生物复杂性的“机制”：理解生物系统中的非最优结构
The "Machinery" of Biocomplexity: understanding non-optimal architectures in biological systems
摘要：
关于生物系统的一个流行假设是，性状已经进化到相对于功能进行优化。这是进化计算中的一个标准目标，虽然在生物科学中并不总是被接受，但它是当适应性最大化时会发生什么的基本假设。这意味着信号通路或遗传学应显示出使产生生化产物或表型适应所需的步骤数最小化的证据。在本文中，它将表明，“最大中间步骤”的原则也可能表征复杂的生物系统，特别是那些极端的历史偶然性或突变和重组的组合是关键特征。对现有文献的贡献是双重的：展示了具有“逼真”属性的工程系统中的非最优性的潜力，以及自然主义背景下的非最优性的基础。这将通过使用Rube Goldberg Machine（RGM）类比来证明。机械RGMs将被介绍，以及它们与概念生物RGMs的关系。这些生物RGMs及其演化（例如引入突变和重组样反转）的示例将使用框图和与复杂网络（称为卷积架构）的互连来演示。然后，概念生物RGM将被映射到人工血管系统，该系统可以使用类似微流体的结构进行建模。理论上的期望，特别是关于最大的中间步骤是否等于拯救或重用的性状损害以前的突变或倒位。然后将讨论未来工作和应用的考虑因素，包括将这种卷积架构纳入复杂网络。
英文摘要：
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.
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