The Laboratory of Interdisciplinary Physics (co-PI Samir Suweis) of the Physics and Astronomy Department of the University of Padova, works to understand complex living systems under a framework given by statistical physics and machine learning. Main research themes can be classified in three broad areas: (i) Ecological modelling and biodiversity ; (ii) Data analysis and complex networks in ecology and biology ; (iii) Criticality in living systems.

Our group is interested in the regulatory mechanisms that are involved in the coordination of gene expression and DNA replication with cell growth rate and division. We use a quantitative biology approach based on the choice of specific reporter systems to measure the activity of the transcription, translation and DNA replication processes. We obtain measurements at the single cell level to uncover the dynamics of the cell cycle and of cellular adaptation to environmental changes and stress in a heterogeneous population.

We are a small, dynamic group at the Quantitative Life Sciences section of the Abdus Salam Centre for Theoretical Physics. Our main goal is to explore and understand how diversity is generated, organized, and mantained in ecological communities. Most of our focus is on microbial communities, which we study with a deep and sincere synergy between theory-driven and data-driven approaches. By unveiling quantitative regularities in complex ecological data and by advancing the theoretical understanding of large interacting communities, we aim to determine how the ecological and evolutionary forces produce the stunning taxonomic and functional diversity that we observe on planet Earth.

We are fascinated by how evolution shapes the interactions at the molecular and single-cell scale to give rise to collective behaviors at the population scale, with particular focus on the feedback between spatial dynamic, stochastic fluctuations and evolution. To probe these phenomena, we use two laboratory model systems, B. subtilis biofilms and phage-E. coli ecosystems, whose population and evolutionary dynamic we model borrowing from the field of statistical physics and population genetics. 

Imagine you are immersed in water and you want to reach a target. You dont see it, but you smell its odor and your skin senses the water currents it generates as it moves. How do you process the complex chemical and mechanical signals carried and distorted by the water flow? We use fluid dynamics, statistical physics, machine learning and behavioral experiments to ask how organisms sense and process complex sensory cues to navigate despite uncertainty in the environment.