|Tuesday||8:30 AM - 11:30 AM||lesson||Lecture Hall A|
|Wednesday||8:30 AM - 10:30 AM||lesson||Lecture Hall C|
Main models of natural computing will be presented, in terms of computational processes observed in and inspired by nature. 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, also including bio-molecular algorithms. A few computational methods both to elaborate genomic information and to investigate biological networks will be as well explained.
By this course the student is expected to deepen his/her notion of Turing computation and extend it to informational processes involving either natural or bio-inspired algorithms. However, no previous biological or computational specific knowledge is assumed from the student.
Introduction to natural computing, biological algorithms, and life algorithmic strategies
Basic notions and data structures: multisets, sequences, strings, trees, graphs
Formal languages and grammars, Chomsky hierarchy
Specific characterization of REG, REC, CF classes
Finite state automata, Turing machines, computational universality and complexity
A nutshell of information 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 a couple of NP-complete problems
Metabolic grammars, networks, and computational dynamics
One midterm written exam (which applies for the first part of the course program) plus one work assignment (for the second). In alternative, a final oral exam may be chosen for the whole program.
Written test will focus on exercises and general questions on traditional computational models, while work assignments will focus on topics of computational genomics (typically dictionary based genomic sequence analysis).
Midterm written test applies only for the exam session in February. In next sessions, only oral exams will be allowed.
|Outcomes Exams||Outcomes Percentages||Average||Standard Deviation|
|18||19||20||21||22||23||24||25||26||27||28||29||30||30 e Lode|
Data from AA 2015/2016 based on 41 students. I valori in percentuale sono arrotondati al numero intero più vicino.