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Introduction to QuantLib Development - Intensive 3-day Training Course - September 10-12th, 2018 - Download Registration Form Here

 

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CCS2018 Satellite. ReACT#3: Robustness, Adaptability and Critical Transitions in Living Systems #3.

Thu, 05 Jul 2018 05:48:51 GMT

Deadline 28/06. Register your abstract soon! 
 
 
After the success of the Satellite at ECCS 2014 and CCS 2016, we would like to propose a follow-up Satellite always focusing on Robustness, Adaptability and Critical Transitions in Living Systems 

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 thermo-dynamical 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. 

In a statistical-mechanics approach, it is fundamental to determine the order parameter, which characterizes the different system phases. This is a crucial step to obtain the key ingredients needed to formulate a modeling framework, so as to obtain a better understanding of the system’s macroscopic behaviour. However, the understating 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 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. 

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. 

Relevance to the conference main tracks 

1. Foundations of Complex Systems* (Complex networks, self-organization, nonlinear dynamics, statistical physics, mathematical modeling, simulation) 

2. Biological Complexity* (Biological networks, system biology, evolution, natural science, medicine and physiology)