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The Design Connection in Biological Tracking Systems

Photo: Gulls of the family Laridae, by Oleg Bor, CC BY-SA 4.0 , via Wikimedia Commons.

In my last article, I summarized a lecture presented at CELS (Conference on Engineering in Living Systems) that presented a model for adaptation based on the engineering principles employed in human engineered tracking systems. Now I will address the connection between these principles and the design inference.  

As a review, biological adaptation is often driven by systems that employ three subsystems:

  • Sensors that monitor specific environmental conditions.
  • Logic-based analyzers such as switches that trigger responses when certain conditions occur. 
  • Mechanisms that drive targeted output responses.

Irreducible Complexity and Timescales 

To say that such tracking systems could not have evolved gradually almost goes without saying. Many examples of NGE do not even directly help an individual organism but only an entire population acting in concert. For instance, increasing the mutation rate to rapidly generate targeted genetic variation will often assist only a few lucky individuals to survive extreme threats such as an antibiotic.  

More generally, not only are all tracking systems irreducibly complex, but they require the subsystems to be meticulously integrated. And the integrating components, such as switches (herehere), correspond to far greater amounts of information than what could have been generated in the available timeframes. This challenge is highlighted by the fact that timescales (waiting times) grow exponentially with the amount of required new information (herehere).

The Design Connection

The presence of highly controlled adaptive mechanisms directly correlates to life employing top-down design that must meet numerous tight engineering constraints. If organisms resulted from haphazard undirected processes, their design constraints would be few and highly flexible. Altering anatomy and/or physiology should then be relatively easy, and the same undirected processes could potentially drive the changes. In contrast, the presence of numerous tight constraints correlates with altering the system being far more difficult. Significant changes would typically require highly specified and coordinated modifications. 

Szallasi et al. in Systems Modeling in Cellular Biology tacitly came to this same conclusion:

An often noted reservation against the type of analogies between biological and engineered systems we brought forward states that these two types of complex systems arise in fundamentally different ways, namely through evolution versus purpose-driven, top-down design (see, for example, Bosl and Li (2005)). Clearly, evolvability is of paramount importance for living systems (Kirschner and Gerhart, 1998). Here, we think of evolvability simply (maybe naively) in the sense of controlled and structured change in lineages, rather than cells, on long time scales in response to perhaps large variations in the environment. At the population level (of all engineered systems of one type), evidently progress in engineering fulfills similar criteria. [Emphasis added.]

p. 32

Note how the authors do not describe evolution using such traditional terms as “random” and “undirected.” Instead, they describe change as “controlled” and “structured.” Their description of evolvability sounds less like neo-Darwinian evolution than like technological innovation.