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.
Our Mission
To develop innovative computational methods and tools that advance biological research and contribute to the understanding of complex biological systems, ultimately leading to improved healthcare outcomes.
Our Vision
To be an architect of biological discoveries, transforming multiscale data into actionable insights and knowledge through algorithms and high-fidelity biological models.
What We Do
In Computational System Biology Lab we mainly focus on developing algorithms, web tools, and software for the enhancement of computational techniques in biological research. We work on different aspects of biological network modelling based on resources available in the public domain.
Different environmental factors causing disease conditions imprint their effect on biological systems. Integrating different levels of data like genomic, proteomic, and metabolomic leads to deep insight into disease study which helps understand disease with target-specific therapeutics.
Such studies are not only computationally challenging but also require sophisticated computational techniques. We develop algorithms based on computational techniques like machine learning and statistical modelling.
Research Focus Areas
System Biology
Holistic approach to understanding biological systems through computational modeling and analysis.
Biological Network Analysis
PPI networks, gene regulatory networks, signalling pathways, and graph-theoretic modelling.
Machine Learning & AI
Classification, prediction, feature selection, and deep learning for biological datasets.
Multi-Omics Integration
Combining genomics, proteomics, and metabolomics for comprehensive disease characterization.
Structural Bioinformatics
Molecular modelling, docking, and computational drug discovery approaches.
Biomarker Discovery
Identification of molecular biomarkers for disease diagnosis and prognosis.
Statistical Modelling
Bayesian inference, multivariate analysis, and high-dimensional statistical frameworks.
Web Tool Development
Interactive bioinformatics platforms, databases, and analytical web applications.