|
by I.G.D. StrachanAbstract—In this paper we assess the validity of the evolutionary simulation described in the paper “Evolution of Biological Information” (Schneider, 2000), which, it is claimed, demonstrates the evolution of new biological information from scratch with no external intervention, as a set of characteristic patterns developed in nucleotide binding sites. The further claim is made that the amount of information that evolves, a quantity designated Rsequence is approximately equal to the amount that is needed to locate the binding sites, given the number of them on the genome. This quantity is designated Rfrequency We find both these claims to be flawed, firstly that information arises from scratch, when in fact all the simulation is demonstrating is a form of “supervised learning” of a fixed target by a simple form of neural network. Secondly, we demonstrate that for the neural network classifier used in the simulation, that Rsequence & Rfrequency except for the unrealistic exceptional case where all the information is confined to one axis (one base in the binding site). We also show that the result showing Rsequence ≈ Rfrequency reported in the Schneider paper is a coincidence, resulting from an incorrect evaluation of the correction for small sample sizes, which fails to take into account the fact that the standard formula for uncertainty (or entropy) is a limiting case that utilizes Stirling's approximation for ln N!, which is not valid for small values of N. Analytical results are backed up by simulations, including an extensive set of runs using Schneider's Pascal program ev.p. Reference: Schneider, T. D. (2000). Evolution of biological information. Nucleic Acids Res., 28:2794- 2799. http://www.lecb.ncifcrf.gov/~toms/paper/ev/. The full paper is
available below: |
