Context: Gaining an understanding of the cellular processes underlying bacterial growth is crucial for fundamental research in biology as well as for applications in biotechnology, health, and environmental technology. New experimental technologies have been developed to monitor growth and gene expression at the single-cell level, opening the path to the exploration of the origins of variability in growth phenotypes within a population of bacterial cells. So far, the data obtained from these technological breakthroughs have been exploited only in part. In particular, appropriate mathematical models and methods to relate single-cell gene expression data with the emergence of growth variability in a population are rare [1].
The ARBOREAL ANR project aims at developing a new mathematical framework for the analysis of growth variability from single-cell data, by combining structured branching processes [2, 3] with models of bacterial growth [4] at the single-cell level. We will obtain a new class of stochastic individual-based models, called Branching Resource allocation Processes (BRP), that will enable investigation of the variability of growth phenotypes in a proliferating microbial population in terms of the variability of physiological and cell division processes. The development of the BRP framework will entail modelling, analysis, and inference, and will exploit microfluidics experiments comprising single-cell measurements of growth and expression levels of ribosomes and enzymes in the model organism Escherichia coli.
Objective: The objective of this internship is to develop a numerical framework for the simulation of BRPs, and to use this framework to perform the first numerical explorations to investigate the onset of phenotypic variability in different BRPs. Leveraging microfluidics data from our team [5], we will compare the (non-asymptotic and asymptotic) behaviour of BRPs to the behaviour of E. coli growing in periodically changing environments. Funded by project ARBOREAL, a PhD position will open on a relevant subject. Successful candidates may be invited on the position.
Mission and activities:
- Define a numerical format for the specification of BRP models.
- Extend existing tools for structured branching processes simulation, and develop new numerical methods for the individual-based simulation of BRPs and for the solution of associated population PDEs.
- For different BRP models, quantify impact of stochasticity in non-asymptotic behaviour and characterize the dependence of phenotypic variability on the different BRP building blocks.
- Familiarize with a dataset obtained through microfluidic experiments.
- Define and extract key quantities from the dataset to check the biological relevance of BRPs models through simulations.
Expected skills and knowledge:
- Knowledge in dynamical systems, stochastic processes, modelisation and mathematical analysis
- Programming language: Julia or Python
- Interest in biological applications and biological data analysis
- Aptitude for teamwork
- Good level of technical and scientific English, both spoken and written.
Additional information:
- When: start in February-March 2025 (flexible).
- How long: 6 months (flexible).
- Where: Inria Centre at the University Grenoble Alpes.
- Contacts: Aline Marguet (aline.marguet_at_inria.fr), Hidde de Jong (hidde.de-jong_at_inria.fr).
References:
- Thomas, P., G. Terradot, V. Danos, and A. Y. Weiβe, Sources, propagation and consequences of stochasticity in cellular growth. Nat Commun 9:4528, 2018.
- A. Marguet, Uniform sampling in a structured branching population, Bernoulli, 25, pp. 2649–2695, 2019.
- S. Méléard and V. Bansaye, Stochastic Models for Structured Populations: Scaling Limits and Long Time Behavior, Springer Cham, 2015.
- N. Giordano, F. Mairet, J.-L. Gouzé, J. Geiselmann, and H. de Jong, Dynamical allocation of cellular resources as an optimal control problem: Novel insights into microbial growth strategies, PLoS Comput Biol, 12, p. e1004802, 2016.
- A. Pavlou, Quantification of bacterial resource allocation in changing environments on the single-cell level, thesis, Université Grenoble Alpes, 2022.
