BIOVISUAL LAB
Bioinformatics and Computational Biology
Explore all 7 topics in S11 - Bioinformatics and Computational Biology. Each topic includes detailed content, subtopics, and interactive labs.
S11
Bioinformatics and Computational Biology
Sequence analysis, modeling, structure prediction, drug design, and systems biology.
All Topics
Click on any topic to explore detailed content, subtopics, and interactive labs.
A. Major Bioinformatic Resources
Major Bioinformatic Resources. Sequence databases, gene expression databases, 3D structure database, pattern sequence databases
B. Basic Concepts of Sequence Analysis
Basic Concepts of Sequence Analysis. Database searches, BLAST and FASTA, sequence identity and similarity, definitions of homologues, orthologues, paralogues, repeat finding, scoring matrix, pairwise sequence alignments, multiple sequence alignments (MSA), application in taxonomy and phylogeny, comparative genomics
C. Gene annotation
Gene annotation. Prediction of gene function using homology, context, structures, networks; Genetic variation- polymorphism, deleterious mutations; Phylogenetics
D. Molecular Modelling and Dynamics
Molecular Modelling and Dynamics. 3-D structure visualization and simulation, Basic concepts in molecular modeling, Molecular Mechanics, Force fields etc
E. Classification and comparison of protein 3D structures
Classification and comparison of protein 3D structures. Anatomy of proteins – Hierarchical organization of protein structure, Secondary and tertiary structure prediction, homology/comparative modeling, fold recognition, threading approaches, and ab initio structure prediction methods, AI-based methods of structure prediction (eg. AlphaFold)
F. Drug design
Drug design. Chemical databases like NCI /PUBCHEM, Fundamentals of Receptor-ligand interactions, Structure-based drug design, Ligand based drug design: Structure-Activity Relationship, QSARs and pharmacophores, in silico predictions of drug activity and ADMET
G. Systems Biology
Systems Biology. Data science applications in biology, health and drug discovery, mathematical modelling of metabolic pathways and disease, digital health, personalized medicine