M.Tech. in Computational Biology

Overview

The genomic revolution in biology enables one to answer many questions in medical sciences like personalized medicine, the etiology of diseases like cancer, HIV, etc. However, the answers to these questions are impossible without a support of powerful computational and statistical tools that helps to understand and uncover the underlying network design principles responsible for these diseases. With the advent of new biotechnological techniques massive amounts of genomics data are generated at a rapid pace from the experiments and the analysis of these data requires tremendous amount of domain knowledge, solid computational background and strong programming skills. The entry cost of this highly interdisciplinary field consists of a good amount of understanding of molecular biology, genomics, algorithms, programming, statistical computation, machine learning, stochastic processes, and other mathematical techniques that underlie biological design principles. Therefore, it is imperative to stitch biology, statistics, algorithms and mathematical models to analyze and interpret large-scale genomic and biological data.  Though the need and potential applications of computational biology and bioinformatics is tremendous in India, currently very few groups have strength and capability in this area. IIIT-Delhi, with its strong focus on research, and already having a good faculty in various CS and EE, is well suited to build a strong theoretical MTech program in Computational Biology.

 

Program Structure

In the MTech program, the student will do 32 credits of courses and 16 credits for a Thesis, for a total of 48 credits. For courses, up to 12 credits can be from CSE courses from a list of courses approved by the PG Committee. Some of them may be compulsory. Currently the list of approved courses is:

  • Graduate Algorithms (or equivalent) - Compulsory
  • HipC
  • Machine learning
  • Bigdata analytics
  • Probabilistic Graph models

The student will have to do a minimum of 20 credits of course work in Computational Biology – a few will be compulsory as defined by PG Committee, others will be electives. A sample of list of courses is given below – this list will evolve over time.

  • Foundations of Modern biology
  • Practical Bioinformatics
  • Systems and Synthetic Biology
  • Introduction to mathematical biology
  • Stochastic Simulations in Systems Biology and Biophysics
  • Molecular mechanics and Biological physics
  • Computational Neuroscience
  • Biostatistics
  • Function Genomics and Data mining

In electives, at most 4 credits of “Independent Study/Project” can be taken.

 

Thesis: Student will be required to do a thesis in Computational Biology – there is no scholarly paper option.

 

Research Areas

  • Computational systems and theoretical biology
  • Stochastic methods in systems biology
  • Bioinformatics
  • Structural biology

Faculty

The following faculty members is offering courses and guiding thesis and scholarly papers in the area of computational biology.

  • K. Sriram, Systems and theoretical biology
  • S. Raychaudhuri, Systems biology, immunology, and Monte-carlo simulations.
  • Arnab Bhattacharjee, Biophysics and Molecular modeling
  • Sikander Hayat, Structural bioinformatics
  • Rohit Gupta, Genomics, Data Mining, Machine learning, Bioinformatics.
  • Srinivasan Ramachandran, Integrative Data analytics of biological Big Data, predictive modelling.
  • Vinod Scaria, Computational Biology and Genome Informatics
  • Debasis Dash, Algorithm and tools development, high throughput data analysis for comparative genomics, Proteome Informatics.
  • Lipi Thukral, Multiscale Biomolecular Simulations, Structural Bioinformatics, Modeling and Visualization of Complex Molecular Data, Medical Informatics.

Career Options after Graduation:

With the successful completion of this program, graduates will acquire the necessary knowledge and skill set to be employed in companies that are working in the areas of computational biology. Graduates are well prepared to pursue PhD or advance level research in renowned research labs in this area.