In the literature many authors argue that most (if not all) genetic networks are scale-free. Is this argument a myth or are the networks all truly scale-free? You're suggestions will be very much appreciated.
Determining whether a particular network is scale-free is difficult. The link below seems to be the most widely cited method of identifying a power-law distribution in empirical data.
There is a breadth of empirical research indicating that features of a power-law distribution not only exist, but are commonplace in biological systems. How well any one particular system fits a power-law is often hotly debated.
It boils down to definitions: a scale-free network, one that follows a power-law distribution, must be infinitely large by definition. Biological networks are not infinite and therefore cannot be "truly" scale-free. That being said, it is not uncommon to find a biological network that fits a power-law distribution over a limited range.
http://epubs.siam.org/doi/abs/10.1137/070710111
I agree with Ellsworth in that illogical systems are not infinite. That is true, and the argument is solid.
However, I've been working on the capability of information processing in the living systems. I think that scale-free networks can be observed if we take into consideration the very information processing; f.i., the process of learning and adaptation, or also the process of memory and gaining new information. A power law can be identified therein.
Would you agree with that?