Introducing the inter-disciplinary field of computational biology by giving students a wide culture on most of its aspects.

Syllabus

General observations

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.

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Introductory module (IM)

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.

  • Basics on the theory of evolution
  • Biology basics
  • Computer science basics
  • Statistics and logic (including introduction to the R language)
  • Basics of programming (python) and algorithms

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First module: Molecular sequences and structures (MSS)

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.

MSS1: Molecular evolution and sequences

  • Theory of evolution and population genetics
  • Sequence alignment, from pairwise to multiple, from genes to genomes
  • Molecular phylogeny

MSS2: Structures (DNA, RNA and proteins)

  • Introduction to biomolecular structures, determination and visualisation
  • Biomolecular structure mechanics, dynamics, prediction and design

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Second module: Evolutionary and Functional Genomics (EFG)

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.

EFG1: Genome structure

  • Introduction ("What's in a genome ?")
  • Gene prediction in prokaryotes and eukaryotes
  • Motifs in genomes
  • Repetitive DNA sequences detection

EFG2: Genome evolution and genome dynamics

  • Evolutionary modelling techniques
  • Analysis of genetic variations
  • Prediction and analyses of haplotypes
  • Molecular mechanisms of genome dynamics
  • Genome dynamics (rearrangement, genetic transfer, genome duplication)

EFG3: Function classification

  • Protein family identification and sequence based functional prediction
  • Function prediction and classification based not on sequence similarity but on physico-chemical properties, physical interactions etc
  • Annotation strategies

EFG4: Transcriptomics and proteomics

  • EFG4a: Transcriptomics and regulatory sequences
    • DNA array technologies, SAGE, EST
    • Analysis of DNA microarrays
    • CHIP/CHIP
  • EFG4b: Proteomics
    • Introduction to the various types of experimental technologies
    • Analysis, interpretation, profiling of collected data (peptide mass spectra, 2D gels)
    • Cell mapping and identification of proteins in complexes
    • Proteome annotation

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Third module: Biological Networks (BN)

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.

BN1: Introduction to networks

  • BN1a: Generic aspects of networks
    • Representation
    • Analysis
    • Evolution
    • Modelling social interactions (game theory and ecology/sociobiology)
  • BN1b: Protein interaction networks
    • Computational prediction of protein interactions
    • Network topology analysis
    • Evolution of protein interaction networks

BN2: Metabolic networks

  • Network representation, inference and analysis
  • Evolution of metabolic networks
  • Modelling of metabolic networks

BN3: Genetic networks

  • Genetic network representation, analysis and simulation
  • Genetic network inference
  • Evolution of genetic networks
  • Regulation of gene expression

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Fourth module: Systems biology (SB)

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.

SB1: Population biology, epidemiology and immune system

SB2: Cytoskeleton and cell morphogenesis, motion and chemotaxis modelling

SB3: Development and whole organism modelling

SB4: Evolutionary development

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Review weeks

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.

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Retreats

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.

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Workshops (W)

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.

W1: History of Computational Biology

W2: Evolution of the Cell

W3: Career Development

W4: Game Theory and Inclusive Fitness

W5: Systems Biology in Medicine

W6: Computational Biology Forum

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Methods

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.

  • Algorithms in computational biology
    • Design and analysis of algorithms
    • Algorithms on strings, trees and graphs
    • Introduction to constraint satisfaction problems
    • Discrete probability
  • Statistical data mining and machine learning
    • Descriptive statistics
    • Probability distributions
    • Hypothesis testing
    • Analysis of regression and variance
    • Bayesian decision theory
    • Supervised classification methods
    • Non-parametric methods
    • Multivariate descriptive analysis (PCA, FCA, etc.)
    • Unsupervised methods
  • Database management systems, knowledge systems and integration
    • Relational, object-oriented, and semi-structured data models and query languages
    • Database design using entity-relationship models
    • Data dependencies and object definition languages
    • Information integration using data warehouses, mediators and wrappers
    • Textual databases and text mining
    • Knowledge representation and integration, ontologies
    • Information retrieval techniques
  • Introduction to dynamic systems
    • Mathematical models of biological dynamic systems
    • Models for population dynamics, evolutionary biology and neural biology
    • Linear differential equations
    • Non-linear differential equations and maps
    • Stability, fixed points and bifurcation
    • Chaotic systems
    • Partial differential equations
    • Cellular automata

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Calendar

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)
10-12 26/11-14/12 MSS1        
13 17/12-21/12   IM R    
Vacation
14-16 7/1-25/1 MSS2 MSS      
17-18 28/1-8/2 EFG1       Algorithms (Combinatorial and Machine Learning)
19-20 11/2-22/2 EFG2       Algorithms and Statistical Data Mining
21 25/2-29/2 EFG3        
22-23 3/3-14/3 EFG4     W1 Database and knowledge systems and integration
24 17/3-21/3   EFG     Dynamic systems
Vacation
25-26 31/3-11/4 BN1       Dynamic systems
27-28 14/4-25/4 BN2      
29-30 28/4-9/5 BN3     W2  
31 12/5-16/05   BN     Dynamic systems
32 19/05-23/05 SB1        
33-34 26/05-6/6 SB3        
35 9/6-13/6 SB4        
36 16/6-20/6 SB2        
37 23/6-27/6       W3+W4  
38 30/6-4/7   SB   W5  
39 07/7-11/7       W6  
40 14/7-15/7     R  
Interviews
Project Definition

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