Wikipedia’s infamously biased, rule-violating, and error-filled entry on intelligent design states that “any appeal to an intelligent creator is explicitly excluded for the paralyzing effect it may have on the scientific progress.” But what if scientists are already using intelligent-design reasoning to study life? What if they are treating biological systems as if they are designed, and what if this approach turns out to be scientifically fruitful?
What if many scientists are doing this and they don’t even realize it? What if this has been going on for years, and it has been one of the most successful methods for investigating biology? Welcome to the field of systems biology. In a new peer-reviewed paper at BIO-Complexity, “Systems Biology as a Research Program for Intelligent Design,” University of Pittsburgh physicist David Snoke makes this point powerfully:
Opponents of the intelligent design (ID) approach to biology have sometimes argued that the ID perspective discourages scientific investigation. To the contrary, it can be argued that the most productive new paradigm in systems biology is actually much more compatible with a belief in the intelligent design of life than with a belief in neo-Darwinian evolution. This new paradigm in system biology, which has arisen in the past ten years or so, analyzes living systems in terms of systems engineering concepts such as design, information processing, optimization, and other explicitly teleological concepts. This new paradigm offers a successful, quantitative, predictive theory for biology. Although the main practitioners of the field attribute the presence of such things to the outworking of natural selection, they cannot avoid using design language and design concepts in their research, and a straightforward look at the field indicates it is really a design approach altogether.
(David Snoke, “Systems Biology as a Research Program for Intelligent Design,” BIO-Complexity, Vol. 2014 (3).)
Snoke outlines some basic differences between an ID approach to studying origins and a Darwinian one:
If both viewpoints tend to focus on material causes and effects in presently existing systems, where do they diverge in their predictions? Consider the two cases mentioned above, namely a very good human designer and a very bad human designer. The bad designer may, for example, be a Darwinian designer who simply tries all kinds of things and throws out the attempts that don’t work. How would we expect their products to differ?
To start, we would expect the good designer to produce products with few non-functional elements. This is related to the expectation that good design will have a high degree of optimization, or efficiency.
It is possible to imagine that a bad designer could also obtain some degree of optimization by simply trying many times, and always keeping the most efficient version. This is the Darwinian explanation of the efficiencies that may exist in biological systems. A bad designer could make random changes to existing designs, and toss away the less optimal versions each time. But even a short consideration tells us that such an approach would probably have some non-functional or non-optimal elements, and that we would expect more of them than we would in a truly well-designed system. Thus, proponents of Darwinism have historically argued for “junk” in living systems, such as “vestigial” organs or “junk” DNA.
A good-design assumption also leads us to expect other attributes besides just the lack of non-functional elements. In well-designed systems we expect to find subtle and elegant methods. By contrast, in badly designed systems we expect to find “kludgy” and “brute force” methods, i.e., methods that involve gross inefficiencies but get the job done. Proponents of Darwinism have often argued that the kludgy, inelegant methods that exist in biology are evidence that biological systems are not designed by an intelligent agent.
These differing predictions of course lead Snoke to ask whether intelligent design or Darwinian biology would lead us to predict the findings of systems biology. He asks: “Given these different approaches and different expectations, what can current systems biology tell us? Which paradigm fits more naturally with the way that systems biology is actually being done?”
Snoke begins his discussion of systems biology by recounting various authorities who have praised the utility of the approach for studying biology. He observes, “Biologists and biophysicists are now learning to think like engineers when approaching biological systems.” He contrasts this approach with the approach of physics:
The world of physics has been dominated by bottom-up, first-principles thinking. In many cases this has meant starting with the microscopic elements of a system, but even in the fields of physics that look at macroscopic behavior, the focus has been on elementary, universal principles such as “scaling laws.” The overriding paradigm in physics has been that simple, non-teleological rules will eventually explain everything. Even emergent behavior in complex systems is assumed to be the result of simple interactions.
By contrast, engineering takes a top-down approach that is explicitly teleological. A goal is defined, and then the parts are arranged to bring about that goal. Basic physics principles may or may not be used, depending on whether they are helpful. Engineering principles are fundamentally design principles, not reductionist principles. Engineering students learn good ways of solving problems to achieve pre-defined goals in the same way that physics students learn universal physical laws.
Snoke spells out what few are willing to explicitly state about systems biology:
While not everyone in the field agrees with the use of engineering terminology, even those who don’t like the word engineering still use engineering-like teleological terms. For example, Wolkenhauer and Mesarovic, writing an essay against the use of engineering terms, say:
We first need to realize that in order to control, reg�ulate or coordinate something, we mean to adapt, maintain, optimize. Thereby, implicitly, there must exist a goal or objective . [Emphasis in original.]
The holistic or emergent approach of systems biology is therefore not just a focus on larger systems or interactions of parts. The productive new paradigm is to look at those larger systems from the standpoint of an engineer seeking an objective. This is exactly the perspective I argued in 2001 should arise from a belief in intelligent design.
The Findings of Systems Biology
After observing, “It has become an extremely productive paradigm in biology to look for biological systems that exhibit the properties of sophisticated engineered systems, i.e ones that resemble methods developed by human engineers over the past few hundred years to accomplish complicated tasks,” Snoke lists various features in biology that have been found to function like goal-directed, top-down engineered systems:
- “Negative feedback for stable operation.”
- “Frequency filtering” for extracting a signal from a noisy system.
- Control and signaling to induce a response.
- “Information storage” where information is stored for later use. In fact, Snoke observes:
This paradigm [of systems biology] is advancing the view that biology is essentially an information science with information operating on multiple hierarchical levels and in complex networks . [Emphasis added.]
- “Timing and synchronization,” where organisms maintain clocks to ensure that different processes and events happen in the right order.
- “Addressing,” where signaling molecules are tagged with an address to help them arrive at their intended target.
- “Hierarchies of function,” where organisms maintain clocks to ensure that cellular processes and events happen at the right times and in the right order.
- “Redundancy,” as organisms contain backup systems or “fail-safes” if primary essential systems fail.
- “Adaptation,” where organisms are pre-engineered to be able to undergo small-scale adaptations to their environments. As Snoke explains, “These systems use randomization controlled by supersystems, just as the immune system uses randomization in a very controlled way,” and “Only part of the system is allowed to vary randomly, while the rest is highly conserved.”
After recounting such seemingly engineered aspects of biology, of the kind that systems biology studies, Snoke asks why systems biology has done such a good job of identifying these features of biology. He finds that the success of systems biology can be attributed to the assumptions it makes. And what are those? Again, Snoke provides a list of assumptions that overlaps neatly with many of the assumptions of intelligent design.
For example, he argues that systems biology assumes “teleology,” which is to say “top-down” rather than “bottom up” design. As he puts it, systems biology assumes that biological systems were built “starting with a goal, and then working backwards to see what is needed and used to accomplish that goal.” That’s a concise description of how designers operate. He provides a lengthy citation from a 2004 paper by the respected cell biologist Arthur Lander at the University of California, Irvine, writing in PLoS Biology under the title, “A Calculus of Purpose.” You can find the entire passage in the original paper, including the following striking comment:
In biology we often pose “why” questions in which it is purpose, not mechanism, that interests us. …As a group, molecular biologists shy away from teleological matters … Molecular biology and molecular genetics might continue to dodge teleological issues were it not for their fields’ remarkable recent successes. Mechanistic information about how a multitude of genes and gene products act and interact is now being gathered so rapidly that our inability to synthesize such information into a coherent whole is becoming more and more frustrating. Gene regulation, intracellular signaling pathways, metabolic networks, developmental programs — the current information deluge is revealing these systems to be so complex that molecular biologists are forced to wrestle with an overtly teleological question: What purpose does all this complexity serve?
(Arthur D. Lander, “A Calculus of Purpose,” PLoS Biology, Vol. 2(6): 0712-0714 (June, 2004).)
Lander identifies various commonly re-used components in biology — networks that function as a “‘switch,’ ‘filter,’ ‘oscillator,’ ‘dynamic range adjuster,’ ‘producer of stripes,’ etc.” He notes that by recognizing the effects of these common elements, biologists are able to more quickly determine the function or purpose of a system.
I don’t see any evidence that Lander himself is a proponent of intelligent design. But he nonetheless concludes that recognizing the “teleological side of molecular biology” is vital for doing research:
These elements can be seen as the foundations of a new calculus of purpose, enabling biologists to take on the much-neglected teleological side of molecular biology. “What purpose does all this complexity serve?” may soon go from a question few biologists dare to pose, to one on everyone’s lips.
Snoke observes that systems biology assumes that biological features are optimized, meaning, in part, that “just about everything in the cell does indeed have a role, i.e., that there is very little ‘junk.'” He explains, “Some systems biologists go further than just assuming that every little thing has a purpose. Some argue that each item is fulfilling its purpose as well as is physically possible,” and quotes additional authorities who assume that biological systems are optimized.
Systems biology also takes for granted that organisms are “robust,” by which Snoke means, “not only in the ability of a single organism to operate in a changing environment” but also “the ability of a type of organism to endure in multiple forms and different ecosystems.” He finds that biological systems are often “overdesigned,” meaning they are “designed to continue to operate under conditions far from the expected normal operating conditions.” Strikingly, Snoke observes that, “Typically the subsystems that are overdesigned are those that are essential for the operation of the whole system,” and “This occurs at all levels in biological systems.”
Finally, Snoke observes that systems biology assumes organismal features can be “reverse engineered,” and that this is “evidenced by the frequent explicit use of this term” in the literature. He notes that this is helping us to improve human technology — as we understand how biological systems work, we can better build our own systems. Snoke calls such thinking “design language,” and he notes that biologists who use the systems biology approach use this kind of thinking to explain what they see. One author he cites even asks, “Can we employ understanding from specific cases to decipher ‘design principles’ applicable to all biological systems?” Snoke observes that many mainstream biologists use precise such terminology.
What does this tell us about the study of intelligent design in biology? More on that tomorrow.