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

A.A. 2024/2025 A.A. 2023/2024 A.A. 2022/2023 A.A. 2021/2022 A.A. 2020/2021 A.A. 2019/2020 A.A. 2018/2019 A.A. 2017/2018 A.A. 2016/2017 A.A. 2015/2016 A.A. 2013/2014 A.A. 2012/2013 A.A. 2011/2012 A.A. 2010/2011 A.A. 2009/2010 A.A. 2005/2006

This information is intended exclusively for students already enrolled in this course.
If you are a new student interested in enrolling, you can find information about the course of study on the course page:

Laurea in Bioinformatica - Enrollment from 2025/2026

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

ModulesCreditsTAFSSD
6
A
MAT/02
6
A
MAT/05
6
C
BIO/13
6
B
ING-INF/05
12
C
CHIM/03 ,CHIM/06
6
A
FIS/01
6
E
L-LIN/12
12
A
INF/01

2° Year  activated in the A.Y. 2022/2023

ModulesCreditsTAFSSD
12
B
INF/01
6
C
BIO/11
6
C
BIO/10
12
B
INF/01 ,ING-INF/05
6
C
BIO/18
6
A
INF/01
6
A
MAT/06
1 module among the following
6
C
FIS/07
6
C
BIO/09
6
C
MAT/05
6
C
BIO/11

3° Year  activated in the A.Y. 2023/2024

ModulesCreditsTAFSSD
12
B
INF/01
12
B
INF/01
12
C
BIO/10
1 module among the following
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
INF/01
Final exam
3
E
-
activated in the A.Y. 2022/2023
ModulesCreditsTAFSSD
12
B
INF/01
6
C
BIO/11
6
C
BIO/10
12
B
INF/01 ,ING-INF/05
6
C
BIO/18
6
A
INF/01
6
A
MAT/06
1 module among the following
6
C
FIS/07
6
C
BIO/09
6
C
MAT/05
6
C
BIO/11
Modules Credits TAF SSD
Between the years: 2°- 3°
12
D
-
Between the years: 2°- 3°
Further activities
3
F
-

Legend | 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.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Discrete Biological Models (2023/2024)

Teaching code

4S01908

Credits

6

Coordinator

Zsuzsanna Liptak

Language

Italian

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Courses Single

Authorized

MoodleMoodle Seminars 0

The teaching is organized as follows:

Teoria

Credits

4

Period

Semester 2

Academic staff

Zsuzsanna Liptak

Lessons timetable
Esercitazioni

Credits

2

Period

Semester 2

Academic staff

Giuditta Franco

Lessons timetable

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

Linear Algebra, Informational methods

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 topics among the following:
- strings for modelling DNA, RNA, and proteins
- graphs for modelling molecules
- graphs for modelling interactions between proteins (protein interaction networks)
- discrete functions (e.g. temporal series) for describing dynamics in biology
- human genomic variation, modelled with strings and with graphs
- haplotyping, modelled with strings and with matrices
- genome rearrangements, modelled with permutations and with strings
- DNA sequencing (layouts, graphs)
- physical map of DNA (multisets, 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)

Bibliography

Vai alla bibliografia
Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

lectures and exercise sessions

Learning assessment procedures

Written exam, possibly followed by an oral exam: students who reach a grade of over 25 in the written exam have to take an additional oral exam, for the others the oral is optional. The written exam consists of practical applications (exercises) as well as theoretical questions (problems studied, mathematical properties, which models exist etc.). In the oral exam, the student will explain in detail their solutions in the written exam, and show to what extent they have mastered the topics.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

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