Birds, spins and entropy: a theoretical physics approach to collective animal behaviour

Flocking is a paradigmatic example of self-organized collective behaviour, where collective ordering emerges from the mutual interactions between  individuals. In this respect, it shares striking similarities with collective phenomena in inanimate systems that have long been studied by the physics community. Still, biological systems are more complicated than physical ones and it is not evident whether they can be described by the same kind of general laws so well understood in physics. Experimental findings often go beyond simple expectations, making this field even more fascinating. In this talk I will discuss our attempts to study collective animal behaviour using  a physicist's perspective. I will show how we used concepts and methods from statistical physics to make sense of experimental data of large flocks of birds. In particular, I will focus on the velocity correlation functions, that well capture the balance between consensus and independence among the large number of individuals in the group. We measured these correlations in real flocks of starlings and found that they exhibit a non-trivial scale-free behavior, indicating a surprisingly large degree of coordination and collective response. I will describe how we can use these correlations to systematically address the inverse problem, extract from the experimental measurements information on the underlying microscopic interactions, and build minimal (maximum entropy) models directly from the data.

The immune system defends us from pathogens. But what is an antigen? Does each lymphocyte really know the complete ensemble of all the molecules of our body? Is there, instead, a systemic cooperation among these  cells resembling neurons in the brain? Is the immune system able to "think"? And, even more interesting, what is self? Is there a sharp separation between self and not-self?
From the pioneering papers by Jerne, Varela and Couthino, the idea of a network of B-clones interacting by exchanging antibodies was formulated as an hypothetical way for explaining tolerance toward self proteins by mature peripheral B-cells (beyond clonal deletion and receptor editing during the ontogenesis). However, despite anti-antibodies are commonly encountered,  evidence in favor of an extensive network is still lacking and even worse, always sharper experiments (e.g. the works by Goodnow) highlighted strong evidence of a phenomenon called "anergy": the latter shifts the responsibility of this lacking of attack by B-cells self-directed to a lacking of signalling by helpers cells (the second key needed for B-cell activation beyond the primary antigenic target, self or not-self): In a nutshell, B-cells need two activation signals, the former being the target (antigen) the latter being a "consensus" by the helpers. Helpers do not allow this consensus and the resulting B-cell with only one signal undergoes into a regime of "anergy". But how can helpers recognize those B-cells self-directed? The problem seems only shifted...  Coupled to a large introduction to theoretical immunology and its formalization through statistical mechanics, we try to revise this discussion within a thermodynamical framework by using the tools of disordered systems.

As we learnt from Young and Laplace, the cohesion of fluids makes them choose specific shapes, in particular spheres at a small scale. We discuss several ways to maintain this ideal shape on a solid, which leads to unique dynamical situations: water pearls do not stick, they run easily and they bounce - a little bit as if they were solid marbles. (But they are not. And the liquid nature of these pearls has interesting consequences on the dynamical shapes they adopt, for example.) The high mobility of liquid pearls implies that tiny forces are sufficient to move them, and we plan to present recent achievements where some asymmetric patterns at a solid surface permit the self-propulsion of the liquid. We would also like to discuss the effects of various fields to control these elusive drops.

Precise measurements of ground deformation have become increasingly common as large networks of GPS receivers and borehole strainmeters have been established over the last decade. Complementing this continuous record are comparatively infrequent but spatially dense images of ground deformation from radar satellites, and a long historical record of leveling, Electronic Distance Meter, triangulation, and tilt data. Deformation can arise from tectonic and volcanic forces and from human activities such as aquifer withdrawal or geothermal exploitation. Mathematical models of how the crust deforms in response to different physical processes are required to characterize driving processes and constrain source location, size, orientation, and strength. This information is valuable for hazards forecasting and mitigation.