Medical Biotechnology

The course aims to provide students with theoretical and practical knowledge related to the application of computational methodologies to the study of complex biological systems, with particular reference to the analysis of omics big data, the use of bioinformatics tools, and the use of molecular dynamics and machine learning techniques. Module 1 - Big Data and Omics Science Knowledge and Understanding Know the basic Unix/Linux shell commands for filesystem management. Become familiar with the basic concepts of genomics and transcriptomics and the main sequencing technologies (first, second and third generation). Understand the organization and content of major biological databases. Ability to apply knowledge and understanding Use shell commands to manipulate files, folders, data streams, and filters (e.g., grep) in big data environments. Apply bioinformatics tools for gene expression analysis, functional annotation, and genomic visualization (e.g., UCSC Genome Browser). Leverage web tools for differential analysis and functional enrichment. Autonomy of judgment Critically evaluate bioinformatics tools, methods, and resources used for omics data analysis. Select the most appropriate strategies for querying, integrating, and analyzing large biological datasets. Communication Skills Effectively present and discuss the results of bioinformatics analyses, using correct scientific terminology and digital communication tools. Learning skills Develop an autonomous and proactive approach to continuous learning in bioinformatics and omics sciences, with emphasis on updating digital resources and databases. Module 3 - Computational Biology and Molecular Dynamics Knowledge and Understanding Gain up-to-date knowledge of computational methodologies for structural analysis of biomolecules, including molecular docking, protein modeling and molecular dynamics. Understand the relationships between protein structure, dynamics and function. Ability to apply knowledge and understanding Use tools for scientific computational sessions and structural analysis of proteins. Model the three-dimensional structure of proteins and simulate the molecular dynamics of soluble and membrane proteins, as well as ligand/protein interactions. Access databases to complete, validate and analyze structural models. Critically interpret simulation results and estimate their biophysical relevance. Autonomy of judgment Independently assess the quality of computational and experimental data. Make informed judgments about the reliability of biological models obtained from simulations or predictions. Communication Skills Communicate methods, results, and conclusions effectively to specialist and non-specialist interlocutors, including in interdisciplinary settings. Learning skills Conduct autonomous computational investigations, including in advanced research settings, while maintaining up-to-date technical and scientific skills.