Portrait de Paul François

Paul François

Membre académique associé
paul.francois@mila.quebec
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

Publications

Waves, patterns, bifurcations: A tutorial review on the vertebrate segmentation clock
Paul François
Victoria Mochulska
Proper vertebrae formation relies on a tissue-wide oscillator called the segmentation clock. Individual cellular oscillators in the presomit… (voir plus)ic mesoderm are modulated by intercellular coupling and external signals, leading to the propagation of oscillatory waves of genetic expression eventually stabilizing into a static pattern. Here, we review 4 decades of biophysical models of this process, starting from the pioneering Clock and Wavefront model by Cooke and Zeeman, and the reaction–diffusion model by Meinhardt. We discuss how modern descriptions followed advances in molecular description and visualization of the process, reviewing phase models, delayed models, systems-level, and finally geometric models. We connect models to high-level aspects of embryonic development from embryonic scaling to wave propagation, up to reconstructed stem cell systems. We provide new analytical calculations and insights into classical and recent models, leading us to propose a geometric description of somitogenesis organized along two primary waves of differentiation.
2024-07-31
Physics Reports (publié)
doi.org
A Waddington landscape for prototype learning in generalized Hopfield networks
Nacer Eddine Boukacem
Allen Leary
Robin Theriault
Felix Gottlieb
Madhav Mani
Paul François
Networks in machine learning offer examples of complex high-dimensional dynamical systems reminiscent of biological systems. Here, we study … (voir plus)the learning dynamics of Generalized Hopfield networks, which permit a visualization of internal memories. These networks have been shown to proceed through a 'feature-to-prototype' transition, as the strength of network nonlinearity is increased, wherein the learned, or terminal, states of internal memories transition from mixed to pure states. Focusing on the prototype learning dynamics of the internal memories we observe a strong resemblance to the canalized, or low-dimensional, dynamics of cells as they differentiate within a Waddingtonian landscape. Dynamically, we demonstrate that learning in a Generalized Hopfield Network proceeds through sequential 'splits' in memory space. Furthermore, order of splitting is interpretable and reproducible. The dynamics between the splits are canalized in the Waddington sense -- robust to variations in detailed aspects of the system. In attempting to make the analogy a rigorous equivalence, we study smaller subsystems that exhibit similar properties to the full system. We combine analytical calculations with numerical simulations to study the dynamical emergence of the feature-to-prototype transition, and the behaviour of splits in the landscape, saddles points, visited during learning. We exhibit regimes where saddles appear and disappear through saddle-node bifurcations, qualitatively changing the distribution of learned memories as the strength of the nonlinearity is varied -- allowing us to systematically investigate the mechanisms that underlie the emergence of Waddingtonian dynamics. Memories can thus differentiate in a predictive and controlled way, revealing new bridges between experimental biology, dynamical systems theory, and machine learning.
2024-07-22
Physical Review Research (publié)
doi.org
arxiv.org
Abstract 6324: Antagonism-enforced braking system to enhance CAR T cell therapeutic specificity
Taisuke Kondo
François X. P. Bourassa
Sooraj R. Achar
Justyn DuSold
Pablo Cespedes
Madison Wahlsten
Audun Kvalvaag
Guillaume Gaud
Paul E. Love
Michael Dustin
Grégoire Altan-Bonnet
Paul François
Naomi Taylor
Chimeric Antigen Receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of hematological malignancies. However, the … (voir plus)rarity of cell surface protein targets that are specific to cancerous but not vital healthy tissue has hindered its broad application to solid tumor treatment. While new logic-gated CAR designs have shown reduced toxicity against healthy tissues, the generalizability of such approaches across tumors remains unclear. Here, we harness a universal characteristic of endogenous T cell receptors (TCRs), their ability to discriminate between self and non-self ligands through inhibition of response against self (weak) antigens, to develop a broadly applicable method of enhancing immunotherapeutic precision. We hypothesized that this discriminatory mechanism, known as antagonism, would apply across receptors, allowing for a transfer of specificity from TCRs onto CARs. We therefore systematically mapped out the responses of CAR T cells to joint TCR and CAR stimulations. We first engineered murine T cells with an ovalbumin-specific TCR to express a CAR targeting murine CD19 and discovered that the expression of a strong TCR antigen on CD19+ leukemia enhanced CAR T killing. Importantly though, the presence of a weak TCR antigen antagonized CAR T responses, assessed by in vitro multiplexed dynamic profiling as well as in vivo cytotoxicity. We developed a mathematical model based on cross-receptor inhibitory coupling that accurately predicted the extent of TCR/CAR antagonism across a wide range of immunological settings. This model was validated in a CD19+ B16 mouse melanoma model showing that TCR/CAR antagonism decreased the infiltration of a tumor-reactive T cell cluster, while TCR/CAR agonism enhanced infiltration of this T cell cluster. We then applied our quantitative knowledge of TCR/CAR crosstalk to design an Antagonism-Enforced Braking System (AEBS) for CAR T cell therapy. This was assessed in a model system using a CAR targeting the tyrosine-protein kinase erbB-2 (HER2) together with a hedgehog acyltransferase (HHAT) peptide-specific TCR that binds strongly to mutated tumor neoantigen while retaining weak affinity for the wild-type self-antigen on healthy tissue. We established a humanized in vivo model of CAR T function and found that AEBS CAR T cells maintained high anti-tumor activity against a human lung adenocarcinoma (PC9) but notably, their anti-tissue cytotoxicity against human bronchial epithelial cells (BEAS-2B) was minimized. AEBS CAR T cells therefore sharpen the discriminatory power of synthetic anti-tumor lymphocytes. Our work highlights a novel mechanism by which TCRs can enforce CAR T cell specificity, with practical implications for the rational design of future anti-leukemia immunotherapies. Citation Format: Taisuke Kondo, François X. Bourassa, Sooraj Achar, Justyn DuSold, Pablo Cespedes, Madison Wahlsten, Audun Kvalvaag, Guillaume Gaud, Paul Love, Michael Dustin, Gregoire Altan-Bonnet, Paul François, Naomi Taylor. Antagonism-enforced braking system to enhance CAR T cell therapeutic specificity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6324.
2024-03-21
Cancer Research (publié)
doi.org
Initial Separation of Poles to form a Bipolar Spindle is a Rapid and Irreversible Process
Allen Y. Leary
Elena Nazarova
Shannon Sim
Eileen O'Toole
Paul François
Jackie Vogel
2023-12-31
International Conference on Machine Learning (publié)
doi.org
Harnessing TCR/CAR Antagonism to Enhance Immunotherapeutic Precision
Taisuke Kondo
François X. P. Bourassa
Sooraj R. Achar
Justyn DuSold
Pablo Cespedes
Madison Wahlsten
Audun Kvalvaag
Guillaume Gaud
Paul E. Love
Michael Dustin
Grégoire Altan-Bonnet
Paul François
Naomi Taylor
2023-11-27
Blood (publié)
doi.org
CD3ζ ITAMs enable ligand discrimination and antagonism by inhibiting TCR signaling in response to low-affinity peptides
Guillaume Gaud
Sooraj Achar
François X. P. Bourassa
John Davies
Teri Hatzihristidis
Seeyoung Choi
Taisuke Kondo
Selamawit Gossa
Jan Lee
Paul Juneau
Naomi Taylor
Christian S. Hinrichs
Dorian B. McGavern
Paul François
Grégoire Altan‐Bonnet
Paul E. Love
2023-11-08
Nature Immunology (inconnu)
doi.org
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
2023-02-22
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