| Abstract: |
| Life itself is a kind of wise tautology, it is there because it can be. The matter our life consists of, as created by the evolution of the universe, just has the right elements and molecules that with their constant recombination, can take over higher-order functions, like delivering a memory established in the genetic code, or a metabolism counteracting the overall increase of entropy in the wider cosmic environment. Once memory and activity achieved by energy storage are possible, the evolution of life on this planet was possible. New higher order functions are invented by evolution, creating new complexities, like functioning, i.e. relatively stable ecosystems thriving on stable matter and energy availability. Eventually human evolution created new regulatory networks, societies with their economies. Coupling all these regulatory networks together leads eventually to the planetary system, which also can be healthy or not, like single organisms or ecosystems, depending on their stability properties. In this contribution we ask ourselves whether there are universal principles such regulatory networks must possess in order to achieve the function they are either possessing by ancient evolutionary trials, or by novel, perhaps artificial construction. We must first be able to describe such systems. Here the suggestion is to look at rule-based systems, based on reaction kinetics, as a universal approach. This will eventually include descriptions as dynamical systems, but also as stochastic processes if necessary. Next we ask whether there are theories which from the set of all possible regulatory networks can determine the ones which are stable according to some stability criterion. Note that equilibrium, or total balance, is just one such stability concept, but perhaps the most important one. Then we are asking whether, once we have a language to describe all regulatory networks, whether there is a way to automatically select a network which best fits measured data. This is the domain of data-driven modelling, at which we will look at in an overview. |
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