Yesterday, I submitted a paper to the arXiv entitled
The "Machinery" of Biocomplexity: understanding non-optimal architectures in biological systems, cross-listed between the
nlin.AO,
physics.bio-ph, and
q-bio.QM categories. Here is a
link to the paper, and abstract, and a summary figure:
One popular assumption regarding biological systems is that traits have evolved to be optimized with respect to function. This is a standard goal in evolutionary computation, and while not always embraced in the biological sciences, is an underlying assumption of what happens when fitness is maximized. The implication of this is that a signaling pathway or phylogeny should show evidence of minimizing the number of steps required to produce a biochemical product or phenotypic adaptation. In this paper, it will be shown that a principle of "maximum intermediate steps" may also characterize complex biological systems, especially those in which extreme historical contingency or a combination of mutation and recombination are key features. The contribution to existing literature is two-fold: demonstrating both the potential for non-optimality in engineered systems with "lifelike" attributes, and the underpinnings of non-optimality in naturalistic contexts.
This will be demonstrated by using the Rube Goldberg Machine (RGM) analogy. Mechanical RGMs will be introduced, and their relationship to conceptual biological RGMs explained. Exemplars of these biological RGMs and their evolution (e.g. introduction of mutations and recombination-like inversions) will be demonstrated using block diagrams. The conceptual biological RGM will then be mapped to an artificial vascular system, which can be modeled using microfluidic-like structures. Theoretical expectations will be presented, particularly regarding whether or not maximum intermediate steps equates to the rescue or reuse of traits compromised by previous mutations or inversions. Considerations for future work and applications will then be discussed.
Example of the "cooption" and "inversion" scenarios described further in the paper.
This paper introduces a new metaphor for understanding non-optimality in the evolution of complex traits. This metaphor is inspired by the
mechanical Rube Goldberg machine (or mechanical RGM as I characterize it in the paper).
In mapping this metaphor to biological systems, I introduce the concept of "maximum intermediate steps" in the function of a trait (traits that are not optimized with respect to evolution, structure, or function), which can be characterized by biological versions of the RGM. I have written about this idea in one of the first posts in this blog, but this is a more formal computational look at how this might work in nature.
As always, I would appreciate feedback.