Portrait de Paul François

Paul François

Membre académique associé
Professeur titulaire, Université de Montréal, Département de biochimie et de médecine moléculaire
Professeur associé, McGill University, Département de physique
Sujets de recherche
Biologie computationnelle
Systèmes dynamiques
Théorie de l'apprentissage automatique
Théorie de l'information
Google Scholar

Biographie

Paul François est professeur titulaire de bio-informatique au Département de biochimie et médecine moléculaire de la Faculté de médecine de l'Université de Montréal, et professeur adjoint au Département de physique de l'Université McGill. Il est biophysicien et se concentre sur l'application de méthodes informatiques (y compris l'apprentissage automatique) à l'évolution, au développement embryonnaire et à l'immunologie. Il est membre associé de Mila – Institut québécois d'intelligence artificielle.

Postes antérieurs :

- Professeur agrégé de physique, Université McGill (2016-2023)

- Professeur adjoint de physique, Université McGill (2010-2016)

Éducation et formation :

- Postdoctorat, Siggia Lab, Université Rockefeller, États-Unis (2005-2010)

- Doctorat en physique théorique, Laboratoire Hakim, École normale supérieure/Université Paris VII, France (2002-2005)

- M. Sc. en physique théorique, École normale supérieure/École polytechnique, France (2001-2002)

- Diplôme d'ingénieur, majeure en physique, École polytechnique, France (1998-2001), promotion X 98

Quelques récompenses :

- 2019 : Médaille commémorative Rutherford en physique, Société royale du Canada

- 2017 : Médaille CAP Herzberg, Association canadienne des physiciens et physiciennes

- 2015 : Prix du recteur de McGill décerné à des chercheurs émergents exceptionnels

- 2014 : Chercheur Simons en modélisation mathématique des systèmes vivants

- 2007 : Bourse postdoctorale Lavoisier (ministère français des Affaires étrangères)

- 2007 : Prix Le Monde pour la recherche scientifique

Étudiants actuels

Qichen Huang
Maîtrise recherche - UdeM
Site web
Github
Philippe Martin
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Guy Wolf
Victoria Mochulska
Doctorat - McGill
Maria Yampolskaya
Postdoctorat - UdeM
Site web
Github
Google Scholar

Publications

TCR/Chimeric Antigen Receptor (CAR) Cross-Antagonism to Fine-Tune CAR-T cell Immunotherapy
Grégoire Altan-Bonnet
Taisuke Kondo
François X. P. Bourassa
Sooraj R. Achar
Justyn DuSold
Pablo Cespedes
Madison Wahlsten
Guillaume Gaud
Paul E Love
Mike Dustin
Paul François
Naomi Taylor
Chimeric antigen receptor (CAR) T cells, are created by extracting T cells from a cancer patient, engineering them to express a CAR targetin… (voir plus)g a tumor specific molecule, then reintroducing them back into the patient. A patient’s T cells contain their own endogenous T cell receptors (TCRs) however, which could potentially interact with the exogenous CAR inserted into the cell. In this study, we examine how TCR and CAR signals interact upon CAR-T activation. We show that weak TCR stimulation can reduce (antagonize) or increase overall CAR-T response, both in vitro and in vivo, across multiple tumor models, in both mouse and human T cells. We further show that the behavior of these TCR/CAR interactions can be manipulated by changing various characteristics of the TCR, CAR, and associated ligands. While this behavior is complex, we show that it can be described by a single mathematical model based on the adaptive kinetic proofreading scheme of ligand discrimination. We conclude by presenting potential applications for cancer immunotherapy. Intramural Research Program of the National Cancer Institute
2023-04-30
Journal of Immunology (publié)
doi.org
What did the T cell see? A deep-learning model of CD8+ T cell activation reveals sharp antigen discrimination at the single cell level
Madison Wahlsten
Amin Akhshi
Sooraj R. Achar
Anagha Yogam Krishnan
Paul François
Grégoire Altan-Bonnet
Immunotherapies such as checkpoint blockade antibodies to block T cell exhaustion have been successful in several cancers such as non-small … (voir plus)cell lung cancer and melanoma, but limited in others (e.g., pancreatic or prostate carcinomas) owing to differences in tumor antigenicity. Therefore, quantifying tumor antigenicity is critical for successful immunotherapies. Our lab has shown that antigenicity can be encoded in a single parameter derived from bulk cytokine dynamics in ex vivo co-cultures between antigen presenting cells (APCs) and T cells. Here we built a model that can capture the antigenicity seen by individual cells. Using a custom robotic platform, we generated high-throughput kinetics of T cell activation in co-culture with APCs by analyzing cells at various timepoints across a large set of activation conditions. We performed spectral flow cytometry to measure the expression of up to 30 surface markers and intracellular signals per cell. To analyze our content-rich datasets, we designed a machine learning-based model that can classify the antigen seen by an individual cell using expression values from flow cytometry. The model performs well not only at classifying T cells (ROC-AUC > 0.91), but also APCs (ROC-AUC > 0.88), suggesting that each individual leukocyte may register the quality of antigen being presented. Blocking cytokine signaling disrupted this antigen classification. Our study demonstrates that every individual lymphocyte can bridge local and global response to achieve high discriminatory power of antigens.
2023-04-30
Journal of Immunology (publié)
doi.org
New wave theory
Paul François
2023-02-14
Development (publié)
doi.org
Evolution of cell size control is canalized towards adders or sizers by cell cycle structure and selective pressures
Felix Proulx-Giraldeau
Jan M Skotheim
Paul François
Cell size is controlled to be within a specific range to support physiological function. To control their size, cells use diverse mechanisms… (voir plus) ranging from ‘sizers’, in which differences in cell size are compensated for in a single cell division cycle, to ‘adders’, in which a constant amount of cell growth occurs in each cell cycle. This diversity raises the question why a particular cell would implement one rather than another mechanism? To address this question, we performed a series of simulations evolving cell size control networks. The size control mechanism that evolved was influenced by both cell cycle structure and specific selection pressures. Moreover, evolved networks recapitulated known size control properties of naturally occurring networks. If the mechanism is based on a G1 size control and an S/G2/M timer, as found for budding yeast and some human cells, adders likely evolve. But, if the G1 phase is significantly longer than the S/G2/M phase, as is often the case in mammalian cells in vivo, sizers become more likely. Sizers also evolve when the cell cycle structure is inverted so that G1 is a timer, while S/G2/M performs size control, as is the case for the fission yeast S. pombe. For some size control networks, cell size consistently decreases in each cycle until a burst of cell cycle inhibitor drives an extended G1 phase much like the cell division cycle of the green algae Chlamydomonas. That these size control networks evolved such self-organized criticality shows how the evolution of complex systems can drive the emergence of critical processes.
2022-09-29
eLife (publié)
doi.org
Universal antigen encoding of T cell activation from high-dimensional cytokine dynamics
Sooraj R. Achar
François X. P. Bourassa
Thomas J. Rademaker
Angela Lee
Taisuke Kondo
Emanuel Salazar-Cavazos
John S. Davies
Naomi Taylor
Paul François
Grégoire Altan-Bonnet
2022-05-19
Science (publié)
doi.org
Endocytic proteins with prion-like domains form viscoelastic condensates that enable membrane remodeling
Louis-Philippe Bergeron-Sandoval
Sandeep Kumar
Hossein Khadivi Heris
Catherine L. A. Chang
Caitlin E. Cornell
Sarah L. Keller
Paul François
Adam G. Hendricks
Allen J. Ehrlicher
Rohit V. Pappu
Stephen W. Michnick
The uptake of molecules into cells, known as endocytosis, requires membrane invagination and the formation of vesicles. A version of endocyt… (voir plus)osis that is independent of actin polymerization is aided by the assembly of membraneless biomolecular condensates at the site of membrane invagination. Here, we show that endocytic condensates are viscoelastic bodies that concentrate key proteins with prion-like domains to enable membrane remodeling. A distinct molecular grammar, namely the preference for glutamine versus asparagine residues, underlies the cohesive interactions that give rise to endocytic condensates. We incorporate material properties inferred using active rheology into a mechanical model to explain how cohesive interactions within condensates and interfacial tensions among condensates, membranes, and the cytosol can drive membrane invagination to initiate endocyosis.
2021-12-08
Proceedings of the National Academy of Sciences of the United States of America (publié)
doi.org