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PhD Student : Mathematical modeling of the evolutionary stabilization of multicellularity in yeasts

This offer is available in the following languages:
Français - Anglais

Date Limite Candidature : jeudi 2 juin 2022

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General information

Reference : UMR5251-AGNCHE-003
Workplace : TALENCE
Date of publication : Thursday, May 12, 2022
Scientific Responsible name : Christèle ETCHEGARAY
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 October 2022
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

Scientific Context : Multicellularity is one of the most crucial innovations in life history. Its multiple appearance during Evolution attests its success. However, theoretical and experimental studies put to light several obstacles to its stabilization. Therefore, the conditions under which multicellular organisms may have been selected remain highly speculative and demand theoretical and experimental validation (Tong et al., 2022).
To this end, the team of biologists led by Bertrand Daignan-Fornier (IBGC) is driving
evolutionary competition assays between unicellular and multicellular forms of the yeast Sac-charomyces Cerevisiae. These assays follow a mixed population during its evolution in order to measure the effect of various environmental and genetic parameters on the composition of the population. In particular, the multicellular form, called snowflake, is obtained when cells do not separate after division, resulting in a tree-like structure. The experiments aim at questionning the importance of the snowflake geometry on its physical and physiological properties (spatial organization, cooperation, propagation), and thereby identifying the mechanisms providing selective advantages to multicellularity.
In this PhD project, we offer to develop mathematical models of the evolutionary dynamics of the snowflake yeast, going from an individual-level description to a population model.
The goal will be on the one hand to better understand how a snowflake grows as a function of its environment, and on the other hand to reproduce numerically the competition assays led at IBGC. To do so, we will use deterministic and/or stochastic modeling approaches (agent-based models, integrodifferential equations, stochastic processes).
Objectives of the PhD : Yeast polarization ability results in the snowflake's tree-like structure. Moreover, the growing snowflake may become unstable and fragment. The first goal of the PhD project will be to characterize the structure and growth of a snowflake. For that purpose, we will use the framework of agent-based models, so that each cells and each division and death events are described. Such models are already used to capture blood vessels formation from growth and branching (Aceves-Sanchez et al., 2021), but they do not take fragmentation into account. We will therefore consider and compare several fragmentation laws. Moreover, we
will investigate the effect of features such as cell number, cell volume and cell cycle duration on the snowflake's dynamics. Using a convenient numerical discretization scheme, we will perform numerical simulations that will be compared to the literature and discussed with the biologists. Then, we will derive a model for the dynamics of a population of snowflakes. For that purpose, it will be necessary to formulate a simplified description of the individual dynamics, starting from the first model. We will then reach a population-level description that still captures individual features of interest. Several modeling approaches will be investigated in deterministic and stochastic settings (integrodifferential equations, structured population processes), e.g in the spirit of Barlukova et al. (2018). We will perform the mathematical analysis of the model, and formulate a coupled system to describe the interaction with the
unicellular population. Finally, we will calibrate the competition model with the experimental data provided by IBGC. To do so, we expect the recruited candidate to take part in the experimental design with the biologists, so that the developed models are consistent with the measured features. The calibration will be based on an optimization problem, and will require driving efficient numerical simulations.
Skills : The recruited person will have skills in mathematical modeling and analysis, with an interest in biology and interdisciplinary projects. Python programming skills are also demanded.

Work Context

The recruited person will work at the Institut de Mathématiques de Bordeaux, and will be attached to the Inria Center of the University of Bordeaux in the MONC (Mathematical Modeling for Oncology) project­-team.
The Institut de Mathématiques de Bordeaux (IMB) is a joint research unit (UMR 5251), CNRS, University of Bordeaux, Bordeaux INP. The IMB is the host laboratory of the Doctoral School of Mathematics and Computer Science and groups together most of the research in mathematics on the Bordeaux site. Research at the IMB is structured around 7 teams and brings together the scientific activities of 158 researchers, 105 doctoral and post-doctoral students and 18 research support staff. The IMB is a partner of the CEA with the Labri, the I2M, the Inria Center of the University of Bordeaux, the IMS, the CEA/DAM. The laboratory is currently involved in 25 ANR projects, 3 projects of the Aquitaine Regional Council, 3 IUF and 2 European H2020 projects.
Inria is the national research institute for digital sciences and technologies. World-class research, technological innovation and entrepreneurial risk are its DNA. Within 200 project-teams, most of which are shared with major research universities, more than 3,900 researchers and engineers are exploring new avenues, often in interdisciplinarity and in collaboration with industrial partners, to meet ambitious challenges.

Additional Information

80' PhD Project

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