#
Computational Biology Major

Master 1 courses

# 1st Semester

## Shared courses

**Organisation: **TD (18h)

**Coordinator : ** Julien Lefèvre

**Evaluation:** projects and oral presentation

The goal of this course is to introduce students to multidisciplinary research topics through seminars and visits to research laboratories and private sector companies.

The teaching unit consists of a presentation of the main professions involved in biological modelling. It will be carried out in two ways. First of all, some seminars will be offered with speakers from outside Aix-Marseille University in the academic and industrial field. Secondly, students will benefit from an immersion in Centuri laboratories where they will discover multidisciplinary research topics. To conclude this unit, students will be asked to present a specific problem related to data processing and modelling. The scientific aspect will also have to be integrated into a reflection on the underlying professional issues, whether in the academic or private sector.

**Organisation: **Lectures (6h), TD (6h), TP (6h)

**Lecturers****: ** Brigitte Mossé, Elisabeth Remy

**Evaluation:** projects and final written exam

This UE presents the finite Boolean dynamical systems, that are mathematical tools more and more used in the field of the modelling of biological regulatory networks.

Prerequisites in logics, set theory, graph theory and Boolean finite dynamical systems are provided, as well as definitions of the different possible associated dynamics, the corresponding regulatory graphs and logical formula. The rôle of feedback circuits in the dynamics is specially emphasised.

Applications of these tools for the modelling of biological networks are presented, mainly in context of diseases. The practical lessons (TP) are done using GINsim, a free software dedicated to the logical modelling of regulatory networks.

**Organisation: **Lectures (18h)

**Coordinators:** Laurence Röder, Françoise Muscatelli,

**Lecturers:** Laurence Röder, Françoise Muscatelli, Michael Kopp

**Evaluation:** continuous monitoring and final exam

This unit is the first part of the Fundamentals of biology course. In this first part, we will give a general presentation of the role of macromolecules in cell function, particularly in the regulation of gene expression, epigenetic inheritance and speciation as a source of biological diversity during evolution.

**Organisation: **Lectures (14h), TP (16h)

**Lecturers: ** Laurent Pézard, Michael Kopp, Andreas Zanzoni

**Evaluation:** projects and final written exam

Computational biology introduce the biological concepts needed to model complex systems, implement modelling approaches (differential, logical, stochastic or deterministic equations) to develop mathematical models of a biological system, analyze mathematical models and biological data to understand complex systems and assess the adequacy between a biological question.

The course is divided in 3 sections:

- Computational neuroscience: dynamic models of neuron function: dynamic behavioural simulation, biological aspects, computer complexity, analytical aspects
- Bioinformatics: alignment, molecular phylogeny, prediction and modelling of structural aspects of proteins, cis-regulation
- Evolution and population dynamics.

**Organisation: **Lectures (12h), TD (12h), TP (12h)

**Lecturer: ** Julien Lefèvre

**Evaluation:** continuous exams

This course recalls the general basics of programming, implemented here with Python.

It also provides the essential elements for a modern practice of programming in the Python language: use of an IDE, version control, use of existing modules, good practice for coding. Part of this course is also devoted to some classic algorithms for sorting and manipulating current data structures as well as algorithms dedicated to bioinformatics.

## Computational Biology courses

**Organisation: **Lectures (9h), TP (9h)

**Coordinator**: Laurence Röder,

**Lecturers: ** Laurence Röder, Benjamin Prud'homme, Estelle Duprez, Andrew Saurin, Charbel Souaid

**Evaluation: **continuous monitoring and final written exam

Genomics is an interdisciplinary field of modern biology that studies the genetic complement of an organism. With nearly 3,500 genomes now sequenced, the post-genomic era has greatly improved our knowledge of the origin of diversity and the evolution of human genomes, cell pathways, organism phenotypes and diseases. The course will be divided into four main themes intended to cover methods of genome analysis: Three-dimensional structureof eukaryotic genomes, Gene regulation and gene regulation network, Evolution of cis regulation, Deregulation of expression in human diseases.

**Organisation: **Lectures (6h), TD (6h), TP (6h)

**Lecturer:** Charlotte Perrin

**Evaluation:** continuous monitoring and final written exam

The purpose of this course is to introduce some of the simplest differential equations and systems of differential equations which underlie the main continuous models used in biology (dynamics of populations or cells, biochemical processes, epidemiology...). We will address both qualitative (equilibria and their stability, long-time behavior) and quantitative (positivity, parameter dependency) properties of the considered models. In parallel to this theoretical study, numerical simulations will be performed during the computer sessions. Practicals will consist in using Python specialised libraries as scipy.integrate in order to visualise trajectories and systems behaviours.

**Organisation****: **Lectures (6h), TD (6h), TP (6h)

**Lecturer: **Anna Frid

**Evaluation:** to be announced

This course will give a review of some basic notions of algebra and analysis. Illustrations in Python will be given. We will focus on the matrix tools necessary for regression:

Matrix calculation: matrix multiplication, matrix transposition ; Determinant calculation with Python, matrix inverse with Python, linear system with Python ; Calculation of eigenvalues/eigenvectors with Python; Norm and scalar product on R^d, transposition of a matrix; Practical courses in Python (with numpy, matplotlib.pylab).

**Organisation : **Lectures (6h), TD (6h), TP (6h)

**Lecturer:** Michael Kopp

**Evaluation:** to be announced

This course is a quick revision of the basics of probability and statistics. The concepts will be taught in relation to concrete biology examples (genome analysis, complex systems). The following concepts will be taught: Combinatorial analysis; Probabilities concepts; Discrete distributions (Bernouilli, binomial, geometric, hypergeometric, Poisson); Quick review of the basic continuous laws (normal and Student’s distribution); Estimation and sampling; Statistical hypothesis tests. Some biological examples of applications could concern the probability of patterns in genomic sequences, the detection of differentially expressed genes.

# 2nd Semester

## Shared courses

**Organisation: **Lectures (12h)

**Coordinators: ** Bianca Habermann, Alphée Michelot, Elisabeth Rémy

**Evaluation:** project and continuous monitoring

Scientific seminars constitute a good way to broaden your scientific horizon. In this regard, MSc students will frequently attend CENTURI seminars. At the end of the semester, students will be asked to write a summary of two seminars they have attended.

**Organisation: **Lectures (12h), TD (12h)

**Coordinator:** Laurence Röder

**Lecturers: ** François Muscatelli, Thomas Lecuit, Dominique Payet, Sandrine Roulland, Valery Matarazzo, Julie Koenig

**Evaluation:** continuous monitoring and final written exam

This course is a continuation of Fundamentals of biology 1. It will show how molecular mechanisms provide the information required for the development and function of tissues and organisms. Instruction is structured around four areas:

- Organismal development;
- Immune system;
- Nervous system
- Intergenerational transmission of traits

**Organisation: **Lectures (6h), TD (6h), TP (6h)

**Lecturer: ** Annie Broglio

**Evaluation:** projects and final written exam

This course aims at providing students with a practical approach of the analysis of biological data with R, based on the concepts acquired in the course “Probabilities and statistics for modelling 1”. The associated mathematical foundations will be developed in the course “Advanced statistics”. The following notions will be investigated:

- Sampling and estimation (moments, robust estimators, confidence intervals)
- Fitting
- Additional distributions
- Hypothesis testing (mean comparison, goodness of fit, …)

The course will be based on the analysis of biological datasets with the R programming language.

**Organisation: **Lectures (6h), TD (6h), TP (6h)

**Lecturer: **Jean-Marc Freyermuth

**Evaluation:** projects and final written exam

**Prerequisite for CMB-B**:

- [STAT1] Probabilities and statistics for modelling 1
- [STAT2] Statistics for biology

The R software will be used in the practicals.

This course will tackle advanced notions in statistics such as:

- Statistical inference (fundamental concepts, estimators, intervals and tests, quadratic error, bias and variance)confidence
- Likelihood (Fisher information, likelihood ratio test)
- Exponential family
- Convergence
- Multivariate Gaussian distributions

**Organisation: **Project

**Coordinators : **Florence Hubert, Laurence Röder

**Evaluation:** project

This course is an internship of 6 to 7 weeks in a public or private laboratory that takes place between mid-April and early June.

**Organisation: **Lectures (9), TD (9h)

**Lecturer: ** Victor Chepoy

**Evaluation:** to be announced

This introductory course focuses on graphs as mathematical objects and some of its uses to solve applications to biological networks. After intruducing different classes of graphs and their properties, the following points will be developped: Planar graphs, graphs on a surface, Euler characteristic; Interval graphs, perfect graphs.

## Computational Biology course

**Organisation: **Lectures (6h), TD (6h), TP (6h)

**Lecturer: **Michael Kopp

**Evaluation:** projects and final written exam

This course is addressed to students with a background in biology, and aims at enforcing the theoretical grounds in order to allow them to apprehend advanced statistics. The following concepts will be investigated:

- Numerical sequences and series (limits, convergence)
- Continuous laws of probabilities (normal, Student, chi2, Snedecor distribution).
- Introduction to the different notions of convergences in probabilities and Relationships between the different laws (convergence between laws).
- Law of large number and Central limit theorem

Practicals in Python will illustrate the different concepts.

**Organisation:** TD (10h), TP (20h)

**Lecturers: ** Denis Puthier

**Evaluation:** projects and final written exam

During this course you will learn more about concepts of probability and statistics applied to biological models (e.g. epigenomic data sets). These biological models will be picked amongst the models presented in other courses. Example of Statistical concepts: Fisher's exact test, hypergeometric distribution, multiple tests, unsupervised classification ; Example of Biological models: detection of differentially expressed genes with counting data, functional enrichment analysis, classification of expression profiles

**Organisation: **TD (15h), TP (15h)

**Coordinator: **Christophe Bordi

**Evaluation:** projects and final written exam

**Bioinformatic analysis**

Analysis of omics data constitutes a first approach towards omics data’s (e.g. DNA sequencing) high throughput analysis. This course will first focus on introducing bioinformatics’ methods and their goal (e.g. quality check, positioning reads on a genome, searching for ChIP-seq’s spikes, etc.). Different kinds of files needed to proceed to these analysis will be presented, and more particularly those used in epigenomic sequencing (e.g. ChIP-seq, RNA-seq, etc.).