PhD Position F/M Stochastic modeling of single-cell plasmid copy number fluctuations

The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris .

The centre has 40 project teams , 32 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris; Its activities occupy over 600 people, scientists and research and innovation support staff, including 44 different nationalities.

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 but we are generally looking for young scientists at all career stages (internship, PhD, postdoc) to join the team at any time in 2025.

The concrete goal of this position is to develop stochastic models of plasmid copy number fluctuations inside single cells and to couple these models to the dynamics of growing populations and microbial consortia. Finally, statistical methods to infer parameters of these models from experimental data will be developed and applied to data of our collaborators.

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

Candidates for the position should have a degree in a theoretical field, such as mathematics, physics, computer science, control engineering or similar, and be capable of using methods from these fields to study dynamical systems and stochastic processes in applications. Specific experience with either continuous-time Markov chains, stochastic differential equations, stochastic chemical kinetics is a plus. 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. Candidates who expect to finish their studies in the near future are also encouraged to apply.

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

2200€ gross/month