Bioinformatics in Drug Discovery
Program Overview
Bioinformatics is a multidisciplinary field that merges biology, computer science, and information technology to analyze and interpret biological data. Here are the key topics within bioinformatics in the Drug Discovery program:
Sequence Analysis
Study DNA, RNA, and protein sequences to identify genes, regulatory elements, motifs, and evolutionary relationships. Techniques include sequence alignment, genome assembly, and motif discovery.
Structural Bioinformatics
Explore the 3D structures of biological molecules (proteins, nucleic acids) to understand their function, interaction, and design of drugs or new proteins. This includes protein structure prediction and molecular docking.
Genomics
Analyze whole genomes to understand the genetic basis of diseases, traits, and evolution. This involves genome sequencing, annotation, comparative genomics, and functional genomics.
Transcriptomics
Study RNA transcripts to understand gene expression patterns, regulation, and the role of non-coding RNAs. This includes RNA-Seq, microarrays, and single-cell RNA sequencing.
Proteomics
Investigation of the proteome, the entire set of proteins expressed by a cell or organism, to understand protein function, interactions, and modifications. Techniques include mass spectrometry, protein-protein interaction networks, and post-translational modification analysis.
Metabolomics
Profile metabolites within cells or tissues to study metabolic pathways and understand the biochemical activity in different conditions or diseases.
Phylogenetics
Reconstruction of evolutionary relationships between organisms or genes using sequence data. This involves constructing phylogenetic trees and studying evolutionary patterns.
Systems Biology
Modeling and analyzing complex biological systems and networks (e.g., gene regulatory networks, metabolic networks) to understand biological systems’ interactions and emergent properties.
Computational Biology
Develop algorithms, models, and tools to solve biological problems, such as modeling biological processes, simulating molecular dynamics, and analyzing large-scale biological data.
Data Mining and Machine Learning in Bioinformatics
Apply statistical and computational methods to extract meaningful patterns from large biological datasets, such as predicting protein functions, identifying disease biomarkers, and clustering gene expression data.
Comparative Genomics
Comparing genomes of different species to identify conserved elements, understand evolutionary changes, and infer functional genomics.
Epigenomics
Study epigenetic modifications (like DNA methylation, and histone modification) that regulate gene expression without altering the DNA sequence.
Population Genomics
Analyze genetic variations within and between populations to understand population structure, migration patterns, and evolutionary dynamics.
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Duration: 12 weeks.
45-50 hours to complete.
- Level: Basic introductory and Advanced applications.
- Prerequisite: A biology background is required. A Computer Science background is preferred but not mandatory.
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Fee: 20 000 EGP
Discount Policy for Group Registration!
- Program timeframe:
- Registration
- Be contacted to confirm the participation
- Scheduling for the program sessions
- Supplied with the technical support to download the software required for the program