FranceCountry of destination:
United States of America
Salmonella infection is the most common vector of collective food poisoning in the developed world. As such, deciphering the mechanisms of infection in humans and animals is a fundamental step towards the design of efficient epidemiological policies against Salmonella zoonoses. Recent studies have identified key interactions between Salmonella Typhimurium and its host during infection: the pathogenic virulence factors promote the inflammation of the host epithelium, modifying the gut nutritional environment and the ecological equilibrium of the commensal microbiota, creating new niches targeted by the pathogen. The resulting competitive advantage can be sufficient to promote Salmonella proliferation and transmission to another host. This research project mainly focuses on the mathematical modelling of S.Typhimurium infection. The host-pathogen interactions involved during the infection process are first modelled without the commensal microbiota: a dynamical system is defined to represent the inflammation dynamics and the resulting pathogen growth. In a second step, we supplement the infection model with the commensal bacteria, focusing on the competition against S.Typhimurium. A specific machine learning method is applied on metagenomic data to give a simplified view of the gut microbiota, through the definition of relevant bacterial metapopulations that compete for the inflammation-induced niches. Metabolic models of the metapopulations are defined to predict their growth in the gut environment and are coupled to the previous host-pathogen model. The resulting pathobiome model, including host-microbiota-pathogen interactions, is finally explored: different scenarii of invasion are studied and qualitatively compared to experiments.
After studying mathematics at the University of Bordeaux and working a few years as a primary school teacher, I went back to the University to complete a Master degree in applied mathematics from UPMC in 2010 and a PhD from the Bordeaux University in 2013. This doctoral research was conducted in the Carmen team (Inria, Bordeaux) and the Liryc Institute (Bordeaux) and focused on new mathematical models of the electrical activity of the heart dedicated to the numerical simulation of cardiac arrhythmias in the atria. These mathematical models were developed in close collaboration with the clinical team of the Liryc Institute. Since 2014, I have been working in the MaIAGE team (INRA, Jouy en Josas) as a researcher in applied mathematics. My main scientific interests are the mathematical modeling of microbial ecology and the analysis of meta-omics data through modeling methods. I am specifically interested in the modeling of the gut microbiota in its environment (rheology, trophic chains, host-microbiota interactions...), together with inference methodologies to decipher microbial interactions driving microbiota dynamics.
Arnaud Bridier, Jean-Christophe Piard, Caroline Pandin, Simon Labarthe, Florence Dubois-Brissonnet, Romain Briandet, 2017. Spatial organization plasticity as an adaptive driver of surface microbial communities. Frontiers in Microbiology, 8: 1364. Doi: 10.3389/ fmicb.2017.01364.
Widder S, Allen RJ, Pfeiffer T, Curtis TP, Wiuf C, Sloan WT, Cordero OX, Brown SP8, Momeni B, Shou W, Kettle H, Flint HJ, Haas AF, Laroche B, Kreft JU, Rainey PB, Freilich S, Schuster S, Milferstedt K, van der Meer JR, Groβkopf T, Huisman J, Free A, Picioreanu C, Quince C, Klapper I, Labarthe S, Smets BF, Wang H7; Isaac Newton Institute Fellows, Soyer OS, 2016. Challenges in microbial ecology: building predictive understanding of community function and dynamics. ISME Journal, 10(11):2557-2568.
Tamara El Bouti, Thierry Goudon, Simon Labarthe, Béatrice Laroche, Bastien Polizzi, Amira Rachah, Magali Ribot, Rémi Tesson, 2016. A mixture model for the dynamic of the gut mucus layer. ESAIM: Proceedings, EDP Sciences, 2016, 55,11-130.
Yves Coudière, Jacques Henry, Simon Labarthe. 2017. An asymptotic two-layer monodomain model of cardiac electrophysiology in the atria: derivation and convergence, SIAM J. APPL. MATH., SIAM, 77 (2),409429.
Simon Labarthe, Jason Bayer, Yves Coudière, Jacques Henry, Hubert Cochet, Pierre Jais, Edward Vigmond, et al. 2014. A bilayer model of human atria: mathematical background, construction, and assessment. EPEuropace, Oxford University Press: Policy B, 2014, pp.29. Doi: 10.1093/europace/euu256.