is English unless all the students are French-speakers.
1st SEMESTER (September to January)
2nd SEMESTER (February to July)
1st SEMESTER (September to January) courses description
* UE2c1, Integrative and spatial view of cellular machinery: from biology to modeling
Integrative approaches are key steps in the thorough exploitation of omics data and their translation into knowledge. In this module, students will have courses on the architecture and the machinery of the cell, and on the genome and epigenome organization. They will learn how to combine predictive and experimental approaches to decode the genomic information through the structural and functional annotation of genomes. The integration and the querying of heterogeneous data imply to perfectly know their origin in order to take into consideration their quality, relevance and confidence levels. The understanding of this approach is the basis of holistic analyses for systems biology. Students will see different methods to produce transcriptome, ORFeome, proteome and interactome resources and how to integrate them in modeling approaches to have new insights on cellular processes.
* UE2c2, Integrative modeling in Physiology
This course will give an integrated view of multi-organ physiological regulation, with particular focus on epithelial transport and on blood pressure regulation. We will present the multi-organ integrated classic model Guyton model of blood pressure regulation, and give a detailed description of the modeling of cardiac and respiratory physiology.
Using the Multi-Agent modelling technique, the Epitheliome project will be presented. As a practical complement to the course, the students will use a user-friendly ODE-solver to work through sample problems in multi-compartmental analysis and cardio-respiratory modeling.
Emergent properties of complex dynamic systems have captured the imagination of mathematicians, economists and biologists alike. Describing and predicting the behavior of such systems remains a formidable challenge, whether one is looking at a 5-day weather forecast, a stock market crash or the emergence of tumors in humans. And yet, in modern systems biology, we are not entirely unarmed facing the complexity and connectivity of biological systems. Systems biologists obtain data from global “omics” studies: genomics, transcriptomics, proteomics, metabolomics, and integrate them into predictive mathematical models. Such models allow us to analyze the regulatory networks, decision processes and all aspects of connectivity pertaining to living systems. Through them we get a closer glimpse at the emergent properties that govern life itself, its evolution, its diversity and its demise. In this course students will get hands-on experience in obtaining and analyzing high throughput proteomics data. They will learn how to integrate the data with other “omics” datasets (transcriptomics, metabolomics) and how to use them in designing constraint based models of the living cells. Finally, students will be trained in using the model predictions to intervene in concrete biological systems, in order to optimize the performance of most commonly used cell factories.
*UE4c1, Statistical analysis of biological sequences and expression data
The large amount of available biological sequences and gene expression data has changed the way biologists experiment to explore the genome. The analysis of such data requires knowledge in exploratory data analysis (visualization, statistic summary), in modeling dependence and in inferential statistics.
This course will present the necessary statistical knowledge to analyze both types of data. It is divided in two parts. The first part is dedicated to biological sequence analysis using Markov models. The second part presents the main methods in exploratory data analysis (unsupervised learning) and inferential statistics required for exploiting microarray. This last family of methods will also be of use for the analysis of biological networks.
* UE4c2, Statistical machine learning for inference of biological networks
The cellular response to input signals, involves many molecules that interact with each other. The development of high-throughput measurement techniques which allows to observe the status of cell’s components at given times and in given conditions has open the door to statistical exploitation of these experimental data to model and identify in quantifiable terms molecular interactions, e.g. biological networks. Machine Learning provides a theoretical and practical framework to build predictive models.
The course will provide elements of statistical learning required to infer networks from experimental data and background knowledge. First part of the course is devoted to link prediction by supervised classification and regression. Second part of the course concerns reverse-modeling of biological networks by estimation of dynamical graphical models and differential equations
- UE5, Formal languages for modeling & simulation in integrative Biology
* UE6c1, Formal verification of biological models
The course aims to provide students with an overview of how formal verification techniques, such as model-checking, can be applied to the analysis of biological systems. The behavior of biological systems is characterized by large networks of interactions, which are responsible for the functioning of the cell. From the modeling point of view two major approaches have been historically considered in the area of biological systems: the continuous-deterministic approach, whereby the dynamics of a systems is expressed in terms of sets of Differential Equations (DE), as opposed to the discrete-stochastic approach, where dynamics are expressed in terms of stochastic processes. In the course we will look at what the application of formal verification methods consists of in both frameworks. More specifically the course is divided in two parts:
1. Modeling and verification of Genetic Regulatory Networks (GRN)
DE modeling of GRN; relevance of parameters in DE modeling of GNR; Problem of analysis of DE-based models of GRN; Discretization of DE-models; Discrete models of GRN; Boolean-models and multi-valued models for qualitative analysis; Application of non-probabilistic model-checking to the analysis of GRN models; Introduction to Linear-time Temporal Logic and Computation Tree Logic model-checking; Compositional approaches to verification of discrete models of GRN.
2. Modeling and verification of Signaling Networks (SN):
Introduction to biochemical reactions and SN of reactions. Enzymatic reactions and phosphorylation reactions. Chemical Equations: basic formalism for reactions; Continuous-deterministic vs Discrete-stochastic modeling; Element of probability theory; introduction to Markov-chains models. SN in terms of Markov-chain models. Verification of stochastic models through probabilistic model checking. Probabilistic Computational Tree Logic and Continuous Stochastic Logic. Type of properties: probabilistic reachability, reward-based properties. Biological case studies: probabilistic verification of MAPK signaling cascade.
* UE6c2, Test applied to biological models
This module focuses on the problematic of testing biological sequences. This issue is tough to tackle mainly because the underlying systems are often designed based on state machines and the extracted sequences are applied in real cases with difficulty. The main problem is to match the observed parameters and the relationship with the specified states.
Methods coming from the telecommunication areas are known as “model-based testing”. These techniques, making part of the expertise of Telecom SudParis (TSP), face the problems of observability and controllability. Thus, to cope with these constraints, monitoring techniques (based on passive testing) are currently raised. They lay on a non-intrusive observation of the real system execution and the formal mapping with biological models.
Content: Courses such as lectures and labs are planned. Some labs are planned to TSP.
- Engineering and specification of communicating systems
- Active testing and test sequences generation
- Monitoring techniques of biologic sequences
- Algorithmic aspects of pattern matching in large data sets: the example of RNA secondary structure.
- A validation language of biologic objects: SBML and some testing frameworks.
The students could also have average 15 hours of personal works.
* UE7c1, Soft Matter for Biology and nanobiology
In the first part of the course we study biological problems with soft matter concepts. We will discuss the supramolecular organization of biological macromolecules, DNA and proteins, in relation with function. We will introduce non-viral gene therapy, we will speak about relation between the structure of the complex DNA-vecteur (cationic lipid, neutral polymer …) and the efficiency of transfection. The extracellular matrix (ECM) is a dense network of various molecules, mainly proteins, it isolates organs. The matrices are insoluble barriers that are normally impermeable to cell passage. During tumor dissemination, invasive cells must solubilise the ECM gel. This degradation involves different enzymes. Up to now the physical mechanism of enzymatic matrix degradation is little studied in relation to the role in cell invasion. We propose a scaling approach and reduced variables to account for the dependence on ECM and enzyme concentration of the degradation kinetics.
In the second part of the course we will speak about nanobiology and their biotechnology applications. The first experiment of the transport of one single strand DNA through a channel inserted in a lipid bilayer was performed in 1996. Macromolecules passing through the channel submitted to an electric field induce detectable blockades of ionic current. The sensitivity of this electrical detection has been used in many areas. Fundamental experiments have been performed to study the transport of peptides, proteins, RNA or DNA, their interactions with pores, to measure DNA-protein or protein-protein interactions. In addition, several applications are concerned by this technique, the ultra-fast sequencing of DNA and RNA, the development of biological sensors and the folding of recombinant proteins.
* UE7c2, Principles in Biomolecular Modeling
Definition and goals: Biomolecular modeling is an ensemble of computational techniques used to mimic the behavior of biomolecules in the cell in order to:
- Gain insights into the physical and chemical factors that determine their structures, dynamics and associations
- Make predictions about their structural, dynamic and energetic properties (that are not yet known experimentally)
In particular, the following subjects will be addressed:
- Computational protein design: sequence and structure-based protein descriptors, search algorithms, de novo design.
- Applications in systems and synthetic biology: modular design, promiscuity re-engineering, protein circuits.
Objective of this course: To provide the basic concepts and notions of biomolecular modeling methods in order to:
- Understand any studies using these approaches
- Be aware of their advantages and limitations
- Be able to implement and use molecular modeling in studies of biomolecules networks
- UE8, Synthetic Biology:
* UE8c1, Synthetic Biology for biosynthetic chemistry
From a chemist point of view, Synthetic Biology can be seen as an extension of Synthetic chemistry. We will describe in vivo synthesis of molecules of interest from a precursor, and illustrate the course with examples. We will focus on limiting steps, and will review the possible solutions.
Content of the course:
- Industrial Biotechnology: definitions, why we need it, success stories,
- Synthetic biology as part of industrial Biotechnology: biocatalysts, appropriate genetic constructions, illustration with one chosen biological species,
- Current limitations of industrial Biotechnology and possible solutions: biocatalysts and their limitations, search for new biocatalysts, current limitations of tools and biological systems, and possible solutions.
* UE8c3, A practical approach of biological devices
One of the goals of synthetic biology is to engineer biological devices. The setting-up of an in situ experiment requires a practical knowledge of DNA and host cells manipulation, modification and characterization techniques.
In this practical course, we will get used to these issues by constructing biological devices from spare parts available from the Biobricks registry (a repository of standardized biological parts including genes, promoters and regulatory parts). We will validate our constructions and test a few cases of logic gates in various host cells. The technical aspects as well as the design principles and the experimental results obtained at the end of this practical work will be discussed collectively in the classroom.
* UE8c4, Modeling and engineering of molecular interaction networks
This course is about metabolic engineering, it is composed of 10 hrs class and 8 hrs practice. The course covers various aspects of the field, including an overview of cellular metabolism, flux balance analysis, isotopomer modeling, and reaction network generation and design.
* UE1c1, English
* UE1c2, French class for foreigners (FLE, "Français Langues Etrangères")
This class is for students who are complete beginners or semi-beginners. Students will be able to start from no previous knowledge of the language or will have the opportunity to improve their communication skills. In this interactive class, students will use basic knowledge of French to communicate in different situations through role playing and informal presentations. The goal of this class is to enhance ability to speak French in real-life situations.