Date: 25 September 2014
Location: Lucca, Italy
Duration: half a day
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 thermodynamical 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 (e.g. pairwise) interactions and its characteristics depend only on few details like the dimensionality or symmetries.
However, the understanding of biological/social systems needs more than a mere generalization of the standard statistical mechanics approach. One of the most striking feature of living systems is that they are structured as evolving systems where 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. 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 both adaptability and robustness.
If we can “learn” from evolution, then we would be able to both better manage/supervise these systems and also design more optimal and sustainable new systems.
Complex networks, self-organization, nonlinear dynamics, statistical physics, mathematical modeling, simulation
Biological networks, system biology, evolution, natural science, medicine and physiology
|14.30||Invited talk||Andreas Wagner||University of Zurich||Exaptation and the Space of Possible Metabolisms|
|15.10||Invited talk||Vasily Dakos||Estación Biológica de Doñana, Sevilla||Detecting critical transitions in complex ecological networks|
|15.50||Contributed talk||C.G. Lazaro||Tipping points for the degradation of the habitat: Irreversibility in the transition to cooperation in changing environments.|
|16.00||Contributed talk||E. Calizza||Network size and complexity explain disturbance propagation within real food webs.||16.20||Invited talk||Thierry Mora||Ecole Normale Superiore, Paris||Statistical mechanics of bird flocks|
|17.30||Invited talk||Miguel Muñoz||University of Granada||Criticality emerging out of adaptive/evolutionary processes in communities of living systems|
|18.10||Contributed talk||L. Alarcon-Ramos||The role of degree diversity in the control of discrete-time SIS epidemics in complex networks. Scale-free vs Regular Networks|
|18.20||Contributed talk||J. Ochab||Scale-free fluctuations in behaviour of sleep deficient humans|
|18.30||Contributed talk||B. Pace||Cell machinery networks: coupling gene networks and metabolism|
|18.40||Contributed talk||M. Adorisio||A spatial maximum entropy model for ecosystems|
A. Wagner Some evolutionary innovations may originate non-adaptively as exaptations, or pre-adaptations, which are by-products of other adaptive traits. Examples include feathers, which originated before they were used in flight, and lens crystallins, which are light-refracting proteins that originated as enzymes. The question of how often adaptive traits have non-adaptive origins has profound implications for evolutionary biology, but is difficult to address systematically. Here I consider this issue in metabolism, one of the most ancient and highly complex biological systems that is central to all life, and discuss recent observations suggesting that any one adaptation in a complex metabolic reaction network entails multiple potential exaptations. Metabolic systems thus contain a latent potential for evolutionary innovations with non-adaptive origins. These observations suggest that many more metabolic traits may have non-adaptive origins than is appreciated at present. More generally, they challenge our ability to distinguish adaptive from non-adaptive traits.
V. Dakos Evidence is increasing that large scale abrupt changes in ecosystems, climate, oceanic circulation patterns, or even human physiology are examples of critical transitions between different dynamical states. Theory suggests that universal properties tend to rule system dynamics prior to critical transitions regardless of differences in the underlying details of the system. When quantified, these properties may be used as leading indicators of resilience. In this talk, I will introduce these ideas by providing examples of these indicators identified in real living systems. I will discuss whether critical transitions occur in ecological networks using as an example mutualistic communities, and I will show how we can detect critical transitions using leading indicators of resilience. These approaches may help to understand and quantify system resilience in a variety of complex networks.
T. Mora It has long been suggested that animal collectives may be described by models of alignment inspired by spin systems from statistical mechanics. I will show that this correspondence can be made mathematically explicit by applying the principle of maximum entropy to field data on flocks of starling. The parameters of the model inferred from the data place it near a critical point, where the speed of birds is controlled in a self-organised manner rather than individually.
M. Muñoz Empirical evidence suggesting that living systems might operate in the vicinity of critical points, at the borderline between order and disorder, has proliferated in recent years, with examples ranging from spontaneous brain activity to flock dynamics. However, a well-founded theory for understanding how and why interact- ing living systems could dynamically tune themselves to be poised in the vicinity of a critical point is lacking. Here we use tools from statistical mechanics and information theory to show that complex adaptive or evolutionary systems can be much more efficient in coping with diverse heterogeneous environmental conditions when operating at criticality. Analytical as well as computational evolutionary and adaptive models vividly illustrate that a community of such systems dynamically self-tunes close to a critical state as the complexity of the environment increases while they re- main noncritical for simple and predictable environments. A more robust convergence to criticality emerges in coevolutionary and coadaptive setups in which individuals aim to represent other agents in the community with fidelity, thereby creating a collective critical ensemble and providing the best possible tradeoff be- tween accuracy and flexibility. Our approach provides a parsimonious and general mechanism for the emergence of critical-like behavior in living systems needing to cope with complex environments or trying to efficiently coordinate themselves as an ensemble.
Department of Physics and Astronomy, University of Padova, Italy
Department of Engineering Mathematics, University of Bristol, UK
Department of Applied Maths, University of Leeds, UK
Sponseored by the Department of Physics and Astronomy of the University of Padova