5:00 - 8:00 pm
- Cesar de la Fuente, University of Pennsylvania, Philadelphie, États-Unis
Accelerating antibiotic discovery with AI
Abstract
- Sylvie Rebuffat, Muséum national d'Histoire naturelle, Paris, France
The continuous story of bacteriocins until emerging origins and methods for discovery
Abstract
8:30 am - 5:00 pm
- Laurent Bazinet, Université Laval, Quebec, Canada
Combining statistical, machine learning and experimental approaches for screening of novel antimicrobial peptides from complex hydrolysates
Abstract
- Éric Biron, Université Laval, Quebec, Canada
The lipopeptide brevibacillin: A promising scaffold for the development of antimicrobials with tunable pharmacological properties and spectra of activity
Abstract
- Frédéric Borges, Université de Lorraine, Nancy, France
Top-down strategies for engineering microbial communities with antimicrobial properties
Abstract
- Françoise Coucheney, Université de Lille, Villeneuve d'Ascq, France
Bacteriocins from Lacticaseibacillus paracasei CNCM I-5369: anti-Escherichia coli activity, original export system, potential medical application
Abstract
- Véronique Delcenserie, Université de Liège, Liège, Belgique
Dynamic gastrointestinal models as engineering tools to decipher food-microbiome-probiotic interactions
Abstract
- Séverine Zirah,Muséum national d'Histoire naturelle, Paris, France
Microcin diversity and role in competitive interactions in poultry microbiota
Abstract
8:30 am - 10:00 pm
Ismail Fliss, Université Laval, Quebec, Canada
Abstract
Simon Heilbronner, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Allemagne
Feast or Famine: Nutrient Sharing Affects S. aureus Growth in the Nasal Ecosystem
Abstract
Sunday Ochai, International Center for Antimicrobial Resistance solutions, Copenhague, Danemark
AMR in a Changing Environment: Bridging Evidence, Policy, and practice Gaps at the Climate-AMR Nexus in Low- and Middle-Income Countries
Abstract
8:30 am - 4:30 pm
Workshop: Integrative Approaches and Artificial Intelligence for Antimicrobial Discovery
Animator: Séverine Zirah, Muséum national d'Histoire naturelle, Paris, France
This workshop will offer an immersive experience designed to familiarise participants with the diversity of “omics” datasets and the principles of classification and machine learning applied to antimicrobial research.
Following an introduction to multi-omic methods and classification and machine learning methods, a hands-on computer workshop will utilise a dataset compiled from a collection of antibiotic-resistant Enterobacteriaceae strains [1-2]. The joint analysis of phenotypic data (bacteriocin antimicrobial activities) and genomic data will aim to generate tools for predicting strain susceptibility to bacteriocins, followed by an evaluation of their relevance.
References:
1. Telhig S, Pham NP, Ben Said L, Rebuffat S, Ouellette M, Zirah S, Fliss I. Exploring the genetic basis of natural resistance to microcins. Microb Genom. 2024,10:001156. doi: 10.1099/mgen.0.001156.
2. Telhig S, Ben Said L, Torres C, Rebuffat S, Zirah S, Fliss I. Evaluating the Potential and Synergetic Effects of Microcins against Multidrug-Resistant Enterobacteriaceae. Microbiol Spectr. 2022, 10:e0275221. doi: 10.1128/spectrum.02752-21.
