Course

Unit 1:
Introduction to Bioinformatics and Translational Medicine; Definition and scope of bioinformatics, Overview of translational medicine.
Unit 2:
Biological Databases and Tools; Sequence databases (Gen Bank, Uni Prot), Structure databases (PDB), Bioinformatics software (BLAST, CLUSTALW, etc.).
Unit 3:
Genomic Analysis Techniques; Next-generation sequencing (NGS), Genome assembly and annotation, Variant calling and analysis.
Unit 4:
Systems Biology and Network Analysis; Gene expression analysis, Pathway analysis, Protein-protein interaction networks.
Unit 5:
Molecular Modelling and Drug Design; Protein structure prediction, Molecular docking, Virtual screening for drug discovery.
Unit 6:
Personalized Medicine and Precision Oncology; Genomic profiling in cancer, Pharmacogenomics, Clinical applications of personalized medicine.
Unit 7:
Computational Tools for Clinical Decision Support; Electronic health records (EHR), Predictive modelling, Biomarker discovery and validation.
Unit 8:
Challenges and Future Directions, Data integration and interoperability, Ethical and regulatory considerations, Emerging technologies in bioinformatics and translational medicine.

Preamble

Bioinformatics and Translational Medicine is a graduate-level course that integrates principles from bioinformatics with applications in translational medicine. The course explores how computational techniques can be used to analyse biological data, facilitate biomedical research, and translate discoveries into clinical practice. Topics include genomic analysis, molecular modelling, drug discovery, and personalized medicine. Emphasis is placed on practical skills development and understanding the role of bioinformatics in advancing translational research.

Course outcome

CO1: To understand the principles of bioinformatics and their applications in biomedical research.
CO2: To explore bioinformatics tools and databases for analyzing biological data.
CO3: To learn about genomic analysis techniques and their relevance to translational medicine.
CO4: To investigate computational methods for drug discovery and development.
CO5: To discuss the challenges and opportunities in applying bioinformatics to translational research.
CO6: To develop practical skills in using bioinformatics software and resources.
CO7: To critically evaluate research studies and applications of bioinformatics in translational medicine.

Program outcome (PO)

PO1: Utilize scientific principles and methodologies to design innovative solutions for data analysis, experimentation, and product development for challenges in translational research.
PO2: Recognize the importance of environmental sustainability in translational research and strive to minimize adverse environmental impacts.
PO3: Engage in ethical conduct, leadership, active listening, constructive feedback, and interpersonal communication to facilitate productive collaborations and knowledge exchange.
PO4: Acquire fundamental and advanced knowledge and skills in project management, financial planning, and entrepreneurship relevant to translational research ventures and initiatives.
3 = High Affinity, 2 = Medium Affinity, 1 = Low Affinity, – = No Affinity

Program Specific Outcome (PSO)

PSO1: Addresses the complexity of interdisciplinary sciences in biological and medical contexts.
PSO2: Deals with regulatory affairs in medicine, covering topics such as ethical considerations and regulatory frameworks.
PSO3: Covers compounds as drugs and their efficacy, involving pharmacology and drug development.
PSO4: Explores the intersection of bioinformatics and artificial intelligence in biology and medicine.
PSO5: Deals with technology in personalizing medicine, involving precision medicine approaches.
PSO6: Focuses on communicating and disseminating science and medicine to the public, involving science communication and public outreach efforts.

Jake Chen and Maricel Kann, Translational Bioinformatics, PLOS Computational Biology.