The syllabus for the first year of courses will include, throughout the year, formal classes provided mostly by foreign instructors, seminars/workshops by invited speakers and by the students themselves, practical classes, and finally short and long projects the students will then have to present to their peers and instructors. Projects will be done in small groups, mainly in the afternoons, some of which will thus be reserved all year long for such purpose. In general, formal courses will take place in the mornings while afternoons will be dedicated to seminars, projects and study, individual or together. Care will be taken to form groups balanced in terms of the students' educational background. This is to encourage students to help one another. In general, a spirit of cooperation is strongly encouraged among the students. Students are expected some time around the end of the first year of courses to choose the research topic they will want to pursue in the following three years. The choice is made by the student with the approval of the director and scientific committee of the PhD Program. PhD supervisors may be chosen among the committee members or instructors of the courses but this is not required. Students have to write a formal research project that he/she then presents during the second retreat period to the Program committee, his/her fellow students, some of the instructors and external guests.
The objective of this module is to bring all students to approximately the same level in the basics of biology, computer science and mathematics (mainly statistics). Mornings are dedicated to formal courses, while afternoons are reserved for practical classes.
This module covers the most basic and essential biological and methodological aspects needed to master the main issues in computational biology. The student is expected at the end of it to have acquired a good working understanding of the most widely used methods in any initial bioinformatic analysis, as well as a good knowledge of the main elementary biological objects that are manipulated. He/she should then be able on his/her own to acquire the further knowledge and technical skills necessary to later on elaborate new methods and/or bioinformatic analysis procedures and protocols.
This module explores the heart of evolutionary and functional issues as may be studied and inferred from biological sequences, and then from DNA microarrays and protein 2D gels. A full week will be set aside around the middle of the module for introducing the basics of database management and knowledge systems and of data integration as these are essential for performing functional analyses.
This module covers the essential elements of biological networks. It starts by introducing generic aspects of networks. Protein interaction networks are then chosen as a first example because of their simplicity as compared to other types of biological networks. The two main ones, metabolic and genetic, are then presented before discussing the important topic of integrating the various types of networks into a single one. This is a relatively unexplored area of study and serves to introduce the spirit of the next module.
This module aims at giving the students a solid background as well as pointers to areas of research in both biology and computational biology that are currently in full expansion. The topics covered in this module are much broader than in the previous ones, and some possibly also more speculative. Although formal courses are provided for most of the topics, some are introduced via seminars, workshops, projects and more informal discussions with instructors.
The objective of the review weeks is to enable students to revise what they have learned in a module. During this week, the students have to make presentations summarizing the material they have gone through during classes. These are followed by discussions among students and with local tutors. For each module, a group of students is chosen for note-taking during the classes. This week is also used for cleaning up the notes with the whole group. At the end of the first year of the PhD Program, all notes are assembled in the form of a booklet to serve for future years. If possible, they will be made publicly available on the web.
Retreats are meant to last one week and to be intensive periods of report making, discussion and constructive self-criticism by the students together with some of the members of the scientific committee. External participants may be invited, including among the instructors of the course. If possible, the retreats take place in an isolated setting. During the second retreat, students are expected to present their research plan for the three years after they finish the course part of the Program.
The objective of the workshops is to provide a more informal introduction to more advanced or general topics than those covered in the courses. Each should last one or two days except for the last one ("Open topics in systems biology" which will last two weeks). Workshops will be complemented by seminars held at regular intervals and presented by invited speakers or the students themselves.
The topics below detail the methodological points that are covered during the course. The distribution of the topics over the duration of the course year appears indicated in the preliminary calendar (yellow cells) below. For practical reasons (essentially, to facilitate inviting foreign instructors), classes are grouped into one or two afternoons per week.
Non white cells (light blue for regular courses, darker blue for review weeks, purple for retreat periods and grey for methodological courses) in a given column indicate the activities that take place for a given week or set of consecutive weeks. Some weeks have two activities happening at the same time (possibly in different days). This is mainly the case of all the methodological courses that in general happen in parallel with other more biologically-oriented or bioinformatic courses.
|Week number||Dates for 2007-2008||Course||Review||Retreat||Workshop||Methods|
|1-6||24/9-2/11||IM||Python and R|
|7-9||5/11-23/11||Algorithms (Combinatorial and Machine Learning)|
|17-18||28/1-8/2||EFG1||Algorithms (Combinatorial and Machine Learning)|
|19-20||11/2-22/2||EFG2||Algorithms and Statistical Data Mining|
|22-23||3/3-14/3||EFG4||W1||Database and knowledge systems and integration|