Areas of expertise at SysBio
At SysBio Lab, we investigate protein structure, DNA and RNA strings and function through deterministic motif discovery (statistical learning), ligand binding site prediction (geometrical constraints), and graph theory approaches for biological sequence pattern discovery. Our group has developed a suite of widely used computational resources, including DeepInteract, Deeplnc, Pharmadoop, DUSR, and CEMDB, which enable large‑scale sequence analysis, protein–protein interaction prediction, and pharmacophore searching. These tools exemplify our commitment to translating algorithmic innovation into accessible platforms for the global research community.
Our systems biology research focuses on olfaction in humans and machines, aiming to decode the cognitive processes of smell through mathematical modeling of neurobiological mechanisms. We study odorant–receptor interactions and odor classification, while developing cognitive models of olfaction using in‑house hardware and bio‑signal acquisition devices. By integrating machine learning classification with systems‑level modeling, we advance both fundamental neuroscience and applied sensory technologies such as electronic noses. Beyond olfaction, our modeling approaches extend to disease pathways, exemplified by mathematical simulations of signaling networks in cancer.
The exponential growth of biological data in the post‑genomic era demands systematic and intelligent approaches. SysBio Lab specializes in Next Generation Sequencing (NGS) data analysis, encompassing whole genome, exome, RNA‑Seq, and ChIP‑Seq datasets. Our workflows integrate data quality assessment, variant calling, assembly, alignment, and interpretation, enabling discovery of novel biomarkers and mechanistic insights. As a part of GenomeIndia Project, we emphasize scalable computational pipelines that address the challenges of large‑scale genomics and proteomics.
Structural & Functional Bioinformatics research at SysBio Lab addresses the dynamic complexity of biological macromolecules. We focus on 3D protein structure prediction, conformational stability analysis, and molecular interaction mapping, with direct applications in drug discovery and therapeutic design. Our research includes target identification, lead compound screening, protein classification, and active/binding site prediction, bridging computational modeling with translational biomedical applications.
Ongoing Research works
SysBio Lab’s olfaction research program is a comprehensive effort that spans molecular biology, computational modeling, systems neuroscience, and translational applications. At the molecular level, our group investigates the structural dynamics of olfactory receptors, decoding how odorants bind and activate receptor proteins. This work has led to pharmacophore modeling and the classification of basic odor categories, with landmark publications such as Structural dynamics of olfactory receptors: implications for odorant binding (2025) and Decoding Seven Basic Odors by Investigating Pharmacophores (2022). On the computational front, we have developed machine learning and deep learning models—including XGBoost frameworks and neural network classifiers—to predict odorant–receptor interactions and uncover structure–odor relationships. These efforts culminated in the creation of OlfactionBase, a repository of odors, odorants, receptors, and their interactions, alongside tools such as DeepOlf and OBPred, which are now widely used in the community.
At the systems level, we model the neurobiological mechanisms of olfactory transduction, building cognitive frameworks of smell using mathematical simulations, bio-signal acquisition devices, and in-house hardware. This systems biology perspective has also been applied to disease pathways, exemplified by our modeling of STAT3 signaling in leukemia. Translationally, SysBio Lab has pioneered electronic nose technologies for healthcare and agriculture. Our patented devices include VOC sampling systems for breath and sebum diagnostics, fruit ripening detection sensors, and UAV-integrated spraying systems. These innovations have enabled breakthroughs such as India’s first smell trademark, recognized by WIPO and widely covered in national and international media.
Together, these strands—molecular insights, computational prediction, systems modeling, and applied device development—define SysBio Lab’s olfaction research as a unique fusion of genomics, bioinformatics, neuroscience, and AI, with impact ranging from fundamental science to industrial consultancy with P&G and Shastrah.AI, and translational outcomes in diagnostics, agriculture, and consumer products. This integrated program positions olfaction not only as a sensory frontier but also as a transformative domain for biomedical innovation and machine intelligence.
- Structural dynamics of olfactory receptors: implications for odorant binding (J Biomol Struct Dyn, 2025).
- XGBoost odor prediction model: structure–odor relationship (J Biomol Struct Dyn, 2024).
- Developing human olfactory network and exploring receptor–odorant interaction (J Biomol Struct Dyn, 2023).
- Decoding Seven Basic Odors by Investigating Pharmacophores (Current Bioinformatics, 2022).
- OBPred: deep neural network classifier for odorant-binding proteins (Neural Computing & Applications, 2021).
SysBio Lab’s cancer research program integrates genomics, structural biology, systems modeling, and pharmacology to address the complexity of tumor biology and therapeutic resistance. At the molecular level, our group has identified novel dysregulated genes in breast cancer through high‑throughput ChIP‑Seq analysis (Scientific Reports, 2017), and explored transcriptional changes driving chemoresistance in triple‑negative breast cancer (NPJ Breast Cancer, 2025). Structural bioinformatics studies have provided insights into the conformational stability of oncogenic proteins, such as EZH2, and guided drug discovery efforts targeting receptor mutations (Scientific Reports, 2016).
On the computational front, we employ network pharmacology, RNA‑Seq analysis, and machine learning approaches to uncover candidate targets and predict drug–gene interactions. This includes work on oral squamous cell carcinoma, where high‑throughput RNA‑Seq and in silico docking identified bioactive compounds with therapeutic potential (J Biomol Struct Dyn, 2024). Our lab has also contributed to the development of multi‑targeted inhibitors of topoisomerase II and computational pipelines for drug repurposing in cancers such as small cell lung cancer and pancreatic ductal adenocarcinoma.
Systems biology modeling complements these efforts by simulating oncogenic signaling pathways, such as STAT3 in chronic myeloid leukemia, to understand resistance mechanisms and identify intervention points (3 Biotech, 2016). Translationally, our research extends to natural product–based therapeutics, anti‑inflammatory agents like curcumin, and phytochemical modulation of pathways such as COX‑3 and NF‑κB in arthritis (Scientific Reports, 2023), reflecting a broader interest in cancer‑associated inflammation.
Together, these strands define SysBio Lab’s cancer research as a multi‑scale program: from gene regulation and receptor dynamics to systems‑level pathway modeling and translational drug discovery. By combining big data analytics, structural insights, and AI‑driven prediction, the lab contributes to precision oncology, offering new biomarkers, therapeutic strategies, and computational tools for tackling cancer heterogeneity and resistance.
- Enhanced ETS1 stability by DNAPKcs orchestrates transcriptional changes during chemoresistance in triple negative breast cancer. (2025) NPJ Breast Cancer, 11(1), 114.
- Identification of candidate target genes of oral squamous cell carcinoma using high-throughput RNA-Seq data and in silico studies of their interaction with naturally occurring bioactive compounds. (2024) Journal of Biomolecular Structure and Dynamics, 42(15), 8024–8044.
- Docking and molecular dynamics simulation for therapeutic repurposing in small cell lung cancer (SCLC) patients infected with COVID-19. (2023) Journal of Biomolecular Structure and Dynamics, 41(1), 16–25.
- In Silico Study of a Small Bioactive Molecule Targeting Topoisomerase II and P53-MDM2 Complex in Triple-Negative Breast Cancer. (2023) ACS Omega, 8(41), 38025–38037.
- Recent development of multi-targeted inhibitors of human topoisomerase II enzyme as potent cancer therapeutics. (2023) International Journal of Biological Macromolecules, 226, 473–484.
SysBio Lab’s microbiome research program focuses on decoding the role of microbial communities in human health, disease resistance, and environmental sustainability. We have developed the HAMP knowledgebase, a curated 3D repository of antimicrobial peptides from the human microbiome, which serves as a global resource for functional annotation and therapeutic exploration (Mulpuru et al., Current Bioinformatics, 2021). Our work extends to natural product discovery, where we identified secondary metabolites from spices and herbs as multitarget inhibitors of SARS‑CoV‑2 proteins, highlighting the microbiome’s contribution to antiviral strategies (Gupta et al., J Biomol Struct Dyn, 2022). We have also provided comprehensive insights into bacterial biofilm infections and their resistance mechanisms, offering perspectives on current treatment strategies (Singh et al., Biomedical Materials, 2022). In parallel, our group has explored bio‑inspired nanocomposites for remediation of pharmaceutical pollutants, linking microbiome‑derived processes to environmental health (Gautam et al., Nanotechnology for Environmental Remediation, 2022). Finally, we demonstrated the therapeutic potential of microbiome‑related phytochemicals in modulating inflammatory pathways such as COX‑3 and NFκB, with direct relevance to arthritis management (Biswas et al., Scientific Reports, 2023).
Together, these contributions establish SysBio Lab as a leader in microbiome‑driven computational biology and translational research, bridging fundamental discovery with applications in infectious disease, inflammation, drug discovery, and environmental remediation.
SysBio Lab’s Algorithms and Tools Development program is at the heart of our research ecosystem, designed to transform complex biological data into actionable insights. We focus on creating deterministic motif discovery frameworks, ligand binding site prediction algorithms based on geometrical constraints, and graph theory approaches for uncovering hidden patterns in biological sequences. These computational foundations have enabled us to build a suite of innovative tools and databases that are widely used in the scientific community.
Among our flagship contributions are DeepInteract (a deep neural network–based protein–protein interaction prediction tool), Deeplnc (for long non‑coding RNA prediction), and DeepOlf (for odorant–receptor interaction modeling). We have also developed specialized resources such as CEMDB (Cancer Epigenetic Markers Database), PHARMADOOP (pharmacophore searching), DUSR (Distributed Ultrafast Shape Recognition), and HumDLoc (human protein subcellular localization prediction). These tools exemplify our commitment to bridging machine learning, bioinformatics, and systems biology.
Our algorithms are not only theoretical constructs but have been translated into user‑friendly online servers and databases, empowering researchers worldwide to perform large‑scale searches, functional annotations, and predictive modeling. Publications such as DeepInteract: Deep Neural Network Based Protein–Protein Interaction Prediction Tool (Current Bioinformatics, 2017) and DeEPn: Deep Learning Based Enzyme Functional Annotation (J Biomol Struct Dyn, 2021) highlight the scientific rigor and impact of this work.
Together, these tools form a computational backbone for SysBio Lab, enabling breakthroughs across genomics, olfaction, cancer biology, microbiome research, and now aging studies. They reflect our philosophy of combining algorithmic innovation with translational utility, ensuring that every model or database we build contributes meaningfully to the advancement of biomedical science.