Editor’s note: We have been delighted to present a series by geologist Casey Luskin on “The Positive Case for Intelligent Design.” This is the 12th and final entry in the series, a modified excerpt from the new book The Comprehensive Guide to Science and Faith: Exploring the Ultimate Questions About Life and the Cosmos. Find the full series here.
There’s a final common objection to intelligent design that the positive case for ID, outlined in this series, helps us to answer. In his Kitzmiller v. Dover testimony, biologist Kenneth Miller referred to intelligent design as a “science stopper.”1 Similarly, in his book Only a Theory, Miller stated, “The hypothesis of design is compatible with any conceivable data, makes no testable predictions, and suggests no new avenues for research. As such, it’s a literal dead end…”2
Yet as we’ve already seen, ID makes a variety of testable and successful predictions. This allows ID to serve as a paradigm guiding scientific research to make new discoveries. The list below shows various fields where ID is helping science to generate knowledge. For each field, multiple ID-friendly scientific publications are cited as examples.
How ID Inspires the Progress of Science
- Protein science: ID encourages scientists to do research to test for high levels of complex and specified information in biology in the form of the fine-tuning of protein sequences.3 This has practical implications not just for explaining biological origins, but also for engineering enzymes and anticipating and fighting the future evolution of diseases.
- Physics and cosmology: ID has inspired scientists to seek and find instances of fine-tuning of the laws and constants of physics to allow for life, leading to new fine-tuning arguments such as the Galactic Habitable Zone. This has implications for proper cosmological models of the universe, hinting at proper avenues for successful “theories of everything” that must accommodate fine-tuning, and other implications for theoretical physics.4
- Information theory: ID leads scientists to understand intelligence as a cause of biological complexity, capable of being scientifically studied, and to understand the types of information it generates.5
- Pharmacology: ID directs both experimental and theoretical research to investigate the limitations of Darwinian evolution to produce traits that require multiple mutations in order to function. This has practical implications for fighting problems like antibiotic resistance or engineering bacteria.6
- Evolutionary computation: ID produces theoretical research into the information-generative powers of Darwinian searches, leading to the discovery that the search abilities of Darwinian processes are limited, which has practical implications for the viability of using genetic algorithms to solve problems.7
- Anatomy and physiology: ID predicts function for allegedly “vestigial” organs, structures, or systems whereas evolution has made many faulty predictions of nonfunction.8
- Bioinformatics: ID has helped scientists develop proper measures of biological information, leading to concepts like complex and specified information or functional sequence complexity. This allows us to better quantify complexity and understand what features are, or are not, within the reach of Darwinian evolution.9
- Molecular machines: ID encourages scientists to reverse-engineer molecular machines — like the bacterial flagellum — to understand their function like machines, and to understand how the machine-like properties of life allow biological systems to function.10
- Cell biology: ID causes scientists to view cellular components as “designed structures rather than accidental by-products of neo-Darwinian evolution,” allowing scientists to propose testable hypotheses about cellular function and causes of cancer.11
- Systematics: ID helps scientists explain the cause of the widespread features of conflicting phylogenetic trees and “convergent evolution” by producing models where parts can be reused in non-treelike patterns.12 ID has spawned ideas about life being front-loaded with information such that it is designed to evolve, and has led scientists to expect (and now find!) previously unanticipated “out-of-place” genes in various taxa.13
- Paleontology: ID allows scientists to understand and predict patterns in the fossil record, showing explosions of biodiversity (as well as mass extinction) in the history of life.14
- Genetics: ID has inspired scientists to investigate the computer-like properties of DNA and the genome in the hopes of better understanding genetics and the origin of biological systems.15 ID has also inspired scientists to seek function for noncoding junk-DNA, allowing us to understand development and cellular biology.16
Avenues of Discovery
Critics wrongly charge that ID is just a negative argument against evolution, that ID makes no predictions, that it is a “god of the gaps” argument from ignorance, or that appealing to an intelligent cause means “giving up” or “stopping science.” As this series has shown, these charges are misguided.
Ironically, when critics claim that research is not permitted to detect design because that would stop science, it is they who hold science back by preventing scientists from investigating the scientific theory of intelligent design. When researchers are allowed to infer intelligent agency as the best explanation for information-rich structures in nature, this opens up many avenues of discovery that are bearing good fruit in the scientific community.
- Kenneth R. Miller, Kitzmiller v. Dover, Day 2 AM Testimony (September 27, 2005).
- Kenneth R. Miller, Only a Theory: Evolution and the Battle for America’s Soul (New York: Viking Penguin, 2008), 87.
- Axe, “Extreme Functional Sensitivity to Conservative Amino Acid Changes on Enzyme Exteriors”; Axe, “Estimating the Prevalence of Protein Sequences Adopting Functional Enzyme Folds”; Behe and Snoke, “Simulating Evolution by Gene Duplication of Protein Features That Require Multiple Amino Acid Residues”; Axe, “The Case Against a Darwinian Origin of Protein Folds”; Gauger and Axe, “The Evolutionary Accessibility of New Enzyme Functions: A Case Study from the Biotin Pathway”; Reeves et al., “Enzyme Families-Shared Evolutionary History or Shared Design? A Study of the GABA-Aminotransferase Family”; Thorvaldsen and Hössjer, “Using statistical methods to model the fine-tuning of molecular machines and systems.”
- Guillermo Gonzalez and Donald Brownlee, “The Galactic Habitable Zone: Galactic Chemical Evolution,” Icarus 152 (2001), 185-200; Guillermo Gonzalez, Donald Brownlee, and Peter D. Ward, “Refuges for Life in a Hostile Universe,” Scientific American (2001), 62-67; Guillermo Gonzalez and Jay Wesley Richards, The Privileged Planet: How Our Place in the Cosmos Is Designed for Discovery (Washington, DC, Regnery, 2004); Guillermo Gonzalez, “Setting the Stage for Habitable Planets,” Life 4 (2014), 34-65; D. Halsmer, J. Asper, N. Roman, and T. Todd, “The Coherence of an Engineered World,” International Journal of Design & Nature and Ecodynamics 4 (2009), 47-65.
- William A. Dembski, The Design Inference; William A. Dembski and Robert J. Marks II, “Bernoulli’s Principle of Insufficient Reason and Conservation of Information in Computer Search,” Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics(October 2009), 2647-2652; William A. Dembski and Robert J. Marks II, “The Search for a Search: Measuring the Information Cost of Higher Level Search,” Journal of Advanced Computational Intelligence and Intelligent Informatics 14 (2010), 475-486; Øyvind Albert Voie, “Biological function and the genetic code are interdependent,” Chaos, Solitons and Fractals 28 (2006), 1000-1004; McIntosh, “Information and Entropy —Top-Down or Bottom-Up Development in Living Systems?”
- Behe and Snoke, “Simulating evolution by gene duplication of protein features that require multiple amino acid residues”; Ann K. Gauger, Stephanie Ebnet, Pamela F. Fahey, and Ralph Seelke, “Reductive Evolution Can Prevent Populations from Taking Simple Adaptive Paths to High Fitness,” BIO-Complexity 2010 (2).
- William A. Dembski and Robert J. Marks II, “Conservation of Information in Search: Measuring the Cost of Success,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 39 (September 2009), 1051-1061; Winston Ewert, William A. Dembski, and Robert J. Marks II, “Evolutionary Synthesis of Nand Logic: Dissecting a Digital Organism,” Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics (October 2009); Dembski and Marks, “Bernoulli’s Principle of Insufficient Reason and Conservation of Information in Computer Search”; Winston Ewert, George Montanez, William Dembski and Robert J. Marks II, “Efficient Per Query Information Extraction from a Hamming Oracle,” 42nd South Eastern Symposium on System Theory (March 2010), 290-297; Douglas D. Axe, Brendan W. Dixon, and Philip Lu, “Stylus: A System for Evolutionary Experimentation Based on a Protein/Proteome Model with Non-Arbitrary Functional Constraints,” Plos One 3 (June 2008), e2246.
- Jonathan Wells, “Using Intelligent Design Theory to Guide Scientific Research”; William Dembski and Jonathan Wells, The Design of Life: Discovering Signs of Intelligence in Living Systems (Dallas, TX: Foundation for Thought and Ethics, 2008).
- Meyer, “The origin of biological information and the higher taxonomic categories”; Kirk K. Durston, David K.Y. Chiu, David L. Abel, Jack T. Trevors, “Measuring the functional sequence complexity of proteins,” Theoretical Biology and Medical Modelling 4 (2007), 47; David K.Y. Chiu and Thomas W.H. Lui, “Integrated Use of Multiple Interdependent Patterns for Biomolecular Sequence Analysis,” International Journal of Fuzzy Systems4 (September 2002), 766-775.
- Minnich and Meyer. “Genetic Analysis of Coordinate Flagellar and Type III Regulatory Circuits in Pathogenic Bacteria”; McIntosh, “Information and Entropy—Top-Down or Bottom-Up Development in Living Systems?”
- Jonathan Wells, “Do Centrioles Generate a Polar Ejection Force?,” Rivista di Biologia / Biology Forum, 98 (2005), 71-96; Scott A. Minnich and Stephen C. Meyer, “Genetic analysis of coordinate flagellar and type III regulatory circuits in pathogenic bacteria,” Proceedings of the Second International Conference on Design & Nature Rhodes Greece (2004); Behe, Darwin’s Black Box; Lönnig, “Dynamic genomes, morphological stasis, and the origin of irreducible complexity.”
- Lönnig, “Dynamic genomes, morphological stasis, and the origin of irreducible complexity”; Nelson and Jonathan Wells, “Homology in Biology”; Ewert, “The Dependency Graph of Life”; John A. Davison, “A Prescribed Evolutionary Hypothesis,” Rivista di Biologia/Biology Forum 98 (2005), 155-166; Ewert, “The Dependency Graph of Life.”
- Sherman, “Universal Genome in the Origin of Metazoa: Thoughts About Evolution”; Albert D.G. de Roos, “Origins of introns based on the definition of exon modules and their conserved interfaces,” Bioinformatics 21 (2005), 2-9; Albert D.G. de Roos, “Conserved intron positions in ancient protein modules,” Biology Direct 2 (2007), 7; Albert D.G. de Roos, “The Origin of the Eukaryotic Cell Based on Conservation of Existing Interfaces,” Artificial Life 12 (2006), 513-523.
- Meyer et al., “The Cambrian Explosion: Biology’s Big Bang”; Meyer, “The Cambrian Information Explosion”; Meyer, “The origin of biological information and the higher taxonomic categories”; Lönnig, “Dynamic genomes, morphological stasis, and the origin of irreducible complexity.”
- Richard v. Sternberg, “DNA Codes and Information: Formal Structures and Relational Causes,” Acta Biotheoretica 56 (September 2008), 205-232; Voie, “Biological function and the genetic code are interdependent”; David L. Abel and Jack T. Trevors, “Self-organization vs. self-ordering events in life-origin models,” Physics of Life Reviews 3 (2006), 211-228.
- Richard v. Sternberg, “On the Roles of Repetitive DNA Elements in the Context of a Unified Genomic– Epigenetic System”; Jonathan Wells, “Using Intelligent Design Theory to Guide Scientific Research”; Josiah D. Seaman and John C. Sanford, “Skittle: A 2-Dimensional Genome Visualization Tool,” BMC Informatics 10 (2009), 451.