In this series, I have been responding to Erika DeBenedictis, an MIT doctoral graduate in biological engineering, and her TEDx talk, “It’s Time for Intelligent Design.” (Find my first post in the series here.) Before continuing, we must first establish some basic definitions and decide how one would assess the quality of a particular design. Keep in mind, first, that assigning terms like “good” or “poor” assumes that the purpose of the design in question is known or can somehow be inferred. Secondly, the classification of “good” or “bad” design needs to be thoughtfully assigned to either a specific feature of the system or to the system as a whole. Finally, since human engineering has taught us that an initially “good” design can become corrupted over time, there must be a way to distinguish a good-but-degraded design from an inherently “poor” design. Allow me to illustrate these three principles using the analogy of an iPhone.
Purpose and Quality Assessment
If no function or purpose is assumed, how can one evaluate quality? The iPhone’s purpose is to be a smart device with a touch screen interface that can provide a computer, photo, phone, and music experience to a user. Millions are willing to spend significant sums of money on an iPhone and will choose it over a variety of other smart devices, suggesting a good overall design to fulfill its intended purpose. In contrast, no one purchases an iPhone to use as a cutting board (I hope!). If the iPhone’s design was mistakenly classified for that purpose, it would also be mistaken as a poor design — expensive, fragile, and definitely not dishwasher safe. The assignment of purpose is pretty straightforward in the context of human engineering, but much less so in biology (see Mayer-Bacon et al.; and Mantri and Thomas). And, mistaking the purpose of a particular biological feature or system may incorrectly suggest poor design.
First Impressions Often Overlook Trade-Offs
To me, the iPhone’s design does not seem optimal in all aspects. I agree that the iPhone exhibits good overall design, but some specific features strike me as a bit flawed. To give an example, I am frustrated by having to purchase and use a separate “lightning connector” to listen to music in my car or to connect my earbuds. But I acknowledge that maybe I am overlooking certain design constraints or the needs of other users. Perhaps Apple anticipated the move to wireless earbuds, the fact that auxiliary inputs are becoming obsolete in newer cars, or the advantages of consolidating to a single lightning port. Therefore, my critiques of specific features do not mean the iPhone isn’t well designed but more likely represent my failure to account for certain design constraints.
Probably Blame Time and Chance for the Flaws
Most actual iPhone flaws are due to wear and tear from use. Although my iPhone was in perfect condition — fresh out of the box — now the screen is cracked, corners dented, lightning cable loose, and my battery always seems to be at 1 percent. A few hard drops, a day at the beach, and a ski trip later, my iPhone’s original design is now degraded.
In biology we observe something similar at both the individual and population level. For an individual, over a lifespan, deleterious variations accumulate in somatic cells, and may cause cancer or other disease. At the population level, deleterious variations also occur and are present today at a much higher frequency than they were in the past (see Fu et al.). This suggests that previously the human genome had fewer predicted deleterious protein coding variations than what we have presently.
Biology Can Help Us Be Better Engineers
DeBenedictis’s statement that “organisms are absolutely the most sophisticated machines we know of” is supported by overwhelming evidence. The systems of biological machinery surpass the latest iPhone (or any other human invention, for that matter)! From the storage mechanisms of biological information to the wonder of self-replication, biology continues to amaze scientists with its ingenious systems and inspire them towards better engineering. Engineers inspired by dental enamel (Ma et al.), light-harvesting systems of photosynthesis (Scholes et al.), hummingbird wing morphing (Maeda et al.), and impact-resistant beetles (Rivera et al.) are currently copying biology to build better ceramics, solar panels, drones, and materials.
Weaver and Birkedal rightly emphasize the contributions of nature’s designs to engineering: “Nature produces a multitude of composite materials with intricate architectures that in many instances far exceed the performance of their modern engineering analogs.” (Frølich et al.)
In 2004, M. L. Simpson et al. published in IEEE that:
Genetic and biochemical processes are highly functional and dense systems. Cells perform highly complex functions regulated by genetic circuits and networks much like engineered systems, only at far greater densities, complexity, and capabilities. Silicon-based technology cannot come close to the kinds of integration seen at the bacterial scale, for example.
To support my argument for good design, in my next post I will offer three examples where core biological infrastructure has been quantified as “optimal.” Optimality refers to the best solution to multiple conflicting constraints and provides a quantitative way to assess design in biology. Trade-offs between conflicting constraints are a fact of life, and thus, achieving an optimized system means sacrificing ideal conditions for one element so that the needs of another element are accommodated. Bar-Even et al. explain this principle within the context of metabolism. They argue that although metabolic pathways initially appeared complex and confusing, the rationale for their design has become clear after constraint consideration:
Metabolic pathways may seem arbitrary and unnecessarily complex. In many cases, a chemist might devise a simpler route for the biochemical transformation, so why has nature chosen such complex solutions? In this review, we distill lessons from a century of metabolic research and introduce new observations suggesting that the intricate structure of metabolic pathways can be explained by a small set of biochemical principles. Using glycolysis as an example, we demonstrate how three key biochemical constraints — thermodynamic favorability, availability of enzymatic mechanisms and the physicochemical properties of pathway intermediates — eliminate otherwise plausible metabolic strategies. Considering these constraints, glycolysis contains no unnecessary steps and represents one of the very few pathway structures that meet cellular demands. The analysis presented here can be applied to metabolic engineering efforts for the rational design of pathways that produce a desired product while satisfying biochemical constraints.
In summary, I remind my readers that prudent scientists must consider conflicting constraints before labeling design in biology as “good” or “bad.” Next, I’ll explore three areas where this has been done in biology and argue that the results should cause us to further investigate the extent of optimality in biology.