MS Bio Informatics


The aim of this MS degree programme is initially to develop faculty for this new discipline. The objective is to provide the students with an advanced knowledge and training that will help them to decipher the biological processes. The expansive nature of biological data can only be translated effectively into knowledge by the use of information technology, as has recently been demonstrated by solving some of the mysteries of gene sequence analysis and decoding the human genome. The students should have thorough knowledge about the data storage and retrieval systems; the types and sources of databases used in biotechnology and how to retrieve data from genetic databases.;
Eligibility for Admission:
As per Advertisement
2 years: 4 semesters
(Course work should be completed in two semesters; one year for research)
Total Credit Hours
30 (24 CH course work + 6 CH thesis)
Research Thesis (6 Credit Hours)
  • Duration of the research project will be at least one year. An independent research topic chosen by the student in consultation with a regular full time faculty member of the department is required.
  • The research work of each student will be reviewed periodically by the supervisor / head of department to ensure the objectives laid down for study are being met.
  • All students must present and defend their research work before the panel of examiners as per the rules of the university.
Medium of Study:
Program Level:
Post Graduate
Scheme of Study
Fall Semester (9 Credit Hours)
Sr. Course   Cr. Hr
1 Advanced Molecular Biology   3
2 Advanced Bioinformatics   2
3 Research Methodology   1
4 Modeling and Simulation   3
Spring Semester (9 Credit Hours)
5 Advanced Computational Algorithms   3
6 Advanced System Analysis & Design   3
7 Computational Biology   3
Summer Semester (6 Credit Hours)
(Choose 2 courses of 3 credit hours courses from the following specialized courses)
  1. Bioinformatics: Algorithms and System

  2. Advance Biochemistry

  3. Perl Programming

  4. Microarray Data Analysis

  5. Proteomics

  6. Artificial Intelligence

  7. Neural Computing and Genetics Algorithms

  8. Protein Classification and Structure Prediction

  9. Understanding Cancer

  10. Theory and Application of Bioinformatics

  11. Metabolobonomics

  12. Nanotechnology

  13. Statistical Methods for Computational Biology

  14. Image Processing

  15. Data Mining

  16. Applied Graph Theory & Algorithms

  17. Biometry and Quantitative Genetics

  18. Applied Biotechnology

  19. Cytogenetics

  20. Mathematical Methods for DNA sequences

  21. Gene Mining

  22. Drug Design & Development

  23. Applications and Commercial Aspects of Bioinformatics


Note: Students must select the specialized course (s) from the above list and will be subjected to the offering by the concerned department.

Course Outlines
As recommended by HEC