B.Tech in Computational Biology
A 4-year undergraduate programme designed to harness the power of computing, statistics and biology to decode life-systems.
B.Tech in computation biology overview
We empower students to harness data and computational science to solve real biological challenges, opening pathways to impactful careers in healthcare, biotechnology, agriculture, and research. Students experience:
Multidisciplinary integration
of computing, algorithm design, AI/ML with molecular biology, genomics, biophysics and statistics to address complex biological systems
Specialisation opportunities
in domains such as drug discovery, genomics and bio-image analytics, providing a diverse set of potential career paths
Tailored curriculum
designed for data-driven life-science applications, equipping students for roles in software, analytics, bio-informatics and research
Specialised domains
in emerging life science areas such as computational drug discovery, genomic data science, healthcare informatics and agricultural computation
Programme specialisations
The programme offers specialisations for students keen to pursue career in a particular domain.
Computational drug discovery
Uses artificial intelligence, modelling and computational tools to accelerate drug design, screening, repurposing and pharmacological analysis.
Genomic data science
Applies bioinformatics and data science to analyse genomic data, enabling advances in precision medicine and personalised healthcare.
Health informatics
Focuses on managing and analysing healthcare data to improve clinical decision-making, patient care and healthcare systems.
Bioimage analytics and decision support
Uses computational tools and artificial intelligence to analyse medical and biological images for diagnostics and clinical decision support.
Pathogen genomics
Studies the genetic makeup of infectious organisms to support disease surveillance, diagnostics, vaccine development and outbreak control.
Computation in agriculture
Applies data analytics, sensors and AI tools to optimise crop management, resource use and agricultural productivity.
Biological systems modelling
Develops computational models of biological systems to understand cellular processes and support advances in biotechnology, drug discovery and biomanufacturing.
Programme details
Technical competencies delivered
- Biological data generation technologies and their applications in genomics and bioinformatics.
- Statistical methods used in molecular biology, genomics, medical and population genetics research.
- Bioinformatics tools and their practical applications in analysing biological data.
- Computational approaches to solving scientific problems, including algorithm design, time and space complexity, and hardware requirements.
- Command-line and scripting-based computing skills relevant to computational biology.
- Design and development of software systems of varying complexity for scientific applications.
- Understanding the local and global impact of bioinformatics and genomics on individuals, organisations and society.
- Professional, ethical, legal and security considerations in bioinformatics and genomics practice.
- Effective communication of bioinformatics and genomics concepts to diverse audiences.
- Collaborative teamwork to achieve shared scientific goals.
- Commitment to continuous learning and professional development in computational biology.
Career opportunities
- Data analyst
- Application developer
- Software engineer
- Research specialist
- Data manager
- Opportunities for postgraduate studies in areas such as computer science, genomics and clinical informatics.
- Genomics: Omics technologies and novel applications of gene and genome sequencing across diverse biotechnology domains.
- Strand Life Sciences
- Rasa Life Science Informatics
- MarksMan Healthcare Communications
- Xcode Life Sciences
- Natural Text
- Helix Biosciences
- Clevergene
- Nucleome Informatics
- AgriGenome Labs
- BD Biosciences
- Biogenick
- Genotypic Technology
| Course | L-T-P | Credits |
|---|---|---|
| BT1101/ BT1102 – Basic Mathematics I / Introduction to Biology* | 3-0-0 | 3 |
| BT1103 – World of BT & CB | 1-0-0 | 1 |
| BT1104 – Chemistry of Biomolecules | 3-0-2 | 4 |
| BT1106 – Biological Systems: Pathways & Signaling | 3-0-0 | 3 |
| CS1101 – Introduction to Computing | 2-1-2 | 4 |
| CE1101 – Earth and Environmental Sciences | 2-0-0 | 2 |
| HS1101 – English | 0-3-0 | 3 |
| HS1102 – Media Project | 0-0-2 | 1.5 |
| ET1101 – Introduction to Entrepreneurship | 0-0-3 | 1 |
| FL1103 – French- I | 0-2-0 | 0.5 |
| Total Credits | 23 |
| Course | L-T-P | Credits |
|---|---|---|
| BT1206 – Molecular Genetics | 3-0-2 | 4 |
| BT1210 – Biophysics | 2-0-2 | 3 |
| CB1201 – Programming Workshop (C++) | 0-0-2 | 1 |
| CB1202 – Programing Workshop (Linux) | 0-0-2 | 1 |
| MA1202 – Linear Algebra & Complex Analysis | 3-1-0 | 4 |
| CS1202 – Discrete Mathematics | 2-0-0 | 2 |
| CS1002 – Data Structures | 3-0-2 | 4 |
| ET1201 – Entrepreneurship Practice | 1-0-0 | 1 |
| HS1202 – Professional Ethics | 0-1-0 | 1 |
| SC1201 – SDGs And Our Career | 1-0-0 | 1 |
| FL1203 – French-II | 0-2-0 | 0.5 |
| Total Credits | 22.5 |
| Course | L-T-P | Credits |
|---|---|---|
| CB2103 – Fundamentals of Data Science | 1-0-2 | 2 |
| CB2104 – Computational Biology | 3-0-2 | 4 |
| CB2105 – Data Engineering & Visualization Workshop [ETL Pipeline & Tableau] | 0-1-2 | 2 |
| CB2106 – Programming Workshop (Python & BioPython) | 0-0-2 | 1 |
| MA2103 – Probability & Statistics | 3-1-0 | 4 |
| CS2102 – Design and Analysis of Algorithms | 3-0-2 | 4 |
| ET2101 – Lean Start-up (Fractal) | 1-0-0 | 1 |
| HS2102 – Principles of Economics (Fractal) | 3-0-0 | 1.5 |
| SC2102 – Sustainability and AI | 1-0-0 | 1 |
| FL2103 – French-III | 0-2-0 | 0.5 |
| Total Credits | 21 |
| Course | L-T-P | Credits |
|---|---|---|
| BT2216 – Omic Technologies | 3-0-2 | 4 |
| CB2207 – Structural Bioinformatics & Drug Discovery | 3-0-2 | 4 |
| CB2208 – Biological Database Management | 3-0-2 | 4 |
| CB2209 – ML Workshop | 0-0-2 | 1 |
| CS3102 – Machine Learning | 3-0-0 | 3 |
| HS2201 – Design Thinking | 1-0-2 | 2 |
| HS2202 – Financial Accounting | 3-0-0 | 1.5 |
| SC2201 – Water and Us | 1-0-0 | 1 |
| FL2203 – French-IV | 0-2-0 | 0.5 |
| Total Credits | 21 |
| Course | L-T-P | Credits |
|---|---|---|
| BT3125 – Microbes and Immune Systems | 3-0-2 | 4 |
| CB3110 – NLP Workshop | 0-0-2 | 1 |
| CB3111 – Computational Genomics & Systems Biology | 3-0-2 | 4 |
| CB3112 – Programming Workshop (R) | 0-0-2 | 1 |
| CS3105 – Object Oriented Programming | 2-0-2 | 3 |
| CS3106 – Web Tech Workshop | 0-0-2 | 1 |
| CS3126 – NLP | 3-0-0 | 3 |
| CB30XX – Elective – I | 3-0-0 | 3 |
| CB30XX – Elective – II | 3-0-0 | 3 |
| HSS Elective – HSS Elective -I | 2-0-0 | 2 |
| Total Credits | 25 |
| Course | L-T-P | Credits |
|---|---|---|
| BT3228 – Research Methods & IPR | 2-0-0 | 2 |
| CB3213 – Algorithms in Bioinformatics | 2-0-2 | 3 |
| CB3214 – Programming Workshop (Java & BioJava) | 0-0-2 | 1 |
| CB3215 – Generative AI in Life Sciences | 3-0-2 | 4 |
| CS3206 – Web Tech Workshop | 0-0-2 | 1 |
| CS3223 – Deep Neural Networks | 3-0-2 | 4 |
| CB30XX – Elective – III | 3-0-0 | 3 |
| CB30XX – Elective – IV | 3-0-0 | 3 |
| HS3201 – Introduction to Professional Development (Fractal) | 2-0-0 | 2 |
| HSS Elective – HSS Elective-II | 2-0-0 | 2 |
| Total Credits | 25 |
| Course | L-T-P | Credits |
|---|---|---|
| BT4129 – Employability Skills | 1-0-1 | 1.5 |
| CB40XX – Elective – V | 3-0-0 | 3 |
| CB40XX – Elective – VI | 3-0-0 | 3 |
| CB40XX – Elective-VII | 3-0-0 | 3 |
| HSS Elective – HSS Elective-III | 2-0-0 | 2 |
| Total Credits | 12.5 |
| Course | L-T-P | Credits |
|---|---|---|
| PR4000 – Research Project (Industrial Internship/R&D Institute with major research project) | 0-0-30 | 15 |
| Total Credits | 15.0 |
| S.No. | Electives |
|---|---|
| 1 | Biomedical Imaging |
| 2 | Public Health Informatics |
| 3 | Clinical Trials – Design and Analysis |
| 4 | Evolutionary Genomics of Pathogens |
| 5 | Digital Twins & Disease Modelling |
| 6 | Computational BioTherapeutic Desgining |
| 7 | Population Genomics |
| 8 | AI in Pharmacovigilance |
| 9 | Digital Health Informatics |
| 10 | Infectious Disease Modelling |
FAQs
Yes. The programme is designed for students with a strong analytical mindset. Biology concepts are introduced progressively and taught alongside computational methods, allowing students to build biological understanding without requiring prior deep expertise in life sciences.
The programme is academically rigorous, as it combines computing, statistics and biology. However, the curriculum is structured to balance theory with application, ensuring students are supported as they move across disciplines rather than being overwhelmed by them.
Graduates are well-prepared for a range of roles. In addition to life-science and biotech positions, students develop strong programming, data analysis and modelling skills that are applicable to software, analytics and data-science roles, depending on electives and projects chosen.
Yes. The interdisciplinary training in computation, statistics and biology aligns well with global postgraduate programmes in computational biology, bioinformatics, data science, biomedical engineering and related research-oriented fields.
This programme is ideal for students who are curious about how technology can solve biological and healthcare problems, enjoy working with data, and are comfortable learning across disciplines rather than focusing on a single traditional engineering stream.
