Estimating Active Information in Adaptive Mutagenesis
by Jonathan Bartlett, The Blyth Institute
Phone: (918) 307-8364 Fax: (918) 592-0997 Address: 4208 W San Antonio, Broken Arrow, OK 74012 Email: jonathan.bartlett@blythinstitute.org
One of the key tasks of creation biology is to understand the logic of creation itself - to comprehend the design of the Creator.  Functional adaptation and biological imperfection are two aspects of the created order of life for which any theory of creation must address (Wood and Murray, 2003).  One key issue in addressing these topics is differentiating between genetic changes which are functional adaptations and those which are a result of the curse.  Bartlett (2009) introduced a second-order mutational classification scheme to aid in such differentiation, noting that fitness measurements alone are insufficient to determine if a mutational mechanism is consistent or inconsistent with the overall design of the organism.  While many criteria in the classification scheme are measurable, mutational mechanism was not given a quantitative measurement.  Here we will use the Active Information metric introduced by Dembski and Marks (2009) to try to measure the mutational mechanism criteria quantitatively.
Active Information is a metric originally developed for computer search procedures which measures the amount of information that a search procedure contributes to finding an appropriate target.  A random search with uniform probability has no Active Information, a search which consistently leads away from its target has negative Active Information, and a search which consistently leads towards its target has positive Active Information.  Active Information is measured by comparing the probabilities of a search hitting its target with the probability of a random search hitting the same targets.  The difference in probability is expressed in bits, computed by log2(1/p).
Historically, Active Information has only been applied to computer-based evolutionary algorithms.  However, it can also be applied to biological systems.  Applying Active Information to the Somatic Hypermutation (SMH) process for refining binding sites during an immune response makes an excellent test case for using this concept biologically.  Because SMH primarily works by restricting the physical range of base pairs which it mutates, it simplifies the Active Information calculations.  One can simply subtract log2(SMH mutation space) from log2(whole genome search space) and estimate that the SMH process contributes approximately 22 bits of Active Information to the search.  Additional factors can complicate this estimate, such as taking into account the number of mutations required for hitting the target.
This is a surprise to some, because the SMH process uses a sort of “shotgun” approach to generating novelty.  But the point of active information is that, even though it is a shotgun approach, the changes are limited only to one gene, rather than spread out across the whole genome.  The active information is the amount of restriction that the process applies which contributes to the likelihood of finding a target.  Searches that are more highly focused than SMH would have more active information, and those that are less focused would have less.  
The same idea can be applied to other mutational processes, such as the SOS response system in bacteria.  However, because in many of these systems the mutations are spaced throughout the genome, it is more difficult to calculate the Active Information involved.
Mutational processes which have zero or negative Active Information are probably not part of an organism’s design, while processes having large positive amounts of Active Information probably are.  In addition, comparing the amounts of Active Information in different types of mutational processes may help lead to a general theory of functional adaptation in creation biology.
Bibliography
	•	Bartlett, J. L.  2009.  Towards a Creationary Classification of Mutations.  Answers Research Journal 2:169-174.
	•	Dembski, W. A. and R. J. Marks.  2009.  Conservation of Information in Search: Measuring the Cost of Success.  IEEE Transactions on Systems, Man and Cybernetics A, Systems & Humans 5(5):1051-1061.
	•	Papavasiliou, F. N. and D. G. Schatz.  2002.  Somatic Hypermutation of Immunoglobulin Genes:  Merging Mechanisms for Genetic Diversity.  Cell 109(2, Supplement 1) 1:S35-S44.
	•	Wood, T. C. and M. J. Murray. 2003.  Understanding the Pattern of Life: Origins and Organization of the Species.  Broadman and Holman, Nashville.