Duration
30h Th, 30h Pr, 30h AUTR
Number of credits
| Master in biomedicine (120 ECTS) | 8 crédits |
Lecturer
Jean-Marie Jacquet
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
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. Various tools will also be presented such as BioCham, BioPepa and Prism.
Learning outcomes of the learning unit
Some of the major discoveries of the last 50 years can be summarized by 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.
Prerequisite knowledge and skills
Planned learning activities and teaching methods
Mode of delivery (face to face, distance learning, hybrid learning)
The course is conceived as a series of presentations that combine theory and practical applications.
Recommended or required readings
Ph. Compeau et P. Pevzner, Bioinformatics Algorithms : An Active Learning Approach, Active Learning Publishers, 2e édition, 2015
The student is assessed on two bases: practicals to be done during the semester and an exam. The latter includes both questions on all the material seen during the course as well as on the practicals.