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
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 2025/2026The 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.
The Study plan 2008/2009 will be available by May 2nd. While waiting for it to be published, consult the Study plan for the current academic year at the following link.
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.
Discrete Biological Models (2010/2011)
Teaching code
4S01908
Teacher
Coordinator
Credits
6
Also offered in courses:
- Discrete Biological Models of the course Bachelor's degree in Bioinformatics (until 2008-2009)
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
II semestre dal Mar 1, 2011 al Jun 15, 2011.
Learning outcomes
The course exhibits 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, along the presentation of the state of the art and of the most recent problematics. Basic concepts (of mathematics, computer science, biology) are recalled, to better understand some traditional mathematical models, algorithms of natural computing, and biological networks, proposed along with a few case studies.
Program
Introduction to different classes of models, and to discrete models
Fibonacci numbers and golden section in nature
Differential and iterative biological models
Criteria to solve polynomials and linear recurrence equations
Malthusian biological population growth models
Dynamical systems, and logistic map
Discrete SIR model of epidemics
Formal languages and biological grammars
Algorithmic models of bio-molecular processes
DNA computing
XPCR for string recombination and concatenation
Membrane models and minimal cell
Discrete models of metabolism
Models of population evolution
Operations on micro-organisms and experimental techniques
Examination Methods
Oral exam
