M2 Genomics Informatics Mathematics and Artificial Intelligence for Health and Environment (GENIOMHE)

The prerequisites for the course are a solid background in molecular and cellular biology, and a confirmed grasp of computer programming. The students are also assumed to have basic knowledge of discrete mathematics.

1. Introduction to Systems Biology and Network Medicine

2. Foundations

   a. Topological analysis

      i. Graphs: definition and fundamental algorithms

      ii. Graph measures

     iii. Case studies

      iv. Random networks study

   b. Discrete dynamics

      i. Boolean networks

      ii. Petri nets

3. Applications to systems biology & tools

   a. Introduction

   b. Types of biological networks

     i. Types and functional roles

     ii. Databases

     iii. Types and data acquisition

   c. Integration of data into networks

     i. Network inference

     ii. Functional integration

   d. Modelling of network dynamics

     i. Modelling principles

     ii. Case studies and software tools

Understand the fundamental principles of network modelling and analysis applied to biological systems, in particular to molecular biology.

The objective of this course is to provide the fundamental background combining theoretical, computational and biological knowledge for this challenge. The course introduces the theoretical frameworks and the computational methods applied to Systems Biology, with a specific focus on networks. The content of the course is divided in three major parts:
The first part will be dedicated to the fundamental knowledge of networks, and in-depth topological analysis of complex networks. Different types of networks, case studies and applications in biology will be discussed.
In the second part, emphasis will be on dynamical networks and on how discrete based mathematical formalisms can be used to describe their dynamics and delineate their properties. Two modelling approaches will be presented, Boolean networks and Petri nets, with concrete examples in biology.
In the third part, the students will be introduced to the standards of network and model description commonly used in Systems Biology. Then, different types of intracellular networks will be analyzed in detail, along with a thorough presentation of available resources such as model repositories and databases. Bottom up and top down approaches for network construction and inference will be discussed and different data types will be presented. Lastly, the students will learn about functional data integration and limitations.

Compétence 1.4
Produire et valider des données scientifiques.

Compétence 2.1
Actualiser ses connaissances théoriques et technologiques par une veille dans son domaine en relation avec l’état de la recherche

Compétence 2.2
Synthétiser et structurer un état de l’art sur une problématique définie, en autonomie, dans un contexte lié à la recherche scientifique et/ou recherche & développement

Compétence 2.3
Se former à de nouveaux outils technologiques.

Compétence 3.1
Structurer et présenter des données et concepts scientifiques à des spécialistes ou à des néophytes.

Compétence 3.2
Argumenter les choix méthodologiques et les interprétations d’une étude.

Compétence 3.3
Extraire et questionner des informations pertinentes d’un document scientifique ou de conférences en anglais.

Compétence 4.1
Travailler en autonomie et collaborer avec les membres d’une équipe afin d’atteindre des objectifs communs.

1. Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature Reviews Genetics, 12(1), 56–68.

2. Jorge G.T. Zañudo, Steven N. Steinway, Réka Albert, Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer, Current Opinion in Systems Biology, Volume 9, 2018, Pages 1-10.

3. Monika Heiner, David R. Gilbert, Robin Donaldson: Petri Nets for Systems and Synthetic Biology. SFM 2008: 215-264

4. Célia Biane, Franck Delaplace: Causal Reasoning on Boolean Control Networks Based on Abduction: Theory and Application to Cancer Drug Discovery. IEEE ACM Trans. Comput. Biol. Bioinform. 16(5): 1574-1585 (2019)

Basics in molecular biology and in functional RNA
Basics in sequence algorithmic and machine learning.

The course will cover the different bioinformatics approaches existing for RNA prediction
- RNA secondary structure prediction
- RNA complexes structure prediction
- RNA 3D structure prediction

- RNA 3D segmentation

- ncRNA prediction and classification
- Prediction of small ncRNAs (microRNA, piRNA, …)
- Prediction of lncRNAs

The goal of the course is to present the bioinformatics methods for the prediction of non-coding RNAs (ncRNAs), their classification and the prediction of their structures (2D and 3D), and the challenges that remain, particularly for identification at large scale.

A project will be proposed and evaluated, as well as a study of scientific publications.

Internship of 5 to 6 months in an academic research laboratory or in an  entreprise.

Python

Programming

Deep learning course of M1 GENIOMHE-AI or equivalent

4. Programme/Plan/Contenus

- Introduction to deep learning for precision medicine: motivation, applications, and challenges

- Advanced recurrent architectures (Transformers) and large language models (LLMs)

- Advanced computer vision architectures (ViT)

- Generative adversarial networks (GAN)

- Overview of machine learning paradigms, including multi-task, self-supervised, federated, continual, and meta-learning in the context of precision medicine applications.

This course explores further the recent developments of AI and deep learning.

Students will also have the opportunity to explore advanced model architectures such as transformers and diffusion models, which constitute the foundation of many state-of-the-art systems, including modern conversational agents like ChatGPT and scientific breakthroughs such as AlphaFold. The course will also cover advanced learning paradigms, including multi-task, continual, and meta-learning, and show how these approaches can be applied to real problems in precision medicine.

Students will be introduced to advanced features of deep learning frameworks, such as PyTorch, through practical sessions that showcase precision medicine applications.

They will further take part in a cutting-edge research project proposed by researchers from the IBISC laboratory.

By following this course, students will be able to:

- understand the key mathematical foundations of deep learning models

- design, train, and evaluate advanced deep learning architectures for precision medicine applications (and beyond)

- identify appropriate advanced architectures for specific applications (image, time series, text, etc.)

- use advanced features of deep learning  popular frameworks like PyTorch (TensorFlow, and Keras) to build and train advanced deep learning architectures

- explain major learning paradigms—including multi-task learning, continual learning, and meta-learning—and apply them effectively in precision medicine contexts

- participate in a cutting-edge AI and precision medicine research project proposed by researchers from the IBISC laboratory

This course will be evaluated through a written theory exam and a project report.

Compétence 1.1
Formuler des problématiques et des hypothèses de travail permettant la conception et la réalisation d’un projet dans son domaine d’expertise et /ou à l’interface de plusieurs domaines en biologie.

Compétence 1.2
Concevoir une démarche expérimentale.

Compétence 1.3
Établir, réaliser et optimiser des protocoles expérimentaux.

Compétence 1.4
Produire et valider des données scientifiques.

Compétence 2.1
Actualiser ses connaissances théoriques et technologiques par une veille dans son domaine en relation avec l’état de la recherche

Compétence 2.2
Synthétiser et structurer un état de l’art sur une problématique définie, en autonomie, dans un contexte lié à la recherche scientifique et/ou recherche & développement

Compétence 2.3
Se former à de nouveaux outils technologiques.

Compétence 3.1
Structurer et présenter des données et concepts scientifiques à des spécialistes ou à des néophytes.

Compétence 3.2
Argumenter les choix méthodologiques et les interprétations d’une étude.

Compétence 3.3
Extraire et questionner des informations pertinentes d’un document scientifique ou de conférences en anglais.

Compétence 4.1
Travailler en autonomie et collaborer avec les membres d’une équipe afin d’atteindre des objectifs communs.

Compétence 4.3
Organiser et planifier son temps de travail de façon à respecter des deadlines.

Bengio, Y., Goodfellow, I., & Courville, A. (2017). Deep learning (Vol. 1). Cambridge, MA, USA: MIT Press.

Géron, A. (2025). Hands-On Machine Learning with Scikit-Learn and PyTorch. O’Reilly Media, Inc.

Basic Knowledge in Probability, Descriptive Statistics, Sampling and
Hypothesis Testing, R programming.

This UE is composed of three parts which revolve around the quantitative
analysis of gene expression : 1. A first part on the statistical methods
for gene expression data analysis 2. A second part on Bayesian networks
3. A third part on information-based methods for experimental design and
statistical learning.

Contenu :

- Microarray and RNA-seq technologies
- gene expression Data : large Data with high fluctuations
- statistical Methods for differential analysis of gene expression Data
- clustering and statistical Methods for coexpressed genes Identification
- statistical Methods for inference of gene regulatory network
- bayesian reasonning : Principles and difficulties
- definition and properties of The bayesian Networks
- exact and approximate inference
- designing and learning a network
/* JC */
- bayesian classification
- information measures, entropy, application to experimental design
- information-based tree learning algorithms

• Knowledge of Python
• Understanding of probability 
• Basic notions of deep learning

Session 1: Introduction to Reinforcement Learning

• What is RL? 
  • Differences between RL, supervised learning, and unsupervised learning 
  • Applications and use cases
• Basic terminology and notation
• Markov Decision Processes (MDPs)
  • Formal framework of RL 
  • States, actions, rewards, and transition dynamics 
  • Policy and value functions
• Dynamic programming concepts 
• Policy evaluation and improvement 
• Policy iteration 
• Value iteration

Session 2: Tabular Methods

• Monte Carlo methods 
• Monte Carlo prediction and control 
• Exploration and initial visit 
• Episodic tasks
  • Temporal difference (TD) learning 
  • SARSA and Q-learning
• On-policy vs. off-policy learning 
• Bootstrapping

Session 3: Function approximation

• Linear approximation 
• Gradient descent methods 
• Policy gradient methods 
  • REINFORCE algorithm 
  • Variance reduction techniques

Session 4: Deep Learning for the Value Function

• Q-Networks (DQN) and developments
• Use of replay buffers
• Double DQN

Session 5: Deep Learning for Policy

• Basic concepts of actor-critic algorithms
• Proximal Policy Optimization (PPO)

Session 6: Advanced Topics and Recent Trends

• Multi-agent RL 
• Exploration strategies
• MCTS usage

This course provides an in-depth introduction to reinforcement learning (RL), emphasizing fundamental algorithms and practical applications. Students will learn the principles of sequential decision-making, Markov decision processes, value functions, policy optimization, and some modern techniques in deep RL. Throughout the course, students will experiment the studied algorithms and approaches by implementing Python agents that learn to solve academic problems.

In summary, the learning objectives are as follows:

• Understand the fundamental concepts of reinforcement learning;
• Implement classic Monte Carlo and temporal-difference (TD) algorithms, extending to actor-critic algorithms;
• Explore function approximation and deep learning through RL.

The course is organized into six three-hour sessions. During each session, students will apply the concepts they have learned by programming learning agents.

This course will be evaluated through a written theory exam and a project report.

Students will demonstrate their theoretical and applied knowledge of modern RL methods. The applied aspects will allow students to demonstrate their ability to program agents in Python using the PyTorch and StableBaseline libraries.

"Reinforcement Learning: An Introduction" par Sutton & Barto

The course outline for the available data assigned:

- identify the type of data / consider biological questions / choice of the right software to answer biological questions and/or necessary developments (if development is required, criteria for validating the method (quality controls) and estimation of the time needed for development)

- Descriptive statistics of the available data

- Analysis strategies / description of tasks to be performed

- Validation criteria / results / giving biological meaning to the results obtained / discussion (limitations, additional analyses with planned protocols, etc.)

- Reporting (strategies, scripts, available/processed data, results obtained/quality of analysis and reflection)

An interdisciplinary project  which will allow the students to understand the place of digital technologies in the processing and interpretation of generated data to address biological problems.

Students will be evaluated through reports (Mini preview, logbook), ability to communicate/work together and interact with the tutor, and oral presentation

Compétence 1.1
Formuler des problématiques et des hypothèses de travail permettant la conception et la réalisation d’un projet dans son domaine d’expertise et /ou à l’interface de plusieurs domaines en biologie.

Compétence 1.2
Concevoir une démarche expérimentale.

Compétence 3.2
Argumenter les choix méthodologiques et les interprétations d’une étude.

Compétence 3.3
Extraire et questionner des informations pertinentes d’un document scientifique ou de conférences en anglais.

Compétence 4.1
Travailler en autonomie et collaborer avec les membres d’une équipe afin d’atteindre des objectifs communs.

Compétence 4.2
Interagir avec des partenaires extérieurs à l’équipe, nationaux ou internationaux, scientifiques ou non scientifiques (organismes publiques, fournisseurs, commerciaux…).

his module aims to consolidate the professional project of our students, giving them a broad view  by offering conferences series, meetings and debates with professionals and researchers in the areas of bioinformatics and biotechnology.

Programming langage
Algorithmic 
Databases

Tutorial course 1 : Introduction to Big Data and Cloud Computing

Tutorial course 2: Introduction to NoSQL databases and data integration

Tutorial course 3: Introduction to Map Reduce and Spark

Directed work course 1: Docker containerization and MangoDB NoQL Database

Directed work course 2: Hadoop Map Reduce 

Directed work course 3: Spark

Understanding biological processes requires storing, accessing and processing many complex and more and more large scale datasets. These data are usually heterogeneous generated by humans, machines (imaging systems, sequencing systems, etc.) or by programs (simulations tools). In parallel, computer science has made significant progress in developing new methods, models and technologies to storing, integrating and processing big heterogeneous data in the Cloud Computing. It is of the benefit of modern biology to be able to use these concepts and tools to achieve its need in term of integration and processing of large amounts of heterogeneous data, with different formats and semantics, and manipulate them through complex workflows.

The objective of this course is to introduce the main concepts of Big Data, Cloud Computing, Containerization as well as Big Data tools such as NoSQL, Hadoop and Spark. Students will learn how these technologies can help engineers and researchers in bioinformatics to use these concepts and tools to conduct their data analytics in data centers and in the cloud computing.

The UE is organized in a set of tutorial sessions and directed work sessions addressing practical examples of concepts introduced in the tutorials sessions.

The used modalities are In-Person (Face-to-Face) with direct interactions. Online Synchronous lectures may be delivered depending on the situation. In this case, instructions are given online in real-time.

Compétence 1.1
Formuler des problématiques et des hypothèses de travail permettant la conception et la réalisation d’un projet dans son domaine d’expertise et /ou à l’interface de plusieurs domaines en biologie.

Compétence 1.2
Concevoir une démarche expérimentale.

Compétence 1.3
Établir, réaliser et optimiser des protocoles expérimentaux.

Compétence 1.4
Produire et valider des données scientifiques.

Compétence 2.1
Actualiser ses connaissances théoriques et technologiques par une veille dans son domaine en relation avec l’état de la recherche

Compétence 2.2
Synthétiser et structurer un état de l’art sur une problématique définie, en autonomie, dans un contexte lié à la recherche scientifique et/ou recherche & développement

Compétence 2.3
Se former à de nouveaux outils technologiques.

1. Cloud Computing for Science and Engineering, Ian Foster and Dennis B. Gannon, The MIT Press, Cambridge, Massachusetts, London, England © 2017 Massachusetts Institute of Technology.
2. NoSQL Data Models: Trends and Challenge, Olivier Pivert John Wiley & Sons, August 2018 
3. Big Data and Hadoop, Mayank Bhusan, BPB Publications, June 2018..
4. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra (English Edition), Geraldine A. Van der Auwera, Brian D. O'Connor, O'Reilly Media,‎ April 2020

Basics in programming

The first part of the course will provide a solid foundation in fundamental C programming concepts, including data types, control structures, functions, and arrays. It progresses to more advanced topics such as pointers, dynamic memory management, structures, and linked lists, preparing students to design and implement efficient, low-level data structures.

The second part will focus how to design and build a scalable application to launch or use many external components at the same time to be efficient. This design must be flexible to accept, to grow up, with all new breakthrough in Health.
- We will see the Domain Driven Design and the software Hexagonal Architecture to design a scalable application. And all the automatization to build a robust and reactive application.
- Feedback and theory on the application of the agile framework for the construction, management and maintainability of a code developed in team and in the long term.

The third part will provide minimum theoretical foundations for developing algorithms in a context of massively distributed architectures. This will include the different types of parallel architectures (including GPU), the parallel programming models. Then will be shown aspects of concurrent programming and distributed programmation, and programmation with GPUs.
The theory is then put into practice by implementing different parallel and distributed algorithms in C / C ++ using common tools such as POSIX threads, OpenMP and MPI.

In this course we will explore the lifecycle (design/build/run) of a health application and constraints it has to overcome to be successful and to be accepted.

Basic knowledge in graphs, computational complexity and algorithmics.

Algorithmic techniques useful when dealing with NP-hard combinatorial optimization problems.

• approximation algorithms (vertex cover problem)
• integer programming
• randomized algorithms
• multicriteria optimization (Pareto front)
• metaheuristics (genetic algorithms, tabu search).

At the end of this course, the students cill be able to 

-Enumerate and differentiate large-scale sequencing technology driven-approaches, i.e. RNA-seq, ChIP-seq, Cut&tag, Cut&Run, Methyl-seq, WGBS, ATAC-seq, Hi-C 

-Specify the distinct steps of the analysis workflows from raw data to pathway analysis (read mapping strategies, transcript assembly, data normalization, isoform-specific or differential gene expression analysis) for both quantitative and qualitative analysis of transcriptomic data

-Understand the limits of bulk RNAseq in regard to Single-cell approach

-Describe the principle of data production and analysis of ChIP-seq from peak calling to data visualization 

-Explain the methods of data exploration: common tools of genes &/or samples classification and functional annotation

-Determine which technology to use among a broad spectrum of functional genomics methods to address specific biological questions 

-Interpret results of the high-throughput data in a scientifically stringent manner

-Enumerate applications of these sequencing data in health and precision medicine

This course provides a thorough knowledge of functional genomic technologies to study distinct levels of gene regulation: transcriptomic, epigenomic & dynamic of chromatin structure.

It will train student to extract pertinent data from scientific articles, identify their limits and synthetize them in a journal club format.

The course consists in lectures, analysis of genomic studies based on several publications during tutorials and oral presentations of research work will be performed as seminars by the students. 

A particular focus on the use of these tools in the context of disease study and precision medicine (sample classification, diagnostic, prognostic tools and pathways analysis for target identification).

The students will be evaluated through oral presentation based on scientific publications and continuous assessment during the course.

C2.1 : Actualiser ses connaissances théoriques et technologiques par une veille dans son domaine en relation avec l’état de la recherche.

C3.2 : Structurer et présenter des données et concepts scientifiques à des spécialistes ou à des néophytes. 

C3.3 : Argumenter les choix méthodologiques et les interprétations d’une étude.

Microbiology, Molecular biology, genetics, Bioinformatics, Biostatistics.

Metagenomics, also known as ecogenomics, is founded on the genomic analysis of complex microbial communities directly extracted from diverse environments such as natural and polluted waters, marine ecosystems, and the digestive tract. This approach enables a comprehensive understanding of the composition and dynamics of microbial communities, as well as their interactions with each other and with their environments, without relying on traditional cultivation techniques.

Bioinformaticians will explore the various approaches used in metagenomic sequence analysis, including sequence alignment, assembly, taxonomic identification, phylogenetic reconstruction, and functional gene screening and annotation in environmental samples. By mining metagenomes for gene discovery, researchers can expand our understanding of microbial metabolism and diversity, identify novel enzymes, and investigate their evolutionary relationships. Finally, the industrial and biotechnological applications of metagenomics will also be discussed.

The objectives of this UE are to provide the students an insight of complex microbial communities in terms of phylogeny and metabolic diversity using bioinformatics and statistical analysis of high throughput sequencing of metagenomes.

Microbiology, metabolism, Molecular biology, Bioinformatics, Biostatistics

Metagenomics: Methods and Protocols — Wolfgang R. Streit (Ed.). Metagenomics: Methods and Protocols (2nd Ed.). Springer, New York, 2017. ISBN 978-1-4939-8274-5.

Deb, R., (ed.) (2020). Metagenomics: Techniques, Applications, Challenges and Opportunities. Singapore: Springer. ISBN-13: 978-981-15-6529-8.

Others : Massana R , et al., 2015 ; de Vargas C, et al., 2015 ; Logares R, et al., 2014 ; Iverson V et al., 2012 ; Luker S et al. 2010 ; Bertin P. N. et al,  2008.

The prerequisites are general concepts in structural genomics and evolution, and concepts covered in the first year of the Master's program (M1) regarding bioinformatics tools. Unix/Linux concepts and utility filters.

The course outline:

• Basic evolutionary principles
• Scales of comparison
• Comparison of genomic data and characteristics
• Parameters Ks and Ka

Two main themes will be addressed as examples:

• Evolution of duplicated genes
• Evolution of transposable elements

Directed work:

• Critical analysis of bioinformatics articles and protocols
• Implementation of a bioinformatics protocol for determining duplicated genes in a plant genome
• Examples of studies on transposable elements (distribution analysis, elements in the vicinity of genes...).

The course addresses the questions posed in comparative genomics and the different scales of study. The objectives are to determine which characteristics of the genomes are being compared and what data enables these comparisons. The course also aims to develop the ability to interpret the results.

1. This teaching unit is organized in the form of main courses (9h) and practical work (9h) in the computer room. Students will be evaluated through several small exams in class and a final exam.

Compétence 1.1
Formuler des problématiques et des hypothèses de travail permettant la conception et la réalisation d’un projet dans son domaine d’expertise et /ou à l’interface de plusieurs domaines en biologie.

Compétence 1.2
Concevoir une démarche expérimentale.

Compétence 1.3
Établir, réaliser et optimiser des protocoles expérimentaux.

Compétence 1.4
Produire et valider des données scientifiques.

Compétence 2.1
Actualiser ses connaissances théoriques et technologiques par une veille dans son domaine en relation avec l’état de la recherche

Compétence 2.2
Synthétiser et structurer un état de l’art sur une problématique définie, en autonomie, dans un contexte lié à la recherche scientifique et/ou recherche & développement.

Compétence 3.2
Argumenter les choix méthodologiques et les interprétations d’une étude.

Compétence 3.3
Extraire et questionner des informations pertinentes d’un document scientifique ou de conférences en anglais.

Une bonne conaissance de python et de l'ecosystem de calcul sicentifique en python est requise (numpy, scipy, scikit-learn). Une connaissance des base de l'apprentissage automatique et des algorithmes de classification est aussi necessaires.

Jour 1 : Préparation et manipulation des données

* Introduction du workflow (VSCode, Python, git, GitHub, tests, …)
* Cours avancé sur Pandas
* Exercices GitHub : NumPy et Pandas

Jour 2 : Pipelines de ML et évaluation de modèles

* Scikit-learn avancé : ColumnTransformer et pipelines
* Traitement parallèle avec joblib
* Généralisation et validation croisée
* Exercices sur scikit-learn
* Introduction à Codalab et présentation des challenges

Jour 3 : Métriques et gestion des données déséquilibrées

* Présentation des différentes métriques de ML
* Problèmes liés aux métriques avec des données déséquilibrées
* Approches d’apprentissage automatique pour traiter les jeux de données déséquilibrés
* Travail sur les challenges de données

Jour 4 : Données complexes

* Feature engineering et encodage avancé des variables catégorielles
* Travailler sur des séries temporelles et du signal
* Inspection des modèles : Partial Dependence Plots, Feature Importance
* Travailler sur un data challenge

Jour 5 : Méthodes d’ensemble et optimisation d’hyperparamètres

* Des arbres de décision au gradient boosting
* Profiling avec snakeviz
* Optimisation des hyperparamètres
* Travailler sur un data challenge

L'objectif de ce cours est d'apprendre la science des données par la pratique. Tous les aspects de la réalisation d'un pipeline de science des données seront abordés, de l'analyse exploratoire des données (EDA) à l'ingénierie des caractéristiques, en passant par l'optimisation des paramètres et les algorithmes d'apprentissage avancés. Un focus important sera mis sur l'evaluation de pipeline de datascience et la validation croisée, ainsi que les outlis pour la metter en place de manière valide.

L'UE se tient sur une semaine, avec le matin et les débuts d'après midi des cours magistraux sur les aspects pratiques de la datascience, et l'après midi des sessions pratique sur la programmation, l'utilisation de git, la participation a un challenge ou la constitution d'un challenge.

No prerequisites, but this course unit is designed for students potentially interested in entrepreneurship (without the prerequisite of having a business project) or who want to explore whether becoming an entrepreneur would be right for them.

This course will cover the key elements of a deeptech entrepreneurial project, detailing each of the essential building blocks for creating a business:

- Introduction: myth and reality of entrepreneurship, the entrepreneur profile, specific characteristics of deeptech entrepreneurship

Presentation of Genopole and the Shaker business creation support program

- Proof of concept (POC)

- Market research

- Business plan

- Sources of funding

- Legal and administrative aspects

- HR, partner search, management

- Intellectual property

- Moving from research to commercialization: the connections?

- Marketing/communication

- Business development, partnerships with large corporations

Provide students with a toolkit for biotech entrepreneurship:

• Give them clear, comprehensive, and practical information on the steps involved in creating a business.

• Encourage them to take the plunge, in the short or long term, and to demystify entrepreneurship.

• Provide them with the network necessary for an entrepreneurial project: ecosystems and professionals who can support them.

The course will be organized into 8 sessions:

- An interactive introductory session (3 hours) aimed at identifying the building blocks of entrepreneurial creation and co-creating the course program with the students.

- 6 sessions (3 x 3 hours + 3 x 2 hours) designed to provide foundational knowledge and illustrate the essential aspects of entrepreneurship through practical examples and testimonials.

- 1 coaching session (2 hours) to prepare a pitch for a biotech/deeptech startup project (group work)

1. basics of computer science (complexity, automata theory, langage theory)
2. basics of the biology of DNA

1. Presentation of DNA self-assembly, theory and experiments.
2. Biomedical applications of DNA self-assembly : reading and discussion.
3. Supervised project on thed design of a DNA origami.

1. Understand the basic principles of DNA self-assembly and DNA computing.
2. Understand basic applications of DNA nanotechnology in biology and medicine.
3. Learn how to design basic DNA nanostructures.

This class starts by a brief presentation of the computational aspects of DNA self-assembly and the experimental technique of DNA origami.  This presentation is followed by a reading session in which the students are encouraged to present their favorite aspects in detail to their peers.  The class ends with a practical assignment in the DNA origami design software https://www.ens-lyon.fr/ensnano/, in which the students are encouraged to design a shape having a high probability of folding in a test tube.

Compétence 1.1
Formuler des problématiques et des hypothèses de travail permettant la conception et la réalisation d’un projet dans son domaine d’expertise et /ou à l’interface de plusieurs domaines en biologie.

Compétence 1.2
Concevoir une démarche expérimentale.

Compétence 2.1
Actualiser ses connaissances théoriques et technologiques par une veille dans son domaine en relation avec l’état de la recherche

Compétence 2.2
Synthétiser et structurer un état de l’art sur une problématique définie, en autonomie, dans un contexte lié à la recherche scientifique et/ou recherche & développement

Compétence 2.3
Se former à de nouveaux outils technologiques.

Compétence 4.1
Travailler en autonomie et collaborer avec les membres d’une équipe afin d’atteindre des objectifs communs.

1. Fan Hong, Fei Zhang, Yan Liu, Hao Yan: DNA Origami: Scaffolds for Creating Higher Order Structures, Chem. Rev. 2017, 117, 20, 12584–12640

2. Adleman, L. M. (1994). Molecular computation of solutions to combinatorial problems. Science. 266 (5187): 1021–1024.

3. Diverse and robust molecular algorithms using reprogrammable DNA self-assembly Damien Woods, David Doty, Cameron Myhrvold, Joy Hui, Felix Zhou, Peng Yin, Erik Winfree Nature 567:366—372, 2019

4. Non-cooperatively assembling large structures Pierre-Étienne Meunier, Damien Regnault DNA25: The 25th International Conference on DNA Computing and Molecular Programming Springer LNCS 11648:120—139, 2019