We aim to study the steady-state cell size distribution of a population of E. coli cells, integrating information collected at the individual scale. To that extent, we propose a stochastic individual-based dynamic model which can be calibrated using temporal single-cell lineage data acquired via micro˛uidic techniques. In particular, this data also grants access to the age structure, which then can be used to to provide a more precise non-Markovian characterisation of the growing population. Using probabilistic techniques, we prove the exponential convergence of the expected value of our stochastic process towards the unique stationary distribution, which can also be observed in real time in the data. A brief heuristic idea of the sufficient criteria for convergence which were used are discussed. We finally compare the predicted distributions to empirical distributions issued from macroscopic observations to validate the proposed micro-to-macro links for healthy and perturbed bacterial populations under different growing conditions.
De Nick Barton
De Quentin Griette
De Ada Altieri
De Mete Demircigil
De Elisa Thébault
De Jean-François Arnoldi
De Michael Scott
De Florence Bansept
De Apolline Louvet
De Vincent Bansaye
De Sébastien Lion
De Philipp Messer
De Raphael Forien
De Nicolas Vauchelet
De David MacLeod
De Cheryl Mentuda
De Léo Girardin
De Coralie Fritsch
De Adrian Lam
De Diana Fusco
De Léonard Dekens
De Oana Carja
De Sepideh Mirrahimi
De Olivier Cotto
De Benoît Henry
De Himani Sachdeva
De Florian Patout
De Ignacio Madrid Canales
De Denis Roze
De Julien Berestycki
De Sylvie Ferrari
De Vasilis Dakos
