A Series on Biological Information: New Perspectives • Article 1: New Scientific Volume, Biological Information: New Perspectives, Challenges Neo-Darwinism, Survives Evolution Lobby's Attempt at Censorship • Article 2: On the Origin of the Controversy Over Biological Information: New Perspectives • Article 3: Censorship Loses: Never Forget the Story of Biological Information: New Perspectives • Article 4: Biological Information New Perspectives Investigates "Information Theory & Biology" • Article 5: In BIO-Complexity and Biological Information: New Perspectives, Granville Sewell Defends his Arguments on the Second Law of Thermodynamics • Article 6: In Biological Information: New Perspectives, Jonathan Wells Explores Functions for Non-Gene-Coding Information • Article 7: In Biological Information: New Perspectives, Michael Behe finds Loss of Function Mutations Challenge the Darwinian Model • This Article: Douglas Axe and Ann Gauger Argue that Design Best Explains New Biological Information

What is the best explanation -- neo-Darwinism or intelligent design -- for the origin of the basic metabolic complexity found in life? According to a new paper by Douglas Axe and Ann Gauger in Biological Information: New Perspectives, "Explaining Metabolic Innovation: Neo-Darwinism Versus Design," in order to explain "the origin of biological complexity," we "must tackle the particular challenge of explaining metabolic complexity." In the course of reviewing the results of their own previous research papers, and the research of others, they examine six obstacles to Darwinian explanations of metabolic complexity. They conclude that a design-based paradigm offers the best solution to those obstacles.

Problem 1: Offsetting the cost of gene expression

Axe and Gauger observe that "The most widely accepted explanation for the origin of new enzymes is gene duplication and recruitment." However, they cite experimental work showing that a duplicate gene is much more likely to be silenced (because of the costly resources expended in transcribing and translating it) than it is to acquire a new function. So the first obstacle is as follows:

First obstacle: Because gene expression is costly, it cannot be assumed that weakly converted enzyme functions isolated by laboratory selection would provide net selective benefit in wild populations.

In their view, intelligent design provides a better explanation because such innovations require a goal-directed cause that looks beyond immediate fitness costs that go along with preserving a non-advantageous duplicate gene. Thus, they derive the

First principle: Innovations are more like investments than quick cash. They must be well implemented to offset their cost, and even then the benefits tend to accrue over a long period.

Problem 2: Winning the fixation lottery

The second obstacle they identify is the fact that local bacterial populations tend to be short lived, and in the long-term "losers in the game of bacterial procreation vastly outnumber winners." For "rare mutations or rare combinations of mutations" this presents a problem: billions of years might be necessary to avoid local extinction, and to fix a rare trait. Therefore, they proceed to the

Second obstacle: Beneficial mutations appearing less than about once per generation in a global bacterial population may remain unfixed for a billion years or more.

To solve this problem, they present a second principle:

Second principle: For innovations to be established reliably they need to be carried past a critical "tipping point" in numerical representation, beyond which they become self-establishing.

Problem 3: Complex adaptation -- combining rare genetic events

A third problem is how long it takes for new features to appear that, in order to provide some kind of functional advantage, require multiple coordinated mutations. They cite some of their own research which suggests that multiple mutations would be required before even modest protein-to-protein conversions could occur. After reviewing this and other research, they conclude:

Two things seem inescapable. One is that enzymatic innovations requiring more than two specific mutations in a spare gene (provided by a duplication event) are implausible in neo-Darwinian terms. The other is that once this limitation is taken into account, most reported experimental conversions of enzyme function are beyond the reach of neo-Darwinian processes under natural conditions.

This leads to the:

Third obstacle: Adaptations requiring duplication and modification of an existing gene should not be presumed feasible if they require more than two specific base substitutions, which seems to exclude most functional conversions.

The solution they propose to this problem suggests:

Third principle: The substantial reworking of a homologous structure needed to give it a genuinely new function is more suggestive of reapplication of a concept than adjustment of a physical thing.

Problem 4: The complexity of metabolic pathways

In Problem 3 they found that producing even one new protein could strain the probabilistic resources of the Darwinian mechanism. But to produce metabolic complexity would require "multiple enzymatic innovations." In their view, "This poses a severe challenge for neo-Darwinism. Mechanisms that have been proposed in attempts to meet this challenge, such as retrograde evolution, or serial duplication and recruitment do not match the actual distribution of protein domains across and within pathways." This leads to the next problem:

Fourth obstacle: Accounts of metabolic innovation must recognize that beneficial metabolic traits typically depend on multiple dedicated genes.

Again, a goal-directed process seems necessary to solve this problem:

Fourth principle: Useful innovations tend to require the simultaneous solution of multiple new problems, which means they tend to be compound innovations.

Problem 5: Radical innovation -- the need for new protein folds

As we've discussed here at ENV in the past, Axe's prior research has suggested that only very rare amino acid sequences yield functional protein folds. How many new folds would be necessary to account for metabolic complexity in a bacterium? The answer is on the order of hundreds. This leads to the

Fifth obstacle: Accounts of metabolic innovations must recognize that they often depend on new protein folds.

They propose the following solution

Fifth principle: Useful innovations often involve both the reapplication of proven design concepts and the de novo invention of new ones.

Problem 6: Causal circularity

The sixth problem is a novel one. Some proteins are necessary for their own production, leading to what Axe and Gauger call "causal circularity." For scientists seeking to explain the origin of such proteins, it's something of a chicken-and-egg problem. Axe and Gauger explain a few small-scale examples of causal circularity, and the problems thus posed for Darwinian mechanisms. For example: "So, in order to conceive of an evolutionary origin of biotin biosynthesis, we must suppose that prior to this origin either A) cells were making their membranes without biotin, or B) cells had an abiotic source of biotin." But they realize this is not a small-scale problem that must be solved:

In fact, there is a simple way to generalize the principle of causal circularity. Since life is a prerequisite for all biosynthesis, any biosynthetic product that is necessary for life in its present form is also necessary for its own biosynthesis in modern life. So causal circularity exists for all essential biosynthetic products.

The sixth problem recognizes these complexities:

Sixth obstacle: The fact that life depends on numerous components jointly means that no simple relationship exists between the functions of these components and the selective story that would be needed for them to have arisen as simple adaptations.

Yet again, only a complex process, capable of working in a top-down fashion to coordinate multiple parts, can solve this problem:

Sixth principle: The implementation of innovation is nearly the opposite of ordinary physical causation. It is the top-down arrangement of matter in such a way that the resulting bottom-up behavior of that matter serves the intended purpose of the innovator.

After comparing the "principles" required to resolve the six problems they raise, they conclude that intelligent design provides a compelling way to solve all of these problems:

[B]iological innovation seems similar in essence to human innovation, though certainly beyond it in degree. This realization is attracting an increasing number of engineers to biology with the aim of reapplying biological innovations in human technology. Although that field of study, known as biomimetics, has practical ambitions, the fact that it exists (and is thriving) also implies an essential similarity between intelligent design in engineering and intelligent design in life.

They propose that a new design-based model of biology could help scientists understand how systems like metabolism arose. Indeed, as a goal-directed process, intelligent design stands apart from unguided Darwinian evolution, and can uniquely provide the kind of innovative solutions necessary for complex life.