PhD Position F/M Modeling and Dynamical Analyses of Cell Death Signaling to Understand Tumor Cell Responses to Cancer Therapeutics and the Immune System

Tolerant tumor cells can emerge after cancer treatments even when combination strategies are used and contribute to partial treatment efficacy. Identifying the molecular mechanisms involved in cell tolerance is thus an essential task in the rational design of efficacious drug combinations.

One of the major pathways contributing to cell death is the extrinsic apoptosis pathway, which activates a cascade of proteins called caspases, responsible for targeting and cleaving many proteins in the cell, and eventually leading to cell death. However, other components and pathways are known to strongly interact with the extrinsic apoptosis pathway, including both anti-apoptotic (such as FLIP or NF-kB and PI3K/Akt pathways) or pro-apoptotic (such as immune effectors, or p53 and JNK pathways) ones.

Mathematical modeling of these pathways is essential to understand the interaction between the different components and their response to cancer drugs and will help to identify potential targets for drug combinations.

This project is a collaboration between M. Chaves from MACBES team at Inria and the team of J. Roux at IPMC (Institut de Pharmacologie Moleculaire et Cellulaire, CNRS, Sophia Antipolis) to develop a modeling workflow that uses single-cell response data and mathematical models to understand the dynamic interplay between death signaling pathways and its role in tumor cell killing.

Funding for this project will be obtained by applying to PhD fellowships at Inria and/or the doctoral schools of Université Côte d'Azur.

The two teams have been developing mathematical models for the extrinsic apoptosis pathway (Pere et al, 2020; Chaves et al 2021), with the goal of identifying the molecular mechanisms involved in cell tolerance to cancer treatments. Two directions will be followed in the current project:

The first goal is to develop a minimal system of ordinary differential equations that integrates a reduced model of extrinsic apoptosis (Pere et al, 2020) with a set of both anti- and pro-apoptotic components, such as FLIP and granzyme B, to include the principal components that modulate caspase activation. The model's parameters will be calibrated with single-cell data from the Roux Lab. A special focus will be on model calibration to caspase response during the first hour after treatment, for subsequent use in cell fate forecasting. "Fast" components that are likely to modulate cell response during the first hour will therefore be included in the model: a strong candidate is the granzyme B pathway, since cell death mediated by granzyme B occurs on average about 20 minutes after natural killer cell contact with the tumor cell.

A second direction is to analyze the transcriptomic profiles of single cells. The Roux Lab developed a pipeline that links the predicted drug response of a cell to its own genome-wide transcriptomic profile in single-cells (Meyer et al, 2020). This pipeline uncovered a cell sensitivity signature to TRAIL, composed of a set of genes which were shown to increase cell sensitivity to death receptor agonists. The goal is to investigate and quantify the role of these high-ranking genes on the apoptosis pathway and their effect on cell-to-cell variability in drug response.

 References:

- M. Chaves, L.C. Gomes-Pereira, and J. Roux. Two-level modeling approach to identify the regulatory dynamics capturing drug response heterogeneity in single-cells, Scientific Reports, 11, pp. 20809, 2021.

- M Pere, M. Chaves, and J. Roux. Core models of receptor reactions evaluate basic pathway designs enabling heterogeneous commitments to apoptosis. 18th Int Conf Computational Methods in Systems Biology (CMSB 2020), 2020.

- Meyer M, Paquet A, Arguel MJ, Peyre L, Gomes-Pereira LC, Lebrigand K, Mograbi B, Brest P, Waldmann R, Barbry P, Hofman P, Roux J. Profiling the Non-genetic Origins of Cancer Drug Resistance with a Single-Cell Functional Genomics Approach Using Predictive Cell Dynamics. Cell Syst. 2020 Oct 21;11(4):367-374.

• Mathematical modeling and analysis of a signaling network, by application of different techniques.
•  Numerical simulations and analysis of the results, combining machine learning strategies for data analysis with methods for simulation of ordinary differential equations.
•  Writing scientific papers on the results and their communication at the main conferences in the area.

• Some experience on analysis and simulation of ordinary differential equations
• Good experience using software such as Matlab, Scilab, Python, or equivalent
• Some experience with or willingness to be using and exploring machine learning algorithms.

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage