Studying at the University of Verona
Academic calendar
Il calendario accademico riporta le scadenze, gli adempimenti e i periodi rilevanti per la componente studentesca, personale docente e personale dell'Università. Sono inoltre indicate le festività e le chiusure ufficiali dell'Ateneo.
L’anno accademico inizia il 1° ottobre e termina il 30 settembre dell'anno successivo.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates. For further information, please get in touch with Operational unit: Science and Engineering Teaching and Student Services Unit
| Period | From | To |
|---|---|---|
| I sem. | Oct 2, 2017 | Jan 31, 2018 |
| II sem. | Mar 1, 2018 | Jun 15, 2018 |
| Session | From | To |
|---|---|---|
| Sessione invernale d'esami | Feb 1, 2018 | Feb 28, 2018 |
| Sessione estiva d'esame | Jun 18, 2018 | Jul 31, 2018 |
| Sessione autunnale d'esame | Sep 3, 2018 | Sep 28, 2018 |
| Session | From | To |
|---|---|---|
| Sessione Estiva Lauree Magistrali | Jul 19, 2018 | Jul 19, 2018 |
| Sessione Autunnale Lauree Magistrali | Oct 18, 2018 | Oct 18, 2018 |
| Sessione Invernale Lauree Magistrali | Mar 21, 2019 | Mar 21, 2019 |
| Period | From | To |
|---|---|---|
| Christmas break | Dec 22, 2017 | Jan 7, 2018 |
| Easter break | Mar 30, 2018 | Apr 3, 2018 |
| Patron Saint Day | May 21, 2018 | May 21, 2018 |
| Vacanze estive | Aug 6, 2018 | Aug 19, 2018 |
Exam calendar
The exam roll calls are centrally administered by the operational unit
Science and Engineering Teaching and Student Services Unit
Exam Session Calendar and Roll call enrolment sistema ESSE3
.If you forget your password to the online services, please contact the technical office in your Faculty.
Per dubbi o domande Read the answers to the more serious and frequent questions - F.A.Q. Examination enrolment
Academic staff
Bloisi Domenico Daniele
domenico.bloisi@univr.it
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 enrolment year.
| Teachings | Credits | TAF | SSD |
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| Teachings | Credits | TAF | SSD |
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1° Anno
| Teachings | Credits | TAF | SSD |
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2° Anno
| Teachings | Credits | TAF | SSD |
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| Teachings | Credits | TAF | SSD |
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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.
Natural Computing (2017/2018)
Teaching code
4S004557
Academic staff
Coordinatore
Credits
6
Also offered in courses
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Language of instruction
English
Period
I sem. dal Oct 2, 2017 al Jan 31, 2018.
Learning outcomes
The course is designed to first recall basic concepts of traditional computational models, such as formal languages and automata, and then present several models of bio-inspired computing, including bio-molecular algorithms. Main models of natural computing are presented, in terms of computational processes observed in and inspired by nature.
Some basic notions of discrete mathematics (sets, multisets, sequences, trees, graphs, induction, grammars and finite automata), of calculus, linear algebra and probability, are assumed, to explain a few computational methods both to elaborate genomic information and to investigate metabolic networks.
The course aims at developing the ability of the student both to master notions of discrete structures and dynamics, and to deepen his/her notion of Turing computation, in order to extend it to informational processes involving either natural or bio-inspired algorithms. Student's knowledge of all the topics explained in class will be tested at the exam, along with his/her learning and understanding skills.
Program
Introduction to natural computing, biological algorithms, and life algorithmic strategies.
Basic notions of discrete mathematics and of formal language theory (Chomsky's hierarchy, automata, and computability).
Elements of information theory (information sources, codes, entropy, and entropy divergences, typical sequences, first and second Shannon's theory).
Methods to extract and analyze genomic dictionaries.
Genomic profiles and distributions of recurrent motifs.
Software IGtools to analyze and visualize genomic data.
Computational models of bio-molecular processes, such as DNA self-assembly and membrane computing.
DNA computing and bio-complexity of bio-algorithms.
DNA algorithms to solve NP-complete problems.
MP grammars, networks, and metabolic dynamics.
Bibliografia
| Author | Title | Publishing house | Year | ISBN | Notes |
|---|---|---|---|---|---|
| Gheorghe Paun, Grzegorz Rozenberg, Arto Salomaa | DNA computing: new computing paradigms (Edizione 3) | Springer | 2013 | Previous editions: 1998, 2006. | |
| Martin A. Nowak | Evolutionary Dynamics (Edizione 1) | Harvard University Press | 2006 | 0-674-02338-3 | |
| Vincenzo Manca | Infobiotics | Springer | 2013 |
Examination Methods
-- One (only) written exam is proposed in the last class of the course, along with about ten questions which cover the whole program. The exam is passed if a majority of questions is answered correctly, by achieving a final evaluation greater or equal to 18/30.
-- Oral exam, covering the whole program, is offered in each exam session. It is passed with an evaluation greater or equal to 18/30.
Written and oral questions are aimed at verifying the knowledge of theorems and proofs, algorithms and data analysis methods (explained in class), as well as verifying the problem/exercise solving ability of the student, developed in the course. In this context, student's skills of learning, understanding, and communication are tested contextually to his/her knowledge of concepts explained in the course.
Optional homework assignment (project), may be agreed with students who have already passed the (either written or oral) exam, in order to increase the final vote with additional marks. It is meant either to deepen some argument of the course, of interest for the student, or to develop software to apply some knowledge assimilated in the course to specific biological systems. This is an opportunity for the student to apply his/her knowledge learnt in the course, by expressing autonomous initiatives.
Teaching materials
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AlgoritmiBiotecnologici
(pdf, en, 1504 KB, 26/10/17)
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AlgoritmiDNA
(pdf, en, 9630 KB, 24/10/17)
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CalcoliMembrana
(pdf, en, 4580 KB, 15/11/17)
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CalcoliMetabolici
(pdf, en, 6521 KB, 22/11/17)
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ChomskyGrammars
(pdf, en, 7912 KB, 14/12/17)
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Follow-up
(pdf, en, 15635 KB, 08/11/17)
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GenomicaInformazionale
(pdf, en, 13545 KB, 04/11/17)
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IGtools
(zip, en, 6431 KB, 08/11/17)
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Informazione-Vita
(pdf, en, 1974 KB, 18/10/17)
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LezioniIntroduttive
(pdf, en, 10895 KB, 10/10/17)
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LezioniProfManca
(pdf, en, 9932 KB, 14/12/17)
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SolutionTest19Dec
(pdf, en, 237 KB, 19/12/17)
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Test Grades
(pdf, en, 53 KB, 08/01/18)
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TuringMachines
(pdf, en, 807 KB, 14/12/17)
Tipologia di Attività formativa D e F
Course not yet included
Career prospects
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List of theses and work experience proposals
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