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
C
CHIM/03
6
C
CHIM/06
6
A
FIS/01
6
E
-
6
A
INF/01
12
A
INF/01

2° Year  activated in the A.Y. 2011/2012

ModulesCreditsTAFSSD
12
B
INF/01
12
B
INF/01
12
C
BIO/10
12
B
ING-INF/05
6
C
BIO/18
6
A
MAT/06

3° Year  activated in the A.Y. 2012/2013

ModulesCreditsTAFSSD
Due insegnamenti a scelta
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
ING-INF/05
6
B
INF/01
Un insegnamento a scelta
6
C
BIO/04
6
C
BIO/11
6
C
FIS/07
6
C
MAT/05
6
C
BIO/09
6
C
ING-IND/25
6
C
BIO/19
6
C
BIO/10
6
C
BIO/11
6
C
MAT/08
6
C
CHIM/02
12
C
BIO/10 ,BIO/11
12
B
INF/01
12
D
-
Altre attivita' formative
3
F
-
Prova finale
3
E
-
activated in the A.Y. 2011/2012
ModulesCreditsTAFSSD
12
B
INF/01
12
B
INF/01
12
C
BIO/10
12
B
ING-INF/05
6
C
BIO/18
6
A
MAT/06

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  (2012/2013)

Teaching code

4S01908

Teacher

Giuditta Franco

Coordinator

Giuditta Franco

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

I semestre dal Oct 1, 2012 al Jan 31, 2013.

Seminars 0

Learning outcomes

The course is designed to introduce several methodologies to model phenomena occurring in nature, by means of discrete mathematical tools and computational systems. The goal is to develop the ability of the student to master different approaches of discrete biological modeling, by means of the presentation of the state of the art and of the most recent problematics. Basic theoretical concepts (of mathematics, computer science, biology) are recalled, to better understand both traditional mathematical models and computational models of cellular and molecular processes, proposed along with a few case studies.

Program

Part I (traditional mathematical models)
Introduction to different classes of models, namely to discrete models
Discrete mathematics fundamentals - induction and recurrence
Fibonacci numbers and golden section in nature
Growth dynamics of microorganisms and of bacterial cultures
Malthusian biological population growth (extended) models
Iterative biological models, recurrence equations solving criteria
Logistic map: stability analysis, periodic orbits, and chaotic behaviour
Lotka-Volterra prey-predator model
Cobweb model of supply/demand interaction
An example of probabilistic model: gambler's ruin

Part II (non-conventional bioinformatics models)
Formal languages and biological grammars
Computational models of bio-molecular processes
Computational complexity of bio-algorithms and NP-completeness
Informational structure of DNA molecule, operations, experimental techniques
Amplification processes for string recombination and concatenation
DNA algorithms solving SAT
Self-assembly biomolecular processes
Discrete models of metabolism
Biological networks
Algorithmic procedures based on bacterial growth, and related experimental techniques

Reference texts
Author Title Publishing house Year ISBN Notes
Garey, M. R. and Johnson, D. S. Computers intractability: a guide to the theory of NP-completeness Freeman 1979 0-7167-1045-5
Gheorghe Paun, Grzegorz Rozenberg, Arto Salomaa DNA computing: new computing paradigms (Edizione 3) Springer 2013
David G. Luenberger Introduction to Dynamic Systems - Theory, Models, and Applications  
V. K. Balakrishnan Introductory Discrete Mathematics  

Examination Methods

Oral exam, or a couple of written midterm exams

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

Teaching materials e documents