After the success of the Satellite at ECCS 2014, CCS 2016, and CCS 2018, the ReACT Satellite: Robustness, Adaptability and Critical Transitions in Living Systems is back for a fourth edition at CCS 2021 in Lyon, France. We are very pleased to announce that the Satellite will be host in an hybrid format, welcoming participants both online and in person! The Satellite will last half a day and will take place during the main conference (25th - 29th October, 2021). The exact date and times will be announced soon.

Living systems are characterized by the recurrent emergence of patterns: power-laws distributions, long-range correlations and structured self-organization in living matter are the norm, rather than the exception. All these features are also typical of systems poised near a critical point. The great lesson from Physics is that criticality can emerge as a collective behaviour in a many-body system with simple interactions and its characteristics depend only on few details, like the dimensionality or the symmetries.

One of the most striking feature of living systems is that they are structured as evolving systems were interactions can turn-on or off, as well as strengthening and weakening, reconfiguring the system connectivity. Thus, by rearranging both the structural and functional topology, living interacting systems may demonstrate unique evolvability, scalability and adaptability properties.

Among these systems, the brain is probably one of the most impressively complex and has received a considerable amount of attention in recent years, aided by the vast amount of experimental data available. The idea that the collective behavior of neurons might emerge from a self-organized critical state has been widely studied, from its fundamental mechanisms to its functional advantages, and it poses a fascinating question that is far from being answered.

It is of crucial importance to make further steps in the understanding of the main properties that simultaneously confer to these systems high level of adaptability, robustness and optimality. If we can “learn” from evolution and unravel the basic principles then we would be able to both better manage/supervise these systems and also design more optimal and sustainable new systems.