Review Article Explores Design Patterns in Biological Cells
An excellent review article, “Design Patterns of Biological Cells,” published earlier this year in BioEssays, shows that design patterns are “generalized solutions to recurring problems.” (Andrews, Wiley, and Sauro 2024) The theory of intelligent design (ID), which suggests that certain aspects of nature are better explained by intelligent agency than by law-like regularities and chance events, predicts design patterns in nature. This is because design patterns are technically derived solutions — derived by designers — to challenges encountered within the design space.
The authors review three types of design patterns. For each, I’ll pick one subcategory and flesh it out in a bit more detail. I will then turn to discussing how each design pattern requires direct actions of an intelligent agent. Here are the three categories:
“Creational” describes recurring patterns for building components of the cell.
“Structural” describes interconnections or relationships between objects in the cell.
“Behavioral” describes the behavior of cellular objects through time.
Creational Design Patterns
Andrews et al. identify five subcategories for creational design patterns. The first of these subcategories is templating. Cells need to maintain the information in DNA and prevent it from being corrupted by time and chance. The solution to this problem is templating where the original information is faithfully templated or copied. Templating in the cell includes DNA replication, transcription, and translation. Templating points to an intelligent agent behind its design because it requires comprehension of the forces imposed by the laws of physics and chemistry that would need to be overcome to preserve information. Templating also requires foresight to imagine a solution, inventing irreducibly complex molecular machines like DNA polymerase, RNA polymerase, and the ribosome. Finally, templating requires creation of the information itself, the physical DNA code for the polymerases and ribosome.
Structural Design Patterns
Structural patterns are design patterns based on object-to-object relationships. For structural patterns, Andrews et al. identify six subcategories. Of these, the third is common currency.
Across cells, there are many common metabolites, including common storage forms of energy, such as ATP. Traditionally, this has been attributed to common ancestry, but there is a critical design-based reason for it. Using a common currency simplifies interactions between objects. For example, it’s easier to fill up your car with gasoline when gasoline is a common currency because no matter where you travel, other people need gasoline for their cars, and so chances are you will find it for sale. Here’s another example: It is easier to buy groceries with a common currency, such as the U.S. dollar, because you don’t have to stop and exchange your money before making a purchase, likely paying a fee for doing so.
Cells face similar constraints. They rely on certain producers of energy at certain times and incur a cost for energy interconversions. Thus, ATP is likely a designed solution to the aforementioned requirements. Andrews et al. point out that the topological pattern of common currency has a “bow tie” architecture. In this type of architecture, many nutrients are turned into a common currency (the knot) which can then expand to accomplish many different things. This design motif requires an intelligent agent because the goals of the ecosystem and organisms must be evaluated before coming up with a currency that can work between lower-level objects. This entails an understanding of how everything will interrelate and what is possible in the design space of physics and chemistry, followed by planning and implementation. Only an intelligent agent has these capabilities, which are not accessible to random processes.
Behavioral Design Patterns
Behavioral design patterns focus on the dynamics of reaction networks. Andrews et al. identify eight such patterns. For example, there is switching, which occurs when a continuum of input needs to be converted into a discrete output. The most common ways of accomplishing this are by using ultrasensitivity or bistability. Ultrasensitive switches facilitate a sharp threshold response, ensuring that the system is fully in one state or the other, rather than in an intermediate state. This can be accomplished in different ways. A classic example of ultrasensitivity in biology is the necessary switch of hemoglobin from binding oxygen in the lungs to releasing it in the muscles. This is performed through the allosteric design of hemoglobin. When the partial pressure of oxygen is high (in the lungs), binding of one molecule of oxygen makes binding of the next oxygen molecule easier. Importantly, when one plots the percentage of hemoglobin bound to oxygen versus the partial pressure of oxygen, the curve is sigmoidal, not hyperbolic. The sigmoidal shape tells us that in the lungs, binding of oxygen is easier after the first molecule binds and release of oxygen becomes easier in the muscle after the first molecule is let go.
Ultrasensitivity, represented by that sigmoidal curve, can be accomplished in another way. Suppose there is a phosphorylation-dephosphorylation cycle where the kinase and phosphatase are working at saturation levels and have rate constants that are independent of the concentration of their substrates. Although it isn’t abundantly clear until this is plotted graphically, the response is also ultrasensitive, i.e., sigmoidal. Given those conditions, the cycle can switch sharply between being almost entirely in one state to being almost entirely in the other state. (Ferrell and Ha 2014b)
Another way ultrasensitivity works is by having multiple phosphorylation sites on a kinase where the kinase isn’t active until the final phosphorylation, and each successive phosphorylation is a little bit easier than the prior one. (Ferrell and Ha 2014a)
Yet another example is concentration-based inhibition. Here, a tightly bound inhibitor might prevent enzyme activity, up until the enzyme concentration surpasses that of the inhibitor. At that point the enzyme is no longer inhibited, leading to a sudden switch.
A final and somewhat different example is positive feedback. This occurs more frequently in developmental networks than in sensory networks. Why is that the case? Positive feedback slows response time, which is advantageous for multistage processes that are time-consuming or include delays. Slower response times can also help reduce noise, which is critical when making irreversible decisions. However, positive regulation can accomplish more than just that. Positive regulation can make sharp decisions between two states and remembering those decisions for a long time, a phenomenon known as bistability. Consider positive feedback in gene regulation: once a gene is activated by positive autoregulation, it is locked ON. The gene will remain elevated even after the input has disappeared, providing long-term memory that the input existed. This type of switching is employed during development to make irreversible decisions that commit a cell to a specific fate. (Alon 2019)
Why does the switching design motif point to intelligence? Designing an effective switch requires understanding the system and what needs to be controlled “— what needs to be turned on or off at what time?” Considering whether control should be manual or automated is needed. Additionally, building appropriate switches requires knowledge about safety. Switching is also often necessary in sequential operations. The type of switch, the sensitivity of the switch, the construction of the switch, and the switch’s compatibility must all be thought through ahead of time. Each switch functions a certain way in the system such that it appears to “know” or “anticipate” other switch behavior so that more complex behavior can be produced.
Why Study These Design Patterns?
It is useful to compile a list of solutions that cells utilize to solve specific problems. Design patterns abstract a broad range of cell functions into a manageable set of distinct patterns connected to the functions they serve by showing how they solve certain problems. Looking at these, as we did with the example of the different ways ultrasensitivity is currently known to be accomplished, provides a deeper understanding of why cellular mechanisms operate the way that they do. Accordingly, design patterns in cells also provide an outstanding illustration of how design-based thinking can further our understanding of biology.
References
Alon, Uri. 2019. An Introduction to Systems Biology: Design Principles of Biological Circuits. Second edition. | Boca Raton, Fla. : CRC Press, [2019]: Chapman and Hall/CRC.
Andrews, Steven S., H. Steven Wiley, and Herbert M. Sauro. 2024. “Design Patterns of Biological Cells.” BioEssays: News and Reviews in Molecular, Cellular and Developmental Biology 46 (3): e2300188.
Ferrell, James E., Jr, and Sang Hoon Ha. 2014a. “Ultrasensitivity Part II: Multisite Phosphorylation, Stoichiometric Inhibitors, and Positive Feedback.” Trends in Biochemical Sciences 39 (11): 556–69.
Ferrell, J. E., Jr, & Ha, S. H. (2014b). “Ultrasensitivity Part I: Michaelian Responses and Zero-Order Ultrasensitivity.” Trends in Biochemical Sciences 39 (10): 496–503.
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