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

TL;DR

This paper challenges the assumption that biological systems are optimized for efficiency, proposing that complex systems may instead be characterized by a principle of maximum intermediate steps, highlighting non-optimality in natural and engineered systems.

Contribution

It demonstrates the potential for non-optimal architectures in biological systems through the analogy of Rube Goldberg Machines and explores their implications for understanding biological complexity.

Findings

Biological systems may follow a principle of maximum intermediate steps.

Non-optimality can arise from mutation and recombination processes.

Engineered 'lifelike' systems can also exhibit non-optimal architectures.

Abstract

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.