PhD Position F/M Mean-field modeling of coupled single-cell–population dynamics with intercellular communication

Context

The project is part of the ERC Starting Grant of Jakob Ruess (Lifeware team at Inria Saclay). Inria is the French national institute for research in computer science, control, and applied mathematics promoting scientific excellence and technology transfer. Within this project, we primarily work on the development of mathematical modeling approaches and statistical inference/estimation methods but we also collaborate with biologists to apply our methods to real data. Our main long-term goal is to develop a comprehensive methodological framework supporting the development of a quantitative understanding of biochemical processes inside single cells that are coupled to the dynamics of growing cell populations. Our research topics are at the intersection of mathematical biology, statistics, control engineering, and statistical physics applied to problems in biology.
This job announcement is for a PhD position on the project.

Assignment

The concrete goal of this position is to extend our exisiting modeling frameworks on coupled dynamics of single-cell and population processes with additional mechanisms that capture active cell-to-cell communication.

Main activities

Overview of the ERC Project:
Synthetic biology aims at engineering biochemical processes to supplement cells with artificial functionality. To this end, we design synthetic gene circuits that operate as dynamical systems inside cells and deploy methods from control engineering to regulate circuit functionality. A key problem in this is that biochemical processes inside single cells are inherently stochastic and create heterogeneity within cell populations that eventually leads to complex couplings between single-cell processes and population dynamics. It is thus difficult to quantitatively predict how exactly a constructed circuit will function in the context of a growing population and to design single-cell circuits such that desired dynamics emerge at the population scale.
At the single-cell scale, stochastic biochemical processes are typically represented as continuous-time Markov chains governed by a chemical master equation (Kolmogorov forward equations). We have recently started to develop a multi-scale modeling framework that augments models of single-cell processes with population scale processes such as growth and selection (Ruess et al., The Journal of Chemical Physics, 2023).
Our multi-scale modeling framework gives us unprecedented opportunities to forward-simulate coupled dynamics of stochastic single-cell and population processes, which paves the way to model, design, and dynamically control synthetic gene circuits so as to create desired functionality within growing populations. Within this project, we thus aim to focus on the development of methods for reverse engineering and controlling multi-scale models and on applying this methodology in real applications for a collection of gene circuits constructed by our collaborators.
Links to publications:
https://doi.org/10.1063/5.0160529 https://doi.org/10.1371/journal.pcbi.1009214 https://doi.org/10.1073/pnas.2114438119 https://doi.org/10.1007/s00285-023-01876-x

Skills

Candidates for the position should have a background in statistical physics and be capable of using methods from this fields to study dynamical systems and stochastic processes in applications. Expertise in biology is not required but candidates are expected to build up an understanding of our concrete applications throughout the course of the project.