The"Machinery"of Biocomplexity: understanding non-optimal architectures in biological systems

Introduction In the early 20 th century, a cartoonist named Rube Goldberg drew a series of cartoons that featured “absurd” machines. An archetypical Rube Goldberg Machine (RGM) performs a simple task such as flipping a light switch using as many intermediate steps as possible. These intermediate steps are linked together in a serial fashion, so that each preceding step triggers all subsequent steps. The many and varied creations of Rube Goldberg have also inspired engineering tournaments that treat such designs as a curiosity [1] (see Figure 1). The winning creations are judged more in terms of creative value rather than their efficiency. In the realm of biological systems and evolutionary biology, however, we find that functioning systems are often not optimal in terms of their form and/or function. The principle of 1 Orthogonal Research, Champaign, IL. USA 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. Exemplars of these biological RGMs and their evolution (e.g. introduction of mutations and recombination-like inversions) will be demonstrated using block diagrams and interconnections with complex networks (called convolution architectures). 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, including the incorporation of such convolution architectures into complex networks.