Paul François

viktoriia.mochulska@mila.quebec

PhD - McGill University

Maria Yampolskaya

Postdoctorate - Université de Montréal

maria.yampolskaya@mila.quebec

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Publications

Mapping T cell phenotype with machine learning to optimize cancer immunotherapy 2307563

Hassan Jamaleddine

Grégoire Altan‐Bonnet

Mahmood Mazarei

Madison Wahlsten

Timothy West

Abstract Introduction T cells play a central role in adaptive immune responses, enabling vertebrates to fight infections and eliminate cance… (see more)r cells. Cancer immunotherapies, and in particular adoptive T-cell therapies, thus aim to harness their tumor-destroying capabilities to treat, or even cure, cancer patients. Despite recent advances in the development of these therapies, a major limitation remains our inability to predict, a priori, which tumor-infiltrating T cells will be best equipped to carry out anti-tumor immunity. Indeed, this multifactorial problem requires a model that integrates information on T cell receptor (TCR) specificity, tumor antigen abundance, and T cell activation history within the complex tumor microenvironment, yet such considerations are largely absent from current T cell selection strategies. Methods To address this gap, we are using a combination approach of high-throughput robotic multiplexing with computational and machine learning techniques to identify signatures of T cell phenotype that best predict response to tumor antigens. Specifically, we aim to study the roles of antigen presentation, TCR/antigen affinity, and inflammatory milieu on shaping T cell phenotype at the single-cell level, and training machine learning models to re-derive the activation history of T cells both in vivo and ex vivo. Results Preliminary results from in vitro co-culture data of T cells with cognate antigen-bearing splenocytes suggests that T cell antigen strength can indeed be back-calculated from single cell phenotype as measured by spectral flow cytometry. Conclusion With a validated model of tumor antigenicity in T cells, this research project aims to better inform T cell selection and optimal preparation strategies for adoptive T-cell therapies. Funding Source n/a Topic Categories Computational and Systems Immunology (COMP)

2026-07-27

Journal of Immunology (published)

Cycles upon cycles - Temperature Scaling of Medaka Development

Sapna Chhabra

Carina B. Vibe

Anubhuti Anushree

Kristina S. Stapornwongkul

Thomas Thumberger

Joachim Wittbrodt

Alexander Aulehla

ABSTRACT How organisms develop in dynamic environmental conditions is a fundamental question. We asked how day-night temperature cycles impa… (see more)ct embryonic axis elongation and segmentation, itself a cyclic process linked to the segmentation clock, using the Japanese rice fish medaka. We developed an unbiased dimensional reduction approach, based on Singular Value Decomposition (SVD), to reliably identify the dynamic modes of segmentation clock oscillations across all temperature conditions. We reveal that the two major dynamic modes show opposite temperature sensitivities: while the temporal oscillation (mode 1) varies strongly with temperature, the spatial phase gradient (mode 2) appears largely temperature invariant. In addition, we found developmental parameters with intermediate, sub-scaled temperature responses, such as axis elongation. We used theoretical modeling to understand how dynamic modes emerge from the underlying local oscillation dynamics and axis elongation. We then exposed embryos to circadian and ultradian temperature cycles to reveal dynamic response patterns of oscillations and axis elongation, and found how these responses are integrated into morphological features. Combined, our theoretical-experimental results support a model in which the dynamic integration of temporal (i.e. segmentation clock related) and spatial (i.e. axis elongation) processes, in particular their sub-scaled temperature response patterns, quantitatively compensate each other to yield a robust, temperature-invariant axis patterning outcome.

2026-05-01

bioRxiv (preprint)

Oscillatory co-expression of HES1 and HES5 Enables a hybrid state in a cross-repressive transcription factor regulatory motif.

Veronica Biga

Anzy Miller

Anoushka Kamath

Robert Lea

Ying Q P Mak

Antony Adamson

Elli Marinopoulou

Nancy Papalopulu

Cerys Manning

Many cell fate decisions in the developing neural tube are directed by cross-repressive transcription factor (TF) motifs that generate bista… (see more)bility, such that cells express one TF but not both. Hybrid states in which cells express both cross-repressing fate determinants have been observed, but how these arise or persist remains unclear. Here, we focus on HES1 and HES5, auto-repressive, oscillatory TFs that regulate neural progenitor maintenance and are expressed in adjacent dorsoventral progenitor domains in the developing spinal cord. Knockdown experiments demonstrate that HES1 and HES5 are cross-repressing in mouse spinal cord neural progenitors, and live-cell imaging in vitro shows that they can be co-expressed, defining a hybrid state. In this state, HES co-oscillate in-phase within single cells. Computational modelling indicates that modulation of cross-repression strength or relative TF abundance destabilises this state, driving resolution towards a single oscillatory HES TF. This is consistent with in vivo analysis showing transient HES1/HES5 co-expression followed by progressive restriction to a single TF oscillator. Our findings suggest that oscillatory expression enables co-existence of cross-repressing TFs, allowing hybrid states within a developmental bistable motif.

2026-04-30

Development (published)

Learning the Principles of T Cell Antigen Discernment

François X. P. Bourassa

Sooraj Achar

Grégoire Altan-Bonnet

T cells are central to the adaptive immune response, capable of detecting pathogenic antigens while ignoring healthy tissues with remarkable… (see more) specificity and sensitivity. Quantitatively understanding how T cell receptors discern among antigens requires biophysical models and theoretical analyses of signaling networks. Here, we review current theoretical frameworks of antigen recognition in the context of modern experimental and computational advances. Antigen potency spans a continuum and exhibits nonlinear effects within complex mixtures, challenging discrete classification and simple threshold-based models. This complexity motivates the development of models, such as adaptive kinetic proofreading, that integrate both activating and inhibitory signals. Advances in high-throughput technologies now generate large-scale, quantitative data sets, enabling the refinement of such models through statistical and machine learning approaches. This convergence of theory, data, and computation promises deeper insights into immune decision-making and opens new avenues for rational immunotherapy design.

2026-02-04

Annual Review of Biophysics (published)

Manifold Learning for Olfactory Habituation to Strongly Fluctuating Backgrounds

François X. P. Bourassa

Gautam Reddy

Massimo Vergassola

Animals rely on their sense of smell to survive, but important olfactory cues are mixed with confounding background odors that fluctuate due… (see more) to atmospheric turbulence. It is unclear how the olfactory system habituates to such stochastic backgrounds to detect behaviorally important odors. Here, we explicitly consider the high-dimensional nature of odor coding, the natural statistics of odor fluctuations, and the architecture of the early olfactory pathway. We show that their combination favors a manifold learning mechanism for olfactory habituation over alternatives based on predictive filtering. Manifold learning is implemented in our model by a biologically plausible network of inhibitory interneurons in the early olfactory pathway. We demonstrate that plasticity rules based on the Intrator, Bienenstock, Cooper, and Munro (IBCM) model or an online principal components analysis algorithm are effective at implementing this mechanism in turbulent conditions and outperform previous models relying on mean background subtraction. Interneurons with an IBCM plasticity rule acquire selectivity to independently varying odors. This manifold learning mechanism offers a path toward distinguishing plasticity rules in experiments and could be leveraged by other biological circuits facing fluctuating environments.

2026-01-15

PRX Life (published)

Division Asymmetry Drives Cell Size Variability in Budding Yeast

Felix Proulx-Giraldeau

Xin Gao

Yagya Chadha

Jordan Yupeng Xiao

Kurt M. Schmoller

Jan M. Skotheim

Cell size variability within proliferating populations reflects the interdependent regulation of cell growth and division as well as intrins… (see more)ically stochastic effects. In budding yeast, the G1/S transition exerts strong size control in daughter cells, which manifests as the inverse correlation between how big a cell is when it is born and how much it grows in G1. However, mutations affecting this size control checkpoint only modestly influence population-wide size variability, often altering the coefficient of variation (CV) only by ∼10%. To resolve this paradox, we combine computational modeling and live-cell imaging to identify the principal determinants of cell size variability. Using an experimentally validated stochastic model of the yeast cell cycle, we perform parameter sensitivity analysis and find that division asymmetry between mothers and daughters is the dominant driver of CV, outweighing the effects of G1/S size control. Experimental measurements across genetic perturbations and growth conditions confirm a strong correlation between mother-daughter size asymmetry and population CV. These findings reconcile previous observations and show how asymmetric division operates in concert with G1/S size control to govern cell size heterogeneity.

2026-01-08

eLife (accepted)

Foci, waves, excitability: Self-organization of phase waves in a model of asymmetrically coupled embryonic oscillators

Anonymous

Kaushik Roy

The segmentation clock is an emergent embryonic oscillator that controls the periodic formation of vertebrae precursors (or somites). It rel… (see more)ies on the self-organization at the presomitic mesoderm (PSM) level of multiple coupled cellular oscillators. Dissociation-reaggregation experiments have further revealed that ensembles made of such cellular oscillators self-organize into an oscillatory bidimensional system, showing concentric waves around multiple foci. Here, we systematically study the dynamics of a two-dimensional lattice of phase oscillators locally coupled to their nearest neighbors through a biharmonic coupling function of the form sinθ+Λsin^{2}θ. This coupling was inferred from the phase response curve of entrainment experiments on cell cultures, leading to the formulation of a minimal Elliptic Radial Isochron Cycle (ERIC) phase model. We show that such ERIC-based coupling parsimoniously explains the emergence of self-organized concentric phase wave patterns around multiple foci for a range of weak couplings and wide distributions of initial random phases, closely mimicking experimental conditions. We further study extended modalities of this problem to derive an atlas of possible behaviors. In particular, we predict the dominant observation of spirals over target wave patterns for initial phase distributions wider than approximately π. Since PSM cells further display properties of an excitable system, we also introduce excitability into our simple model and show that it also supports the observation of concentric phase waves for the conditions of the experiment. Our work suggests important modifications that can be made to the simple phase model with Kuramoto coupling, which can provide further layers of complexity and aid in the explanation of the spatial aspects of self-organization in the segmentation clock.

2025-12-31

Physical Review E (published)

Generative epigenetic landscapes map the topology and topography of cell fates.

Epigenetic landscapes were proposed by Waddington as the central concept to describe cell fate dynamics in a locally low-dimensional space. … (see more)In modern landscape models, attractors represent cell types, and stochastic jumps and bifurcations drive cellular decisions, allowing for quantitative and predictive descriptions. However, given a biological problem of interest, we still lack tools to infer and build possible Waddington landscapes systematically. In this study, we propose a generative model for deriving epigenetic landscapes compatible with data. To build the landscapes, we combine gradient and rotational vector fields composed of locally weighted elements that encode ‘valleys’ of the Waddington landscape, resulting in interpretable models. We optimize landscapes through computational evolution and illustrate our approach with two developmental examples: metazoan segmentation and neuromesoderm differentiation. In both cases, we obtain ensembles of solutions that reveal both known and novel landscapes in terms of topology and bifurcations. Conversely, topographic features appear strongly constrained by dynamical data, which suggests that our approach can generically derive interpretable and predictive epigenetic landscapes.

2025-12-15

PubMed (unknown)

Dynamical model and geometric insights in the discontinuity theory of immunity

Christian Mauffette Denis

Maya Dagher

Vincent Verbavatz

François X.P. Bourassa

Grégoire Altan-Bonnet

The immune system’s most basic task is to decide what is “self” and “non-self”, but a precise definition of self versus non-self r… (see more)emains challenging. According to the discontinuity theory of immunity, effector responses depend on how quickly an antigenic stimulus changes: rapid change triggers an immune response, whereas gradual change fosters tolerance. We present a model of adaptive immune dynamics including T cells, Tregs and cytokines that reproduces the hallmarks of the discontinuity theory. The model allows for sharp discrimination between acute and chronic infections based on the growth rate of the immune challenge, and vaccination-like acute dynamics upon presentation of a bolus of immune challenge. We further show that the model behavior only depends on a handful of testable assumptions that we map to geometric constraints in phase space. This suggests that the model properties are generic and robust across alternative mechanistic details. We also examine the impact of multiple concurrent immune challenges in this model, and demonstrate the occurrence of dynamical antagonism, wherein, in some parameter regimes, slow-growing challenges hinder acute responses to fast-growing ones, with further counter-intuitive behaviors for sequential co-infections. Together, these results place the discontinuity theory on firm mathematical footing and encourage further investigation of interferences of multi-agent immune challenges, from chronic viral co-infections to cancer immunoediting.

2025-07-17

bioRxiv (preprint)

Engineering TCR-controlled fuzzy logic into CAR T cells enhances therapeutic specificity

Taisuke Kondo

François X.P. Bourassa

Sooraj Achar

MyLinh T. Duong

Anirvan Ghosh

Jérémy Biton

Grégoire Altan-Bonnet

Naomi Taylor

2025-04-10

Cell (unknown)

Unclocklike biological oscillators with frequency memory

Christian Mauffette Denis

Entrainment experiments on the vertebrate segmentation clock have revealed that embryonic oscillators actively change their internal frequen… (see more)cy to adapt to the driving signal. This is not consistent with either a one-dimensional clock model or a limit-cycle model, but rather it suggests a new “unclocklike” behavior. In this work, we propose simple, biologically realistic descriptions of such internal frequency adaptation, where a phase oscillator activates a memory variable controlling the oscillator's frequency. We study two opposite limits for the control of the memory variable, one with a smooth phase-averaging memory field, and the other with a pulsatile, phase-dependent activation. Both models recapitulate intriguing properties of the entrained segmentation clock, such as very broad Arnold tongues and an entrainment phase plateauing with detuning. We compute analytically multiple properties of such systems, such as entrainment phases and cycle shapes. We further describe new phenomena, including hysteresis in entrainment, bistability in the frequency of the entrained oscillator, and probabilistic entrainment. Our work shows that oscillators with frequency memory can exhibit new classes of unclocklike properties that can be tested through experimental entrainment. Published by the American Physical Society 2024

2024-10-30

Physical Review X Life (published)

arxiv.org

Nonreciprocal synchronization in embryonic oscillator ensembles

Christine Ho

Laurent Jutras-Dubé

Michael L. Zhao

Gregor Mönke

István Z. Kiss

Alexander Aulehla

Synchronization of coupled oscillators is a universal phenomenon encountered across different scales and contexts e.g., chemical wave patter… (see more)ns, superconductors and the unison applause we witness in concert halls. The existence of common underlying coupling rules define universality classes, revealing a fundamental sameness between seemingly distinct systems. Identifying rules of synchronization in any particular setting is hence of paramount relevance. Here, we address the coupling rules within an embryonic oscillator ensemble linked to vertebrate embryo body axis segmentation. In vertebrates, the periodic segmentation of the body axis involves synchronized signaling oscillations in cells within the presomitic mesoderm (PSM), from which somites, the pre-vertebrae, form. At the molecular level, it is known that intact Notch-signaling and cell-to-cell contact is required for synchronization between PSM cells. However, an understanding of the coupling rules is still lacking. To identify these, we develop a novel experimental assay that enables direct quantification of synchronization dynamics within mixtures of oscillating cell ensembles, for which the initial input frequency and phase distribution are known. Our results reveal a “winner-takes-it-all” synchronization outcome i.e., the emerging collective rhythm matches one of the input rhythms. Using a combination of theory and experimental validation, we develop a new coupling model, the “Rectified Kuramoto” (ReKu) model, characterized by a phase-dependent, non-reciprocal interaction in the coupling of oscillatory cells. Such non-reciprocal synchronization rules reveal fundamental similarities between embryonic oscillators and a class of collective behaviours seen in neurons and fireflies, where higher level computations are performed and linked to non-reciprocal synchronization.

2024-08-26

Proceedings of the National Academy of Sciences (published)