Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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Linear algebra and analysis
2° Year It will be activated in the A.Y. 2026/2027
| Modules | Credits | TAF | SSD |
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3° Year It will be activated in the A.Y. 2027/2028
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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Linear algebra and analysis
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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One module to be chosen among:
- 2nd year - Discrete Biological Models - delivered in 2026/2027
- 3rd year - Database technologies, Advanced machine learning techniques for biomedical data, Network programming, Signal and image processing II - delivered in 2027/2028One module to be chosen among:
- 2nd year - Elements of physiology, Biophysics - delivered in 2026/2027
- 3rd year - Model organism in biotechnology research, Molecular biology laboratory - delivered in 2027/2028Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Discrete Biological Models (2026/2027)
Teaching code
4S01908
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Courses Single
Authorized
The teaching is organized as follows:
Teoria
Esercitazioni
Learning objectives
The aim of the course is to present methods from discrete mathematics which are employed in the analysis of biological phenomena, with a major emphasis on the computational analysis of genomes. At the end of the course the students will be able to apply discrete probability and information theoretic tools for analysing genomic data.
Prerequisites and basic notions
notions of basic mathematics
Program
In this course we will introduce discrete models for attacking some computational problems arising in biological research: We will see how combinatorial structures can be applied to computational biology problems. In particular, we will study several of the following topics:
- strings for modelling DNA, RNA, and proteins
- graphs for modelling molecules
- graphs for modelling interactions between proteins (protein interaction networks)
- pangenomes for modelling of human genomic variation (with strings or with graphs)
- haplotyping, modelled with strings and with matrices
- genome rearrangements, modelled with permutations and with strings
- DNA sequencing (layouts, graphs)
- mass spectrometry data (strings)
- Fibonacci sequence
- malthusian models of population growth
- criteria for solving linear recurrences
- dynamic study of the logistic map
- metabolic dynamics modelled with graphs (metabolic networks)
Didactic methods
lectures and exercise sessions
Learning assessment procedures
Written exam, consisting of practical applications (exercises) as well as theoretical questions (problems studied, mathematical properties of different models, which models exist for a given problem etc.).
Evaluation criteria
Knowledge of the topics studied in the course, ability to explain and reproduce the models studied; where applicable, the algorithms for solving the problems studied. Both theory and application on specific examples.
Criteria for the composition of the final grade
The final grade is the average between the grade for Theory and the grade for Exercises.
Exam language
Italiano
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