My introduction to the role of intelligent design (ID) detection in science was a student job in the summer of 1978 working for National Defense Research. My assignment was to write software that could detect Soviet submarines amid the natural background noise of the ocean. I successfully completed the project by utilizing, among other things, a fast Fourier transform applied to the signals from underwater acoustic microphones. The otherwise hidden target signal stood out like a sore thumb.

After graduating with degrees in physics and in mechanical engineering, I spent a few years working as a test engineer, helping to coordinate the build and test of experimental aircraft engines for a major company. During that time, I had the opportunity to interact with different departments, including Design. I could see how a new model of aircraft engine emerged, beginning with an idea in an intelligent mind, which was then encoded into prescriptive information and culminated in a novel, fully functional turbine engine. It was ID in action.

In science, it is essential to define what one is talking about when referring to ID. In my own view it can be defined as follows:

Intelligent Design: an effect that requires an intelligent mind to produce

Examples include the unique acoustic signature of a submarine, a smartphone, the fitness function of a well-designed genetic algorithm, and an artificial protein.

I was invited to consider doing a PhD in Bioinformatics at the University of Guelph. On the condition that my research would focus on the role of information encoded in biopolymers, I applied and was accepted into the Biophysics Masters program with the understanding that if my academic performance was “outstanding” (their term — and I mention this only to protect the academic integrity of my alma mater), I might be permitted to transfer into a PhD program after the first year. Twelve months later the Biophysics department gave the go-ahead to complete a PhD program.

The first project was to develop a method to measure the minimum level of functional information required to code for a protein family. The results were published, and I then moved on to my first major ID-inspired project. As a result of my experience in testing experimental aircraft engines I observed that an intelligent designer usually specifies a higher degree of information for interdependent components. What if I applied this ID observation to the functional information encoding the 3D structure of globular proteins? My prediction was that it should reveal interdependencies within the primary structure. This, in turn, would reveal key sub-molecular structural components within the larger 3D structure.

To test this hypothesis and prediction, I ran a large multiple sequence alignment for ubiquitin through a pattern discovery program that searched for patterns of high information content between sites in the sequence, and clustered the sites accordingly in a nested hierarchy. I was thrilled when the results revealed a cluster tree that predicted important structural interdependencies as well as key binding areas and possible stages of the folding sequence. Comparison with the known 3D structure for ubiquitin, together with published research on binding areas and unfolding experiments, confirmed that the results were meaningful. These were published in a subsequent paper. This approach could give rise to an enormous ID-inspired research program that would give further understanding into protein structure and folding, should someone decide to pursue it.

I have observed three ways in which ID is firmly entrenched in science:

Design Application: The application of intelligence to first principles of physics to produce a desired effect. (e.g., artificial proteins)

Design Derivation: Beginning with a complex effect, the process of reverse engineering back to first principles in an effort to understand the design and how it works (e.g., genetics is probably the largest reverse engineering/ID project that science has embarked upon).

Design Detection: The analysis of an effect to determine if it requires an intelligent mind (e.g., marking and identification of artificial genomes, proteins and designer drugs, as well as forensic science, archeology, decryption and SETI).

The application of these three areas gives rise to a number of predictions in science, which I will deal with in other posts.

The role of ID in science continues to intrigue me, especially design detection. Over the years, I noticed that wherever there were statistically significant levels of functional information, there was an intelligent mind not far away.

How does one develop a method in science to detect when ID is required? I have observed that different disciplines in science use a variety of approaches. However, I realized there was a hypothesis that could provide a common solution to all design detection problems. Despite the variety of approaches used in forensic science, archeology, SETI, and biology, the core of each method could be reduced to just one, generally assumed hypothesis, which I call the central hypothesis:

Central hypothesis: A unique attribute of an intelligent mind is the ability to produce statistically significant levels of functional information (I_f).

This hypothesis is testable, falsifiable, verifiable, and provides the basis for a scientific method for design detection as follows:

Step One: Measure the minimum level of I_f required to produce the effect.

Step Two: Determine if the level of I_f is statistically significant.

Step Three: If I_f is statistically significant, the artifact/signal/effect tests positive for ID.

In the future, I hope to discuss an application of this method for design detection in science, together with its implication for the protein families of biology.

Image credit: Kirk Durston using MacPyMol.

Cross-posted at SQyBLu/Contemplations.