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Probabilistic methods for identifying signatures of selection on evolutionary trees: from data to theory and back M/F

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Français - Anglais

Date Limite Candidature : vendredi 27 mai 2022

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General information

Reference : UMR8023-ALEWAL-008
Workplace : PARIS 05
Date of publication : Friday, May 6, 2022
Scientific Responsible name : Aleksandra Walczak
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 September 2022
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

Selection is one of the main forces that shapes the genetic diversity observed in an evolving population, but characterizing its strength and patterns is still challenging. B-cell affinity maturation is an example of accelerated evolution under selection, which allows us to study these processes on shorter timescales than macroevolution. Here we will develop new methods to learn the properties of B cell selection from repertoire sequencing data, overcoming two issues: current methods do not allow us to go beyond neutrality (absence of selection) to infer evolutionary processes; we lack good summary statistics estimators to go beyond rejecting neutrality. We will build mathematical models of proliferating cell populations undergoing non neutral mutations, and use them to characterize patterns of selection in trees. Informed by those models we will develop inference schemes to learn the parameters of selection from the data. The outcome will be tools to characterize selection, and a basis for identifying responding lineages in clinical settings.

Work Context

The thesis will be carried out in the group of Aleksandra Walczak and Thierry Mora at the physics department of the ENS with the work carried out in the team of Amaury Lambert at the IBENS, which is a 3-minute walk away. The project is a multidisciplinary passion, with frequent meetings between the three teams and constant discussions.

Constraints and risks

The success of the thesis is based on fruitful interactions with biophysicists and mathematicians who will help to quantitatively analyze the data and use data-driven modeling to shed light on the process. The motivation for interdisciplinarity and quantitative biology is essential. Experience in statistical physics would be an advantage.

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