Infering plasticity mechanisms in cancer cell population

Updated: 2 months ago
Job Type: FullTime
Deadline: 30 Jun 2024

24 Mar 2024
Job Information

phlam laboratory
Research Field

Computer science
Researcher Profile

First Stage Researcher (R1)

Application Deadline

30 Jun 2024 - 23:00 (UTC)
Type of Contract

To be defined
Job Status

Hours Per Week

To be defined
Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme
Is the Job related to staff position within a Research Infrastructure?


Offer Description

The development and regeneration of tissues result from a complex orchestration of cellular processes such as proliferation, differentiation, motility, and intercellular communications. Although cancer originates from accumulation of mutations and cellular dyregulation, tumor progression is also driven by such a complex spatiotemporal dynamics of cell-fate events. For example, within a tumor, a small subset of cells, called Cancer Stem Cells (CSC), are known to play a central role to regenerate a new tumor with an heterogeneity comparable to the original one [1,2]. Our project specifically focuses on the abilities of cancer cells to reprogram into CSC and to form CSC niches favorable for the regeneration of invasive tumors. Such plasticity is presumed to contribute to resistance to therapies or metastasis [3].

The proposed project is focused on data mining and mathematical modeling but relies on existing experimental data from two kinds. One the one hand, microscopy data allow spatiotemporal tracking of breast cancer cell phenotypes to probe spatial patterns arising from cell to cell communications. On the the other hand, single cell transcriptomic profiling of phenotypes gives access to genome wide differences in expression level. The aim of the project is to build a mathematical model of regulatory networks that bridge the gap between these two experimental approaches.

A first challenge lies in, from spatiotemporal data, infering a mathematical model that describes the main determinants of phenotypic plasticity, distinguishing contributions from intracellular signaling, intercellular signaling, and stochastic processes. This approach will be based on the framework of nonlinear stochastic modeling such as generalized Langevin equations [4,5]. Such framework will allow to characterize the key property of cell-cell interaction (juxtacrine/paracrine, inhibition/induction) involved respectively in the patterning process of stem cell niches and the homeostasis process of cell-type proportion maintenance [6,9]. A second challenge is to use transcriptomic profiling data to identify master regulators of phenotypes. Such data will be used to map the built mathematical model onto putative regulatory networks. This will allow to propose new experiments to test hypotheses. New experiments will be designed and carried by biologists of the consortium.

The ultimate goal is to refine models of breast CSC plasticity and, more broadly, models of the spatiotemporal dynamics of tumor tissues. Our aim is to provide system level understanding of CSC platicity in order to propose new therapeutical strategies.


Expected profile, environment and application procedure

Applicants should have a strong background in physics, applied mathematics or data science with a strong interest for computational/quantitative/systems biology. The proposed project will essentially consist to gain information from multidimenional and complex time series dataset and organize it into biologically-relevant and predictive models.

This interdisciplinary project will mobilize complementary tools and expertise involving two laboratories of Lille university (PhLAM and Canther).

Lille is a human-sized city located in the north of France. It has a rich cultural scene, with numerous museums, theaters, and music venues. The city’s architecture is a blend of Flemish, Renaissance, and French styles, reflecting its history and influences. Lille is well-connected by public transportation, with an extensive metro and tram network making it easy to navigate the city. Additionally, the city is a major transportation hub, with a high-speed train (TGV) station linking it to Paris (55min), Brussels (30min), London (90min) and other major European cities.

PhD studentships are fully funded for 3 years for a net salary about 18-20KE/year. For further details on the project and to apply please contact Benjamin Pfeuty ([email protected] ) and Francois Anquez ([email protected] ) including a letter of motivation, a Curriculum Vitae and two recommendation letters. Contact us as soon as possible to ensure sufficient time to prepare for the official doctoral school application procedure (starting in may with a decision made end of june).


[1] Battle, Clevers. Cancer stem cells revisited. Nature Medecine, 2017 23(10):1124-1134.

[2] Stingl, Caldas. Molecular heterogeneity of breast carcinomas and the cancer stem cell hypothesis. Nature Reviews Cancer, 2007 7 :791–799

[3] Jain et al. Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity . Seminars in Cancer, 2023 Nov:96:48-63.

[4] Bidan et al. Transcriptomic Analysis of Breast Cancer Stem Cells and Development of a pALDH1A1:mNeptune Reporter System for Live Tracking . Proteomics . 2019 19(21-22):e1800454.

[5] Frishman and Ronceray. Learning Force Fields from Stochastic Trajectories. Phys Rev X 2020; 10, 021009.

[6] Callaham et al . Nonlinear stochastic modelling with Langevin regression. Proc Math Phys Eng Sci. 2021 477(2250):20210092.

[7] Pfeuty, Kaneko. Requirements for efficient cell-type proportioning: regulatory timescales, stochasticity and lateral inhibition. Physical Biology, 2016, 13 :026007.

[8] Beretta, E., Capasso, V., & Morozova, N. (2012). Mathematical modelling of cancer stem cells population behavior. Math. Model. Nat. Phenom., 7(1), 279-305.

[9] Olmeda F, Ben Amar M. Clonal pattern dynamics in tumor: the concept of cancer stem cells. Sci Rep. 2019 ; 9(1):15607. doi: 10.1038/s41598-019-51575-1.

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phlam laboratory

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