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Generative models versus underlying symmetries to explain biological pattern
Mathematical models play an increasingly important role in the interpretation
of biological experiments. Studies often present a model that generates the
observations, connecting hypothesized process to an observed pattern. Such
generative models confirm the plausibility of an explanation and make testable
hypotheses for further experiments. However, studies rarely consider the broad
family of alternative models that match the same observed pattern. The
symmetries that define the broad class of matching models are in fact the only
aspects of information truly revealed by observed pattern. Commonly observed
patterns derive from simple underlying symmetries. This article illustrates the
problem by showing the symmetry associated with the observed rate of increase
in fitness in a constant environment. That underlying symmetry reveals how each
particular generative model defines a single example within the broad class of
matching models. Further progress on the relation between pattern and process
requires deeper consideration of the underlying symmetries
Complex Beauty
Complex systems and their underlying convoluted networks are ubiquitous, all
we need is an eye for them. They pose problems of organized complexity which
cannot be approached with a reductionist method. Complexity science and its
emergent sister network science both come to grips with the inherent complexity
of complex systems with an holistic strategy. The relevance of complexity,
however, transcends the sciences. Complex systems and networks are the focal
point of a philosophical, cultural and artistic turn of our tightly
interrelated and interdependent postmodern society. Here I take a different,
aesthetic perspective on complexity. I argue that complex systems can be
beautiful and can the object of artification - the neologism refers to
processes in which something that is not regarded as art in the traditional
sense of the word is changed into art. Complex systems and networks are
powerful sources of inspiration for the generative designer, for the artful
data visualizer, as well as for the traditional artist. I finally discuss the
benefits of a cross-fertilization between science and art
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