Introduction

This intensive 4-months Certificate Course in Computational Biology is designed for life sciences professionals seeking to enhance their skills in bioinformatics. The program combines foundational theory with hands-on training, covering key topics such as sequence analysis, genomics, proteomics, computational tools with practical application of Machine Learning, python programing and R language in data analysis.
Participants will learn to analyze biological data, interpret results, and apply computational techniques in real-world research and clinical settings. The certificate course emphasizes practical application through projects & assignment based learning, enabling professionals to integrate computational biology skills into their careers and contribute to advancements in healthcare, biotechnology, and life sciences.

Career Advancement: Prepares students for careers in bioinformatics, computational biology, and systems biology by covering essential and advanced topics.

Research-Ready Skills: Equips learners with hands-on experience in tools, programming, and techniques crucial for computational biology research.

Interdisciplinary Competence: Bridges biology with computer science, enhancing the ability to work on interdisciplinary projects.

Problem-Solving Abilities: Develops analytical and computational problem-solving skills applicable to real-world biological problems.

Time-Efficient Learning: Weekend-only classes balance professional and academic commitments, making it accessible for working professionals and students.

  • Explain the significance of bioinformatics in modern research and healthcare.
  • Analyze genetic variations and evolutionary trends using bioinformatics software.
  • Analyze next-generation sequencing (NGS) data for genomic insights, including variant detection and annotation.
  • Perform differential gene expression analysis and visualize transcriptomic patterns.
  • Correlate protein structure with function, enhancing the understanding of bio molecular mechanisms.
  • Analyze protein-ligand interactions to predict drug efficacy and specificity.
  • Understand basic machine learning concepts and their applications in biological research.
  • Perform data manipulation, visualization, and basic bioinformatics computations using Python libraries.

Course Duration = 4 months (16 Weeks)

Class Schedule = Saturdays or Sunday, 4 hours each

Credit Hours = 4

Total Hours = 64 hours

Mode of Classes = Physical (Baqai Institute of Information Technology, Baqai Medical University, Nazimabad 3).

Starting from 18th Jan 2025

Last Date to Apply: 15th Jan 2025

Registration Link:

Course Fees: 40000/ installment available

25% only those who can confirm seats before 06th Jan.

Payment Methods: Easy Paisa on 0332-2799327

Week

Topic to be Covered

Theory / Lab hours

Learning outcome

1

Introduction to Computational Biology

1 Theory

3 Labs

· Understand the scope, applications, and importance of computational biology.

· Identify basic tools and resources in computational biology.

2

Biological Databases and Data Retrieval

1 Theory

3 Labs

• Learn about primary databases (GenBank, PDB, UniProt).

• Gain hands-on experience in retrieving data and understanding data formats.

3

Sequence Alignment Techniques

1 Theory

3 Labs

· Understand sequence alignment types (global, local).

· Perform sequence alignment using tools like BLAST and Clustal Omega.

· Interpret alignment results in the context of homology and function.

4

Phylogenetic and Evolutionary Analysis

1 Theory

3 Labs

· Explain evolutionary relationships using phylogenetic trees.

· Create and interpret phylogenetic trees using tools like MEGA or PhyML.

5

Genomics (NGS) data Analysis with Linux Basis

2 Theory

· Understand genomics and NGS technology workflows and analysis.

6

6 Lab

7

Transcriptomic Data Analysis with R Programming

3 Theory

· Understand the Basics of R

· Perform RNA-Seq data processing, differential expression analysis, and data visualization.

8

9 Lab

9

11

Protein Structure Prediction and Analysis

1 Theory

3 Lab

· Explain basic concepts of protein structure, folding, and stability.

· Use tools like AlphaFold or SWISS-MODEL for 3D structure prediction.

· Interpret structures using visualization software like PyMOL.

12

Molecular Docking and Drug Design

1 Theory

3 Lab

· Describe the principles of molecular docking and its applications.

· Perform molecular docking using AutoDock or similar software.

· Analyze docking results for potential drug discovery applications.

13

Machine Learning Applications in Computational Biology

2 Theory

· Understand the basics of machine learning and its applications in biology.

· Gain practical experience with simple machine learning models for biological data analysis.

14

6 Lab

15

Python for Biologist

2 Theory

• Understand the basics concept of Python programing and its global application

· Gain hands-on training on biological data with python scripts

16

6 Lab
Students, working professional, Experienced Scientists /Research fellows (Universities & other R&D institutions) with specialization in Biotechnology, Botany, Zoology, Biochemistry, Microbiology, Genetics, Bioinformatics, Biomedical sciences, Chemistry, Pharmaceutical Sciences, Computational biology, computational chemistry, Molecular Biology, Cell Biology, and other life science areas are suitable for this training
Furrukh Zaman (Associate Professor & Director BIIT)
  • Khalida Naveed (Assistant Professor, BIIT)
  • Abdul Rafay Khan (Bioinformatician, Senior Research Officer, SIUT)
  • Fahad Khan (Bioinformatican, Data Analyst)
Address III-B-3/17, Nazimabad Karachi, Behind Baqai Hospital Nazimabad
Email Address biit@baqai.edu.pk
Phone Number 0332-2799327
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