Gpuccio: Functional Information Methodology

[gpuccio]

@Art

Second, “one attempt” and “random walk” are not the same thing and will produce very different probabilities if finding a feature with a given FI.

For what its worth, here is how I visualize this contrast. The “one attempt” approach yields a value that is a sort of state function (like entropy in thermodynamics). The “random walk” approach yields values that are pathway dependent, and thus not state functions (but rather akin to work in thermodynamics). The two simply cannot be equated.

I am not equating them. In most cases, the correct model is a random walk. However, probabilities for a random search and a random walk from some unrelated state, given a high number of attempts, are similar. Many people are more familiar with the concept of a random search. I suppose that, for frameshift mutations, it’s more a random search. However, I had no intention to equate the two things.

With new FI, the model is a random walk from an unrelated state. FI expresses well the probability of finding the target. It is true that a minimum number of steps is necessary to make the target possible, but here we are discussing billions of steps, and extremely improbable targets. The initial difference is not really relevant.