Applied Bioinformatics - Basics To Network Biology

Detailed insights of bioinformatics concepts, protein structure prediction, network biology and SBML

What you'll learn

Apply bioinformatics tools to analyze DNA, RNA, and protein sequences for functional annotation and biological interpretation.

Use protein modelling algorithms to predict and validate 3D structures based on sequence and structural similarity.

Interpret mass spectrometry data to identify peptides, proteins, and post-translational modifications using computational tools.

Build and analyze biological networks to explore protein interactions and gene regulation in complex biological systems.

Requirements

Basic biology knowledge needed.

Description

Applied Bioinformatics – Basics to Network Biology is an interdisciplinary field that integrates computational tools and biological knowledge to understand complex biological systems. It begins with basic bioinformatics, which covers fundamental topics such as sequence alignment, gene annotation, molecular evolution, and database mining. These foundational skills enable the analysis of DNA, RNA, and protein sequences for functional and structural insights. A significant aspect of applied bioinformatics is protein structure prediction, which uses computational models to determine the 3D conformation of proteins, critical for understanding molecular function and drug-target interactions. Techniques like homology modeling, threading, and ab initio predictions are commonly employed here. As research progresses into systems-level understanding, network biology becomes essential. This involves the construction and analysis of biological networks, such as protein-protein interaction (PPI) networks, gene regulatory networks, and metabolic pathways. These networks help in identifying key regulatory molecules, potential drug targets, and emergent biological properties. A pivotal tool in modeling and simulating such networks is SBML (Systems Biology Markup Language), a standardized XML-based language that allows for the exchange and analysis of computational models in systems biology. Together, these areas form a comprehensive framework for understanding biology from molecules to networks, facilitating innovations in diagnostics, therapeutics, and biotechnology.

Overview

Section 1: Genetic Mapping, SNP, EST, GSS, Molecular Predictions with DNA sequence

Lecture 1 Comparative Genomics, Genetic mapping, Physical mapping, SNPs, ESTs, GSS

Lecture 2 Molecular Predictions with DNA sequence, Network Biology

Section 2: Protein Structure Prediction: Need, Algorithms, and their limitations

Lecture 3 Need for protein structure prediction, Methods of Structure prediction

Lecture 4 Methods of Structure Prediction - Homology Modelling

Section 3: PROTEIN-PROTEIN INTERACTION NETWORK ANALYSIS

Lecture 5 Protein Identification and interaction

Lecture 6 Unraveling Protein Interactions: A Network Analysis Deep Dive.

Section 4: SAGE, Microarray, and Metabolic Networks

Lecture 7 Biological pathway databases – Reactome, SAGE, Microarray

Lecture 8 Metabolic Network

Lecture 9 Systems Biology, SBML and Systems Biology Workbench

Lecture 10 Assignment

Bioinformatics students curious to learn protein modelling, network biology and gene prediction strategies with their limitations,Researchers curious to learn metabolic network and its construction strategies