Natural Computing (2017/2018)

Course code
4S004557
Name of lecturers
Giuditta Franco, Vincenzo Manca
Coordinator
Giuditta Franco
Number of ECTS credits allocated
6
Other available courses
Academic sector
INF/01 - INFORMATICS
Language of instruction
English
Period
I sem. dal Oct 2, 2017 al Jan 31, 2018.

Lesson timetable

Go to lesson schedule

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.

Syllabus

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.

Reference books
Author Title Publisher Year ISBN Note
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

Assessment methods and criteria

-- 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 aids

Documents

Statistics about transparency requirements (Attuazione Art. 2 del D.M. 31/10/2007, n. 544)

Data from AA 2017/2018 are not available yet