The scientific research on the idea of self-organization in the biological domain comes from a long and rich philosophical tradition, going back at least to Kant, who claimed that biological systems should be understood as “natural purposes” (Naturzwecke), i.e. entities whose parts are reciprocally causes and effects of each other, such that the whole system can be conceived as organized by itself, self-organized.
In recent times, investigations on self-organisation in relation to biological systems were initially undertaken by Cybernetics (and specifically “second” Cybernetics) and then, starting from the 60’, by a number of theoretical and formal models in the field of Theoretical Biology. Since then, the idea of self-organization has been progressively broadened and applied to a wide range of physical and chemical phenomena (as dissipative structures in far-from-equilibrium conditions), contributing to make it a legitimate scientific concept. At present, although there is an increasing agreement in Biology on the fact that self-organization does play a central role in biological phenomena, at different levels, it is also commonly recognized that it cannot capture, alone, the complexity of biological organization.
The general aim of this workshop is to focus on the scope and limits of the idea of self-organization in biological systems, in the light of the more recent scientific advances on this issue. Self-organising and self-assembling processes pervade the biological domain (e.g., autocatalytic networks, protein folding, membrane formation, chemical signalling, various aspects of morphogenesis, patterns of collective behaviour…). Yet, living organisms appear to be more than self-organization, given the complexity of its constitutive, interactive, agential and historical/evolutionary dimensions, as well as the distinctive interactions they establish across levels of organization.
The workshop will be resolutely interdisciplinary, and will bring together experts with diverse backgrounds, including philosophy, biology, physics, chemistry complexity sciences and modelling.