Program Overview
The course will introduce computational approaches recently developed for studying biological systems with a focus on biotechnological applications: the identification of essential genes (Tn-Seq and network analysis), or genes that are involved in interesting processes (Tn-Seq) together with methods to study gene regulation (ChIP-Seq, small RNAs). On these premises we will then discuss how to engineer eco-systems (community engineering) and how metabolic optimization can be achieved (model-guided metabolic engineering).
An introduction to computational methods for the characterization of protein structures, with biochemical basis.
Key Program Features
Key Features:
- Fully taught in English
- Interdisciplinary curriculum combining molecular biology, genomics, proteomics, metabolomics, and computational biology
- Strong laboratory component with hands-on thesis internship
- Elective courses for specialisation in microbial, animal, or plant biotechnology
- Erasmus+ partnerships with universities in Germany, Spain, France, Norway, Denmark, and the Netherlands
Career Opportunities
The course will introduce students to the computational techniques that are at the basis of the identification of important genes in Tn-seq datasets and to the structural analysis of networks with the aim of identifying genes that can be manipulated for specific objectives. Techniques to study gene regulation will also be discussed (ChIP-Seq, sRNA).
In the part of the course relating to the computational study of proteins, the student will learn the biochemical and biophysical bases on which the secondary and tertiary structure prediction algorithms, structural disorder and protein dynamics are based. The student will also directly perform a series of prediction tests by learning to use structural analysis and prediction programs.
Program Curriculum
Introduction: definition and aims of Bioinformatics. Genome projects and next-generation sequencing. Gene and genome annotations. A bioinformatic view of the structure of protein-coding genes: exons, introns, promoters, and alternative splicing. The structure of mature eukaryotic mRNAs. Primary and specialized biological databases. Genome browsers. Definition of sequence similarity, homology, orthology, and paralogy. Global and local alignments. Scoring matrices for nucleotide and amino acid sequence alignments (PAM and BLOSUM). BLAST sequence similarity search: algorithm and usage. Multiple sequence alignments. Expression data and RNA-Seq. Functional gene annotation and gene ontology.
Teaching methods
Theoretical lectures will be alternated with practical exercises with the PC.
Teaching Resources
Slides and handouts will be shared with students.
Reference textbook (suggested):
M. Helmer Citterich, F. Ferrè, G. Pavesi, C. Romualdi, G. Pesole, Fondamenti di bioinformatica, Zanichelli editore 2018
Computational Biology
Course syllabus
- Exploring function and regulation
- Transposon insertion mutagenesis for the discovery of essential or otherwise important genes
- ChIP-Seq, transcription factor binding sites and gene regulatory networks;
- Metagenomics and metatranscriptomics
- Small RNAs in Bacteria, mechanisms of action, function and dynamical behavior of small genetic circuits implementing sRNA-mediated regulations;
- Introduction to network theory with applications in Biology (+Practice in R).
- Protein Structure and its analysis
- The main chemical and geometrical properties of protein structures will be shown: secondary (alpha helix, beta sheets and coil) and tertiary structures. TIM barrel will be used as an example of protein fold ductility.
- Covalent and non-covalent bonds are fundamental for protein folding: peptide bond, salt bridges, van der Waals interactions and hydrogen bonds. The role of water in protein folding.
- Computer analysis of protein structures to verify several of the protein properties discussed during course.
- The evolution of the structure of globular proteins, of membrane proteins and of intrinsically disordered proteins will be accompanied by test of protein structure predictions.
- Protein structure prediction by homology modelling
- Protein-protein and protein-ligand docking algorithms
- Protein structure prediction with machine learning methods
Admission Requirements
- Bachelor's degree with at least 60 ECTS in biotechnology-related subjects (including biochemistry, molecular biology, genetics, microbiology)
- At least 10 ECTS in mathematics, physics, computer science, or statistics
- At least 10 ECTS in chemistry
- English proficiency at B2 level or above (CEFR)
- GPA equivalent to 88/110 or higher (23/30 average) is considered adequate preparation
Tuition & Financial Information
Detailed tuition information is not available. Please contact the university for the most current tuition and fee information.
Application Deadline
January to August
About University of Milan
University of Milan
Milano, Italy
University of Milan is a distinguished institution of higher education committed to academic excellence, innovative research, and preparing students for leadership in their chosen fields. The...
University Profile- Application Deadline January to August
- Start Date April 2026
- Language English
- Duration 2 years
- Credits 120 ECTS
