For systems biologists, life is optimally designed

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Image: Human knee, by Blausen.com employees (2014). “Medical Gallery of Blausen Medical 2014”. WikiJournal of Medicine 1 (2). DOI: 10.15347 / wjm / 2014.010. ISSN 2002-4436., CC BY 3.0 , via Wikimedia Commons.

In my last two articles (here, here), I described the revolution in systems biology where practitioners replaced evolutionary premises with design-based assumptions like the central role of teleology. Now I’m going to examine how biologists have increasingly given up the belief that bad design is ubiquitous in life. Instead, they often assume that biological structures and systems are highly optimized.

Expectation of bad design

The underlying logic of the Standard Evolution Model predicts that poor design and non-functional remnants of the evolutionary past of organisms should contaminate the biosphere. The reason is well summed up in Wikipedia‘s article “Argument from Poor Design”:

“Bad design” is consistent with the predictions of the scientific theory of evolution using natural selection. This predicts that features developed for certain uses will then be reused or co-opted for other uses or abandoned altogether; and this sub-optimal state is due to the inability of the hereditary mechanism to remove the particular remnants of the evolutionary process.

In terms of the fitness landscape, natural selection will always be drifting “up the hill,” but a species typically cannot get from a lower peak to a higher peak without first traversing a valley.

Figure from Wikipedia the fitness landscape. The landscape is a theoretical representation of the comparison of different organisms in terms of their fitness. The x-axis (and often also the y-axes) represent the variation in characteristics (e.g. size, coat color) and the z-axis represents fitness. Natural selection is expected to take fitness to a peak, but that peak is often not the highest.

The expectation of bad design is not simply a subjective conclusion based on intuition, but has been rigorously demonstrated in computer models. Such a model by Snoke, Cox, and Petcher clarified why evolutionary processes that allow for an increase in complexity must generate large amounts of junk DNA and non-functional elements. The details of their model are complex, but the logic behind it is simple.

For complex innovations to emerge, organisms must allow non-functional DNA to appear and remain in the population until a functional sequence is created. Such additions to the genome could be made by gene duplication and then repeated mutation. Junk DNA would inevitably accumulate and comprise a significant percentage of the genome. For this reason, biologists once assumed that junk DNA made up 97 percent of the human genome.

Likewise, the creation of complex structures (e.g. molecular machines) requires countless trial-and-error arrangements of molecules or tissues until something beneficial appears. Most attempts would either be inoperable or inefficient. Consequently, only a minority of biological structures and systems should appear highly optimized.

Central argument against design

The most obvious difference in predictions between intelligent design and undirected evolution is the extent to which life exhibits sub-optimal / non-functional versus optimal design. The philosopher Philip Kitcher emphasized this point in his book Living with Darwin: Evolution, Design, and the Future of Faith. As a main argument for rejecting intelligent design, he cited examples of what he considered to be clumsy, incompetent designs:

If you were a talented engineer designing a whale from scratch, you probably wouldn’t think of equipping it with a rudimentary basin. … If you were to design a human body, you could certainly improve the knee. And if you were to design the genomes of organisms, you certainly wouldn’t fill them with junk.

The biologist Nathan Lents argued similarly in his book Human Errors: A Panorama of Our Glitches, From Senseless Bones To Broken Genes that the “botched” design in the entire human body shows that we are not the product of an intelligent designer, but of an undirected evolutionary process:

The third category includes the human defects that are due to nothing other than the limits of evolution. All species are stuck in their bodies and can only move forward through the smallest changes, which are random and infrequent. We have inherited structures that are terribly inefficient but cannot be changed.

Therefore, our throats carry food and air through the same tiny space, and our ankles have seven senseless bones sloshing around. Much more would be required to fix one of these poor designs than a single mutation could ever achieve. To assume that these living things were created separately means to consider the creative agent as a whimsical, clumsy, mediocre engineer, unintelligent designer.

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Change perspectives

Yet most of the examples of supposedly bad design cited by Kitcher, Lents, and other skeptics have been refuted (here, here, here, here). The remaining ones typically represent deteriorations of what were once optimal designs or appeal to the fallacy of the imperfection of the gaps.

Alleged examples of bad design are usually opinions resulting from the armchair critics’ limited understanding of the technical literature and their lack of engineering training. For example, in direct contradiction to Kitcher and Lents’ claims, engineers often re-use design motifs in new ways, just as they did with the whale pool. And doctors and engineers have shown how the human knee and ankle are optimally and exquisitely designed (here, here, here, here). Engineers even took inspiration from these structures to design artificial limbs (here, here).

In addition, thanks to the ENCODE project, it is now known that most of the human genome is functional. The devastating effects of this revelation on the theory of evolution have not gone entirely unnoticed. Biochemist Dan Graur stated bluntly:

If the human genome is indeed free of junk DNA, as the ENCODE project implies, then a long, undirected evolutionary process cannot explain the human genome. Conversely, when organisms are designed, all or as much of the DNA as possible is expected to have a function. If ENCODE is right, then evolution is wrong.

“Basic principles of optimality”

Equally important, systems biologists now recognize that adopting an optimal design leads to the most productive research. Nikolaos Tsiantis, Eva Balsa-Canto and Julio R. Banga, for example, have developed a model for studying biological systems that is based on the identification of “underlying principles of optimality”. And in their 2018 Bioinformatics Articles interviewed leading researchers who also demonstrated the predictive power of assuming optimality:

Sutherland (2005) claims that these principles of optimality enable biology to move from merely explaining patterns or mechanisms to being able to make predictions from first principles. Bialek (2017) points out that optimality hypotheses should not be adopted for aesthetic reasons, but as an approach that can be directly verified through quantitative experiments. Mathematical optimization could therefore be viewed as a fundamental research tool in bioinformatics and computational systems biology.

Other researchers have even shown that biological systems such as DNA replication and translation, embryological development, and sensory processes are working at the limit of what is physically feasible. Human engineering pales in comparison to such accomplishments.

The enormous preponderance of evidence is consistent with the design-based prediction of optimality. And it directly contradicts a central prediction of any theory of undirected evolution. Will this evidence convince critics like Kitcher and Lents to reconsider their views? Most likely not, since their belief in scientific materialism is based not on empirical evidence but on their philosophical beliefs. Fortunately, despite social pressures to maintain the status quo, many biologists have allowed the evidence to point them in the right direction. These scientists will lead biology through the next great scientific revolution that has just begun.


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