Objectifs :
Artificial Intelligence has proven remarkable success across scientific fields, and holds the potential to revolutionize the study of microbiomes. AI models excel at learning abstract concepts and non-linear patterns from complex, multidimensional datasets, offering innovative solutions for long-standing challenges in microbiome science.
The school aims to introduce students at the Master and PhD level to new applications of data-science, mathematical modelling, and AI in microbiome science. Participants will explore how novel statistical methods, large language models, autoencoders, and deep reinforcement learning methods are advancing our ability to understand, model, design and control microbiomes.
The program provides hands-on practical experience and ample opportunities for the scientific exchange between participants, including short pitches by students and participants.

Programme :
Wednesday October 1st
The first day will be focused on microbiome data analysis
9:00 – 9:30 - Welcome coffee: Launching the school and group announcements
Daniel Garza / Ariane Bize
9:30 – 11:00 - Bas Dutilh (theoretical lecture)
Lecture title: Mapping the Microverse and Modelling its Drivers
11:00 – 12:00 – Student pitches
12:00 – 13:30 - Lunch break
13:30 – 15:30 - Julian Tap (theoretical and practical lecture)
Lecture title: to be defined
15:30 – 16:00 - Coffee break
16:00 – 17:30 - Bas Dutilh (practical lecture)
19:00 – Group dinner

Thursday October 2nd
The second day will be focused on microbiome modelling

9:00 – 9:30 – Coffee break
9:30 – 11:00 - Jean-Loup Faulon (theoretical lecture)
Lecture title: Bacterial Reservoir Computing
11:00 – 12:00 – Haris Zafeiropoulos (theoretical and practical lecture)
Lecture title: to be defined
12:00 – 13:30 - Lunch break
13:30 – 15:30 - Djordje Bajić (theoretical and practical lecture)
Lecture title: Statistical mapping of Structure-Function Landscapes in Microbiomes
15:30 – 16:00 - Coffee break
16:00 – 17:30 - Jean-Loup Faulon (practical lecture)

Friday October 3rd
The third day will be focused on microbiome engineering

9:00 – 9:30 – Coffee break
9:30 – 11:00 - Lucas Böttcher (theoretical lecture)
Lecture title: Simulation and Control of High-Dimensional Dynamical Systems Using Artificial Neural Networks
11:00 – 12:00 – Daniel Garza (theoretical and practical lecture)
Lecture title: Microbiome Central Processing Units: a prototype for applying AI to control complex microbiomes.
12:00 – 13:30 - Lunch break
13:30 – 15:30 – Alex Fedorec (theoretical and practical lecture)
Lecture title: computational design of synthetic microbial communities
15:30 – 16:00 - Coffee break
16:00 – 17:30 - Lucas Böttcher (practical lecture)

Pré-requis :
None

Equipe pédagogique :
Bas Dutilh – Professor of viral ecology and omics of the Friedrich-Schilller Universitat, Jena, Germany (Viral Ecology and Omics) Julian Tap – Research scientist at Micalis, INRAE (Julien Tap) Jean-Loup Faulon – Director of research at Micalis, INRAE (Jean-Loup Faulon – Retro Synthetic Biology Team) Haris Zafeiropoulos – Postdoctoral reasercher at KU Leuven, Belgium (Home | Haris Zafeiropoulos) Djordje Bajić – Assistant professor at TU Delft, Netherlands (Djordje Bajić Group) Lucas Böttcher – Assistant professor of computational science Frankfurt School of Finance and Management, Germany (lucas-boettcher.info) Daniel Garza – Research scientist at PROSE, INRAE (Daniel Garza) Alex Fedorec – Group leader at the University College, UK (Alexander Fedorec Profile | University College London)

Méthode pédagogique :
Scientist lectures, participant presentations, practical courses

Compétences acquises à l'issue de la formation :
Students will have access to new methods and tools to study microbiomes. Focusing on three axis: data analysis, modelling, and engineering, students will learn in theory and in practice how to deploy cutting edge statistical tools and artificial intelligence models to make sense of complex microbiome datasets.

La formation participe à l'objectif suivant :conforter la culture scientifique des doctorants dans leur champ disciplinaire ou en interdisciplinaire