# From single cells to population: using deep learning and mathematical modeling to investigate the key differences between drug-tolerant and drug-sensitive cancer cells in live-cell microscopy experiments

Marielle Péré, Marina Moureau Barbato, Benjamin Bian, Carlos Martinez,

Jérémie Roux

► To cite this version:

Marielle Péré, Marina Moureau Barbato, Benjamin Bian, Carlos Martinez, Jérémie Roux. From single cells to population: using deep learning and mathematical modeling to investigate the key differences between drug-tolerant and drug-sensitive cancer cells in live-cell microscopy experiments. MEI Center/Université Côte d'Azur International Symposium & Summer School 2025 - A New Era in Medical and Healthcare Science: from reality to virtuality, Jun 2025, Osaka (Japan), Japan. hal-05156713

HAL Id: hal-05156713

https://inria.hal.science/hal-05156713v1

Submitted on 10 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
From single cells to population: using deep learning and
mathematical modeling to investigate the key differences between
drug-tolerant and drug-sensitive cancer cells in live-cell
microscopy experiments.

Marielle Péré¹,²,³, Marina Moureau Barbato¹,², Benjamin Bian¹,²,
Carlos Martinez⁴, Jérémie Roux¹,²,³

¹ Université Côte d'Azur, Institut de Pharmacologie Moléculaire et Cellulaire (IPMC, CNRS UMR 7275, Inserm 1323), 06560 Sophia Antipolis, France.

²Université Côte d'Azur, IPMC (CNRS UMR 7275 Inserm 1323), Institut Hospitalo-Universitaire (IHU) RespirERA, Valbonne, France.

³ Université Côte d'Azur, Inria, INRAE, CNRS, MACBES Team, 06902 Sophia Antipolis, France.

⁴ Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2085, Valparaíso, 2362803, Chile

The emergence of drug-tolerant cells in isogenic cancer populations marks a key-step towards the acquisition of genetic resistance. Identifying the dynamic and molecular signature associated with drug-tolerant cells is essential to design new therapeutic strategies targeting these problematic cells. In this talk, I will present two of our most recent studies carried out during my current postdoctoral fellowship at the Institute of Molecular and Cellular Pharmacology. These approaches combine live-cell microscopy experiments with computational modeling frameworks to identify drug-tolerant cells and reveal the molecular mechanisms underlying their emergence.

After a brief introduction of our experimental pipeline, I will present our first results on modeling single-cell apoptosis signaling pathway using bayesian hierarchical models. The aim of this study is to reproduce the spectrum of drug-sensitive and drug-tolerant cell dynamics observed in different cancer cell lines in response to drug ligands. (Using a bayesian framework, it is possible to discover a specific signaling signature common to all cells sharing the same phenotype, while taking account data uncertainties.) Finally, focusing on the population level, I will briefly present our more recent findings demonstrating that in a cell population, most cells that are tolerant to Tumor necrosis factor (TNF)-Related Apoptosis Inducing Ligand (TRAIL) are sensitive to necroptotic drugs. I will detail our mathematical model of phenotypic switch in drug sensitivity between these two cell-death modalities (apoptosis and necroptosis), and how we have used this model to uncover new insights into this sensitization process. I will finish by presenting how we intend to use it to control cell states, opening up new possibilities for reducing the emergence of drug-tolerant cells by designing smarter drug combinations.