Our student Bernat Casajuana presented a poster at the 3rd Biology for Physics Conference, held September 6 to 10, 2026, at the Barcelona Biomedical Research Park, under the theme “Energy and Information in Living Matter.” The conference combined invited talks, contributed talks, and poster sessions, with a particular focus on connecting early-career researchers with established leaders in the field.
The conference explored living systems through nonequilibrium statistical physics, focusing on how biological function depends on energy dissipation and information processing far from equilibrium. Core concepts included entropy production as a signature of irreversibility, thermodynamic and information-theoretic limits on cellular sensing and signaling, and methods for inferring physical laws directly from noisy experimental data.
That last topic is close to what Bernat has been working on with physics-informed neural networks (PINNs), where instead of learning the physical laws from data, the laws are already known and are used to guide a machine-learning model for parameter recovery in the Repressilator, a small synthetic gene network.
Work carried out with Roger Casals-Franch, Adrián López García de Lomana, Pere Martí-Puig, and Jordi Villà-Freixa (IRIS-CC · UVic-UCC · University of Iceland).
Bernat had presented the project a few days earlier, as a talk, at MDAI 2026 in Vic.