At the end of the course, the student will master the main computer concepts and methods underlying the modelling of biological systems.
Part of the course consists in a project done by groups of students, which allows students to acquire soft skills.
Some of the major discoveries of the last 50 years are certainly the fact that the genetic heritage of any organism is contained in its DNA, that the genes are reduced to a nucleotide sequence of the DNA and that their expression corresponds to the execution of a program. These observations have raised hopes for a better understanding of diseases and, as a result, the birth of predictive and personalized medicine.
Technological advances have reinforced these hopes. It is indeed less and less expensive and faster to sequence an entire genome. In addition, many databases are now available to biologists.
On the scientific level, it is worth observing that these discoveries and advances are in fact very familiar to the computer scientist. For instance, finding a gene in a DNA sequence is actually amounts to determining whether a sub-sequence of characters appears in another. More generally, it is becoming increasingly clear that modelling biological systems cannot be achieved without the help of computer concepts and methods.
In this context, the objective of the course is to study the major computer concepts and techniques underlying the modelling of biological systems.
The course shows how biological problems, such as the search for pattern and sequence similarity, genome alignment and understanding of genetic regulatory networks can be reduced to problems in computer science using, to name but a few examples, process algebras, ultra-metric trees, Eulerian and Hamiltonian paths, De Bruijn graphs, constraint programming, dynamic programming, Petri networks or automatic learning problems. Several tools such as BioCham, BioPepa and Prism will also be studied.
The student is assessed on two bases: work to be done during the quadrimester and an exam. The latter includes both questions on all the material seen during the course as well as on the practicals.
As the work to be done require a real involvement from the student, in application of article 32, paragraph 1 of the Regulations for Studies and Exams, only work of a sufficient quality (with a grade higher than 5/20) will be accepted for the second session.
Ph. Compeau et P. Pevzner, Bioinformatics Algorithms : An Active Learning Approach, Active Learning Publishers, 2e édition, 2015
| Training | Study programme | Block | Credits | Mandatory |
|---|---|---|---|---|
| Master 60 en sciences informatiques (horaire décalé) | Standard | 0 | 4 | |
| Master 60 en sciences informatiques (horaire décalé) | Standard | 1 | 4 |