In vivo learning-based control of microbial populations density in bioreactors

no code implementations • 15 Dec 2023 • Sara Maria Brancato, Davide Salzano, Francesco De Lellis, Davide Fiore, Giovanni Russo, Mario di Bernardo

Our work showcases the viability of learning-based strategies for the control of cellular density in bioreactors, making a step forward toward their use for the control of the composition of microbial consortia.

External control of a genetic toggle switch via Reinforcement Learning

no code implementations • 11 Apr 2022 • Sara Maria Brancato, Francesco De Lellis, Davide Salzano, Giovanni Russo, Mario di Bernardo

We investigate the problem of using a learning-based strategy to stabilize a synthetic toggle switch via an external control approach.

reinforcement-learning Reinforcement Learning (RL)

Intermittent yet coordinated regional strategies can alleviate the COVID-19 epidemic: a network model of the Italian case

no code implementations • 15 May 2020 • Fabio Della Rossa, Davide Salzano, Anna Di Meglio, Francesco De Lellis, Marco Coraggio, Carmela Calabrese, Agostino Guarino, Ricardo Cardona, Pietro DeLellis, Davide Liuzza, Francesco Lo Iudice, Giovanni Russo, Mario di Bernardo

Using the model, we confirm the effectiveness at the regional level of the national lockdown strategy implemented so far by the Italian government to mitigate the spread of the disease and show its efficacy at the regional level.

Physics and Society Populations and Evolution 93C10, 92D30, 92D25 J.2