MODELS FOR BIOLOGICAL SYSTEMS
- Overview
- Assessment methods
- Learning objectives
- Contents
- Bibliography
- Delivery method
- Teaching methods
- Contacts/Info
To attend the course of Models for Biological Systems the student is required to have familiarity with the basic notions in the realm of formal languages, algorithms and complexity. This requisite is surely respected by students with a 3-years degree in Mathematics or Computer Science
The scope of the exam is to certify that student have all knowledge and ability explained above, by evaluating the knowledge skill and, mainly, the ability to understand and evaluate the novelty in the area proposed in scientific papers.
The exam consists in an oral discussion in a classroom. This is, normally, a 30 minutes discussion requiring, normally, to exposing the content of a scientific paper. The evaluation focuses, in particular, on the ability to understand the results and the ability to understand how these results compare with respect the literature and can be applied in practice.
The knowledge of the technical terminology is implicitly checked, since the questions and specifications of problems use such a terminology.
The aim of the course is to allow students to know the basic relations between computer science and biology, making them able to understand and develop elementary modelling of biological systems and to employ formalisms and languages inspired by biology. After having attended the course, the student will have clear how computer science and biology are related disciplines.
At the end of the course, the student will be able to:
1. Understand and compare the main features of languages and formalisms proposed to model and analyse biological systems.
2. Understand and analyse pattern matching algorithms.
3. Understand the main features of formalisms inspired by biological systems, e.g. Lindenmayer systems and cellular automata.
4. Understand the basic idea of DNA Computing.
Further, the student will be able to tune pattern matching algorithms to new challenges coming from biology, to model simple biologic behaviours by using the proper formalism and to analyse the expressive power of languages inspired by biology.
The student will be also required to develop an autonomy of judgement in recognising the main challenges of the modelling of biological systems and employment of biology-inspired languages.
Finally, the student will get a proper knowledge of (possibly standard) technical terminology used in the context of modelling of biological systems and formalism inspired by biology.
The course is organised as follows:
DNA Computing (6h)
- Introduction (2 h)
- The Hamiltonian path problem (2 h)
- The SAT problem (2h)
Pattern matching algorithms (14 h)
- Algorithm naive (2 h)
- Algorithm by Boyer-Moore (4 h)
- Algorithm by Apostolico-Giancarlo (4 h)
- Algorithm by Knuth-Morris_Pratt (4 h)
Cellular Automata (8 h)
- Introduction (2 h)
- Game of life (2 h)
- Applications (4 h)
Lindenmayer Systems (4 h)
Modelling biological systems (16 h)
- Base challenges (4 h)
- Formal methods (4 h)
- Process algebras (8 h)
Due to variety of the arguments, there is no textbook covering all of them.
The slides, in pdf format, are available in the e-learning platform.
The course is organised in frontal lessons (48 h).
Frontal lessons are dedicated to illustrating the essence and principles of modelling of biological systems and formalisms inspired by biology mentioned in the “Contents” section. In detail, frontal lessons are dedicated to describing the typical challenges of modelling of biological systems, of pattern matching algorithm, with particular focus on biological applications, and of expressiveness of biological inspired formalisms and DNA computing.
The lecturer will meet students upon appointment, proviso a request via e-mail to name.surname@uninsubria.it. The lecturer will answer only to e-mail that are signed and sent from domain studenti.uninsubria.it.
Borrowed from
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