About Lab

Welcome to Saikat's Lab, in the Department of Biotechnology, Faculty of Natural Sciences at Ramaiah University of Applied Sciences (RUAS), Bengaluru, our lab operates at the intersection of quantitative biology, applied mathematics, and data science. We develop computational frameworks, mechanistic models, and machine learning pipelines to solve complex biomedical challenges from cellular immune kinetics to population-level public health strategies.

Core Research Themes:

• Infectious Disease Modeling & Immunodynamics Host-Pathogen Interactions: Investigating within-host dynamics of major global pathogens, with a primary focus on Mycobacterium tuberculosis (Mtb), malaria, and HIV.
• T-Cell Dynamics & Immunity: Modeling immune response behavior, including the protective effects of prior pathogen exposure and cellular-level kinetic variations.
• Epidemiological & Interventional Simulations: Simulating disease transmission pathways and evaluating public health interventions, such as novel vaccination strategies, targeted preventive therapy prioritization, and treatment adherence programs.
• Microbiome & Maternal-Fetal Health Gestational Diabetes & Placental Health: Investigating gut microbiota dysbiosis and profiling microbial signatures as non-invasive, predictive biomarkers for placental health and metabolic complications during pregnancy. Multi-Omics Data Integration: Bridging metagenomic and clinical profiles to understand metabolic crosstalk across physiological barriers.
• Computational Diagnostics & Causal Inference Causal Discovery in Oncology: Applying causal discovery algorithms and Structural Equation Modeling (SEM) to fine-needle aspiration and cytological features for interpretable breast cancer diagnostics.
• Electrogastrography (EGG) Signal Processing: Developing analytical and time-series pipelines to process and interpret non-invasive gastric myoelectric activity.
• Applied Machine Learning in Biomedicine: Building predictive, interpretable machine learning models to detect disease phenotypes and uncover latent patterns across complex clinical datasets.

Methodological Approach:

• Our lab integrates clinical, experimental, and epidemiological data with modern computational tools.
• Mathematical & Mechanistic Modeling: Ordinary and partial differential equations (ODEs/PDEs), agent-based models, and stochastic simulations.
• Statistical Inference & Causal AI: Bayesian networks, structural equation modeling, and causal graphical models.
• Translational Pipeline: Translating theoretical frameworks into policy-relevant public health insights and clinically viable diagnostic tools.

Teaching

• Biostatistics
• Biostatistics & Data analysis
• Mathematics and Computational Thinking in Biology
• Introduction to Programming
• Immuno-informatics
• Bioinformatics & Structural Biology
• Artificial Intelligence

Research Interests

Team

Grants

• Updated soon...

Open Position

• PhD
• Masters
• Postdoc

Openings are available. Candidates with a background in applied mathematics, computational biology, bioinformatics, or related fields are encouraged to apply.

Postdoc: We welcome applications from highly motivated researchers interested in advancing independent research in the above areas. We actively support and mentor candidates to apply for national and international fellowships, including NPDF/DBT-RA/ICMR PDF/AICTE PDF/TWAS-DBT PDF, and other competitive funding schemes.

Please send your CV, a brief statement of interest, and transcripts to the mail id of Dr. Saikat Batabyal.

Publications

• Saikat Batabyal, Kevin Urdahl, Vitaly V Ganusov (2026). Potential limitations of community-wide strategy to treat Mycobacterium tuberculosis infection. . [DOI]
• Saikat Batabyal, Chakraborty, D, Ganusov, V (2024). A brief overview of mathematical modeling of the within-host dynamics of Mycobacterium tuberculosis. Frontiers in Applied Mathematics and Statistics. [DOI]
• Saikat Batabyal (2021). COVID-19: Perturbation dynamics resulting chaos to stable with seasonality transmission. Chaos, Solitons & Fractals. [DOI]
• Saikat Batabyal, Arthita Batabyal (2021). Mathematical computations on epidemiology: A case study of the novel coronavirus (SARS-CoV-2). Theory in Biosciences. [DOI]
• Saikat Batabyal, Jana, D., Upadhyay R. K. (2021). Diffusion driven finite time blow-up and pattern formation in a mutualistic preys-sexually reproductive predator system: A comparative study. Chaos, Solitons & Fractals. [DOI]
• Saikat Batabyal, Jana, D., Lyu, J., Parshad, R.D.: (2020). Explosive predator and mutualistic preys: A comparative study. Physica A: Statistical Mechanics and its Applications. [DOI]
• Saikat Batabyal, Jana, D., Parshad, R. D., Basheer, A., Upadhyay R. K. (2021). Pattern formation in an explosive food chain model: the case of “apparent” mutualism. The European Physical Journal Plus. [DOI]
• Saikat Batabyal, Arthita Batabyal (2021). Public healthcare system capacity during COVID‐19: A computational case study of SARS‐CoV‐2. Health Science Reports. [DOI]
• Dey, D., Kumar, A., Pandey, P., Batabyal, A., Saikat Batabyal, Lingayat, D. (2022). Role of STI in Sustainable Development: Towards food and nutrition security. Policy Brief, IISc, Bangalore. [DOI]
• Jana, D., Saikat Batabyal, Lakshmanan, M. (2020). Self-diffusion-driven pattern formation in prey-predator system with complex habitat under fear effect. Eur. Phys. J. Plus. [DOI]
• Banerjee, D., Saikat Batabyal (2017). Hyper relative order (p, q) of entire functions. Annals of West University of Timisoara - Mathematics and Computer Science. [DOI]