Many biological systems are `composite' systems, built by several sub-units interacting with each other through often complex coordination networks. A crucial non trivial property is the way these systems are able to collectively respond to external perturbations or stimuli and to environmental changes. In this project we will use methods and approaches from statistical physics to investigate response behavior in a variety of biological systems, and to better understand the role of interactions, adaptivity, out of equilibrium features and disorder.
Response behavior is a key property of a living system. Understanding how to describe it, and what are the important factors determining it is however a highly demanding task because such systems are i) interacting; ii) complex; iii) in some cases out of equilibrium.
A theoretical analysis is still in most biological contexts underway, and this is why our project is novel, of potentially large impact, and future repercussions on both theoretical developments and applications.
The general problem of quantifying the role out of equilibrium properties in response behavior is still very open. In equilibrium systems there are important relationships between the system in the unperturbed state and the way iot reacts to stimuli (the so-called fluctuation-dissipation relationships - FDT)
On the contrary, there are very few analysis of FDT relations in biological active matter, limited to theoretical computations in very simple models and with basically no experimental results on collectives of interacting biological units. Potentially interested cases range from filament networks, cell tissues, bacterial clusters, up to animal groups. In all these cases the contribution of our research can provide important information on the efficiency with which the system exploits energy to achieve a specific biological function through a collective dynamics.
In the context of cell response behavior, issues related to the stress -response strategies in the use of metabolic pathways are crucial in many contexts (among others Warburg and Crabtree effects) and can be crucial to gather important information about tumor growth and development. Proteome re-allocation can imply significant biosynthetic costs.