Computational System Biology Laboratory

We develop algorithms, web tools, and software to advance biological research combining network modelling, multi-omics integration, and machine learning to decode disease mechanisms and identify therapeutic targets.

9+ Publications
5+ Tools Built
5+ Researchers

About the Laboratory

The Computational System Biology Laboratory (CSBL) is part of the Department of Bioinformatics at the University of North Bengal. Our lab is dedicated to advancing the field of computational biology through innovative research, algorithm development, and the creation of cutting-edge bioinformatics tools.

We focus on bridging the gap between computational science and biological research, developing solutions that help researchers worldwide understand complex biological systems and disease mechanisms.

By integrating genomic, proteomic, and metabolomic data layers, we produce deep insights into disease mechanisms—enabling identification of target-specific therapeutics through machine learning and statistical modelling.

Research Focus Areas

  • System Biology
  • Biological Network Analysis
  • Machine Learning & AI
  • Multi-Omics Integration
  • Drug Target Discovery
  • Web Tool Development

Core Research Areas

Interdisciplinary computational approaches to biological and biomedical challenges

Machine Learning & AI

Classification, prediction, feature selection, and deep learning for high-dimensional biological datasets.

SVM Random Forest Neural Networks

Biological Network Analysis

PPI networks, gene regulatory networks, signalling pathways, and graph-theoretic modelling.

PPI Pathways Graph Theory

Multi-Omics Integration

Combining genomics, proteomics, and metabolomics for comprehensive disease characterization.

Genomics Proteomics Metabolomics

Computational Drug Discovery

In-silico screening, molecular docking, ADMET prediction for novel therapeutic targets.

Docking Screening ADMET

Statistical Modelling

Bayesian inference, multivariate analysis, and high-dimensional statistical frameworks.

Bayesian Regression Multivariate

Web Tool Development

Interactive bioinformatics platforms, databases, and analytical web applications.

Web Apps Databases APIs

Recent Publications

Tools & Software

Explore our developed bioinformatics tools, R packages, and web applications.

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Research Team

Meet our faculty, PhD scholars, and research students working in computational biology.

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Collaborations

We welcome researchers and institutions interested in computational biology collaborations.

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Interested in Collaboration?

We welcome researchers, students, and institutions to work with us.