, resistome, and virulence-associated gene characterization of <i>Acinetobacter johnsonii</i> in NASA cleanrooms and a clinical setting

  Microbiology Spectrum · 2026-03-23

  articleOpen access

  ABSTRACT Evidence suggests the persistence of non-spore-forming Acinetobacter johnsonii in high-stakes controlled and nutrient-limited environments. Here, we investigated the mechanisms underlying this adaptability through a comprehensive genomic analysis of 22 isolates of A. johnsonii from NASA’s Payload Hazardous Servicing Facility (PHSF) and one carbapenem-resistant strain (E154408A) from patient colonization in Ireland. Core-genome phylogeny revealed clustering of PHSF-originating isolates in a monophyletic clade divergent from the main species lineage. Species-wide virulence-associated genes and metabolic reconstruction indicated the exclusive presence in PHSF-originating isolates of two complete efflux pumps and a conserved allantoin racemase, suggesting adaptability for multiple environmental stresses. The ubiquity of bla OXA in genomes analyzed ( n = 112) and the phenotypically validated multidrug-resistant profile of the E154408A strain highlight A. johnsonii ’s potential as an antimicrobial resistance (AMR) reservoir. Plasmidome analysis suggested gain/loss events across the monophyletic population and potential AMR acquisition pathways. Genome-to-metagenome mapping identified genomic signatures of A. johnsonii in PHSF &gt;10 years post-initial isolation. IMPORTANCE Acinetobacter johnsonii is increasingly recognized as an emerging human pathogen, with growing evidence of its ability to persist in controlled, high-stakes environments, posing risks as both a persistent environmental contaminant and an antimicrobial resistance (AMR) reservoir. Yet, gaps remain in our understanding of its AMR profile and the mechanisms that enable its enhanced environmental adaptability. This knowledge is necessary in contexts where biological cleanliness is a priority, such as clinical settings and spacecraft assembly facilities’ cleanrooms, where contamination of hardware with terrestrial microorganisms is concerning. In this study, we aim to address some of the key knowledge gaps by providing genomic insights into a rare multidrug-resistant clinical isolate and 22 NASA cleanroom isolates that persisted for over a decade in extremely clean conditions. Our findings will help assess the contamination risk of A. johnsonii in high-stakes environments and ultimately strengthen our ability to manage this microbial contaminant across terrestrial and extraterrestrial settings. Cleanroom-derived A. johnsonii genomes show traits consistent with increased adaptability. Genomic signatures of A. johnsonii persisted in the cleanrooms for over 10 years. bla OXA is ubiquitously found in all 112 A. johnsonii genomes analyzed. Isolate E154408A is the first reported patient colonization case by carbapenem-resistant A. johnsonii in Europe.

  PublisherDOI

• Plasmidome, resistome, and virulence-associated genes characterization of <i>Acinetobacter johnsonii</i> in NASA cleanrooms and a clinical setting

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-10

  preprintOpen access

  Abstract Evidence shows persistence of non-spore-forming Acinetobacter johnsonii in high-stakes controlled and nutrient-limited environments. This study aims to explore the mechanisms underpinning such adaptability through a comprehensive genomic analysis of 22 isolates of A. johnsonii from NASA’s Payload Hazardous Servicing Facility (PHSF) and one carbapenem-resistant strain (E154408A) from patient colonization in Ireland. Core-genome phylogeny revealed clustering of PHSF-originating isolates in a monophyletic clade divergent from the main species lineage. Species-wide virulence-associated genes and metabolic profiling indicated the unique presence in PHSF-originating isolates of two complete efflux pumps and of a conserved allantoin racemase, suggesting adaptability for multiple environmental stresses. Observed ubiquity of bla OXA in investigated genomes (n=112) and phenotypically-validated multidrug-resistant profile of E154408A strain highlight A. johnsonii ’s potential as antimicrobial resistance (AMR) reservoir. Plasmidome analysis suggested gain/loss events across the monophyletic population and potential AMR acquisition pathways. Genome-to-metagenome mapping identified genomic signatures of A. johnsonii in PHSF &gt;10 years post initial isolation. Importance Acinetobacter johnsonii is increasingly recognized as an emerging human pathogen, with growing evidence of its ability to persist in controlled, high-stakes environments, posing risks as both persisting environmental contaminant and antimicrobial resistance (AMR) reservoir. Yet, gaps remain in our understanding of its AMR profile and the mechanisms that enable its enhanced environmental adaptability. This knowledge is necessary in contexts where biological cleanliness is a priority such as clinical settings and spacecraft assembly facilities cleanrooms, where contamination of hardware with terrestrial microorganisms is concerning. In this study, we aim to address some of key knowledge gaps by providing genomic insights into a rare multi-drug resistant clinical isolate and 22 NASA cleanroom isolates that persisted for over a decade in extremely clean conditions. Our findings will help evaluate the contamination risk of A. johnsonii in high-stakes environments and ultimately strengthen our ability to manage this microbial contaminant across terrestrial and extraterrestrial settings. Highlights Cleanrooms-derived A. johnsonii genomes show favorable traits for increased adaptability Genomic signatures of A. johnsonii persisted in the cleanrooms for &gt;10 years bla OXA is ubiquitously found in the genome of all A. johnsonii E154408A is the first patient colonization by carbapenem-resistant A. johnsonii in Europe

  PublisherOA PDFDOI

• An integrated systems biology approach establishes arginine biosynthesis as a metabolic weakness in Candida albicans during host infection

  Cell Communication and Signaling · 2025-08-04

  articleOpen access

  Candida albicans, responsible for approximately 70% of all Candida infections, is a leading cause of invasive candidiasis and poses a significant global health threat. With the emergence of drug-resistant strains, mortality rates have reached a staggering 63.6% in severe cases, complicating treatment options and demanding the discovery of novel therapeutic targets. To address this pressing need, using a unique multidisciplinary approach, we attempted to identify some the critical metabolic pathways that can be targeted to modulate the virulence of CAL. Condition-specific genome-scale metabolic models (GSMMs), along with a novel integrated host-CAL model developed in this study, highlighted the central role of arginine (Arg) metabolism and uncovered ALT1, an arginine biosynthesis enzyme, as a critical metabolic vulnerability in CAL virulence. Heightened expression of arginine biosynthesis genes indicated that increased arginine synthesis mainly occurred through proline intermediates during host interaction. Significantly impaired virulence and in vivo pathogenicity of ALT1-deleted CAL highlighted the potential of targeting arginine metabolism as a novel strategy to combat antifungal resistance and underscored the power of integrating systems biology with experimental approaches in identifying new therapeutic targets.

  PublisherOA PDFDOI

• UNFOLDing Robustness, Plasticity, Evolvability and Canalisation of Biological Function

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-06

  preprintSenior author

  Abstract A unique balance of seemingly contradictory properties like robustness and plasticity, or evolvability and functional canalisation, characterises biological systems. To understand the basis of these properties, we investigate gene regulation, which is at the core of biological function. We simulate dynamical models of over 190 million genetic circuits covering all possible three-gene circuit structures. Our computational pipeline classifies these circuits into functional clusters by matching their temporal response shapes. Thus, we generate a dataset linking circuit structure, parameters and a corresponding functional label. Our key finding is a finite list of 20 functions that three-node genetic circuits can perform. Moreover, the structure-parameter space for these circuits tends to be primed for responses that stabilise over time following a perturbation. Every structure exhibits potential for multifunctionality with a range of 2–17 functions contingent upon parameters. We quantify network degeneracy, showing that many structural changes can be made to circuits without altering function. We define three quantities: structural, parametric, and functional diversities. Using these diversities, we construct a UNified FramewOrk for reguLatory Dynamics (UNFOLD) to analyse four key biological properties—robustness, plasticity, evolvability, and functional canalisation. Using UNFOLD, we identify that only 6.5% of network structures are non-plastic, while parameter sets enabling parametric robustness exist for every three-node network. We identify functionally canalised circuits from structure pairs that can be interchanged for a large number of parameter sets without a change in function. Overall, our framework offers insights into the fundamental organisation of biological networks by thorough analysis of three-node networks. Significance Statement Biological systems exhibit remarkable properties like robustness, plasticity, evolvability, and canalisation. This study presents a unified computational framework to understand these properties by exhaustively exploring the design space of three-node genetic circuits, identifying that only 20 functions are achievable, and revealing a natural bias toward stability. We uncover key principles of network degeneracy and multifunctionality, highlighting the versatility of genetic circuits. By analysing structural, parametric, and functional diversities, we identify structural and parametric changes that can transition a genetic circuit from robust behaviour to plasticity or from being canalised to becoming evolvable. Our work advances theoretical insights into biological function. It provides a method to identify alternate designs and parametric conditions for genetic circuits, paving the way for the design of reliable synthetic genetic circuits.

  PublisherDOI

• GViNC: an innovative framework for genome graph comparison reveals hidden patterns in the genetic diversity of human populations

  NAR Genomics and Bioinformatics · 2025-07-04 · 2 citations

  articleOpen access

  Genome graphs provide a powerful reference structure for representing genetic diversity. Their structure emphasizes the polymorphic regions in a collection of genomes, enabling network-based comparisons of population-level variation. However, current tools are limited in their ability to quantify and compare structural features across large genome graphs. We introduce GViNC, Genome graph Visualization, Navigation, and Comparison, a novel framework that enables partitioning genome graphs into interpretable subgraphs, mapping linear coordinates to graph nodes, and summarizing both local and global structural variation using new metrics for variability, hypervariability, and graph distances. We applied GViNC to multiple pan-genomic and population-specific genome graphs constructed with over 85M variants in 2504 individuals from the 1000 Genomes Project. We found that genomic complexity varied by ancestry and across chromosomes, with rare variants increasing variability by 10-fold and hypervariability by 50-fold. GViNC highlighted key regions of the human genome, such as Human Leukocyte Antigen and DEFB loci, and many previously unreported high-diversity regions, some with population-specific signatures in protein-coding and regulatory genes. By bridging sequence-level variation and graph-level topology, GViNC enables scalable, quantitative exploration of genome structure across populations. GViNC's versatility can aid researchers in extensively investigating the genetic diversity of different cohorts, populations, or species of interest.

  PublisherOA PDFDOI

• Metagenomic profiling of municipal drinking water microbiomes in an Indian city: Insights into diversity, water quality, and AMR potential

  Journal of environmental chemical engineering · 2025-10-25

  articleCorresponding
  PublisherDOI

• An improved framework for grey-box identification of biological processes

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-20

  preprintOpen accessCorresponding

  Abstract Developing models from observations is at the heart of empirical science. Grey-box Modeling combines the insights gained from the results obtained from first principles with observational data. When the model turns out to be unsatisfactory, the goodness of such grey-box models in terms of predictability and parameter estimates largely depends on either modifying the model structure obtained from the first principles or conducting new experiments. Unfortunately, in the context of biological models, where the model structures are usually nonlinear ODEs with a large number of states and parameters along with sparse and noisy experimental data, traditional identification protocols have to go through several iterations to identify the source of the issue. Even after multiple iterations, they may still arrive at sub-optimal solutions. In this work, we propose an improved framework with a new set of tools to resolve this issue unambiguously with a minimum round of iterations.

  PublisherDOI

• PURE: Policy-guided Unbiased REpresentations for structure-constrained molecular generation

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-24

  preprintOpen access

  Abstract Structure-constrained molecular generation (SCMG) generates novel molecules that are structurally similar to a given molecule and have optimized properties. Deep learning solutions for SCMG are limited in that they are pre-disposed towards existing knowledge, and they suffer from a natural impedance mismatch problem due to the discrete nature of molecules, while deep learning methods for SCMG often operate in continuous space. Moreover, many task-specific evaluation metrics used during training often bias the model towards a particular metric -”metric-leakage”. To overcome these shortcomings, we propose Policy-guided Unbiased REpresentations (PURE) for SCMG that learn within a framework simulating molecular transformations for drug synthesis. PURE combines self-supervised learning with a policy-based reinforcement-learning (RL) framework, thereby avoiding the need for external molecular metrics while learning high-quality representations that incorporate an inherent notion of similarity specific to the given task. Along with a semi-supervised training design, PURE utilizes template-based molecular simulations to better explore and navigate the discrete molecular search space. Despite the lack of metric biases, PURE achieves competitive or superior performance than state-of-the-art methods on multiple benchmarks. Our study emphasizes the importance of reevaluating current approaches for SCMG and developing strategies that naturally align with the problem. Finally, we illustrate how our methodology can be applied to combat drug resistance, by identifying sorafenib-like compounds as a case study.

  PublisherOA PDFDOI

• COmmunity and Single Microbe Optimisation System (COSMOS)

  npj Systems Biology and Applications · 2025-05-21

  articleOpen accessSenior author

  Bioprocessing utilises microbial monocultures and communities to convert renewable resources into valuable products. While monocultures offer simplicity, communities provide metabolic diversity and cooperative biosynthesis. To systematically evaluate these systems, we developed COmmunity and Single Microbe Optimisation System (COSMOS), a dynamic computational framework that simulates and compares monocultures and co-cultures to determine optimal microbial systems tailored to a specific environment. COSMOS revealed key factors shaping biosynthetic performance, such as environmental conditions, microbial interactions, and carbon sources. Notably, it predicted the Shewanella oneidensis-Klebsiella pneumoniae co-culture as the most efficient producer of 1,3-propanediol under anaerobic conditions, aligning closely with experimental data, including optimal carbon source concentrations and inoculum ratios. Additional findings highlight the resilience of microbial communities in nutrient-limited processes and emphasise the role of computational tools in balancing productivity with operational simplicity. Overall, this study advances the rational design of microbial systems, paving the way for sustainable bioprocesses and circular bio-economies.

  PublisherOA PDFDOI

Recent grants

• NIH Grant F31CA168198

  NIH · $62k · 2014

Frequent coauthors

• Arun K. Tangirala

  Indian Institute of Technology Madras

  51 shared

• Malvika Sudhakar

  Technical University of Denmark

  34 shared

• Priyan Bhattacharya

  29 shared

• Aarthi Ravikrishnan

  Genome Institute of Singapore

  28 shared

• Balaraman Ravindran

  Indian Institute of Technology Madras

  27 shared

• Balagurunathan Kuberan

  25 shared

• Pratyay Sengupta

  Robert Bosch (India)

  23 shared

• Raghunathan Rengaswamy

  Indian Institute of Technology Madras

  22 shared

Awards & honors

• King Baudouin Award