Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX, 77843, USA.
Department of Biology, East Carolina University, Greenville, NC 27858, USA.
Integr Comp Biol. 2022 Feb 5;61(6):2095-2108. doi: 10.1093/icb/icab169.
The incredible complexity of biological processes across temporal and spatial scales hampers defining common underlying mechanisms driving the patterns of life. However, recent advances in sequencing, big data analysis, machine learning, and molecular dynamics simulation have renewed the hope and urgency of finding potential hidden rules of life. There currently exists no framework to develop such synoptic investigations. Some efforts aim to identify unifying rules of life across hierarchical levels of time, space, and biological organization, but not all phenomena occur across all the levels of these hierarchies. Instead of identifying the same parameters and rules across levels, we posit that each level of a temporal and spatial scale and each level of biological organization has unique parameters and rules that may or may not predict outcomes in neighboring levels. We define this neighborhood, or the set of levels, across which a rule functions as the zone of influence. Here, we introduce the zone of influence framework and explain using three examples: (a) randomness in biology, where we use a Poisson process to describe processes from protein dynamics to DNA mutations to gene expressions, (b) island biogeography, and (c) animal coloration. The zone of influence framework may enable researchers to identify which levels are worth investigating for a particular phenomenon and reframe the narrative of searching for a unifying rule of life to the investigation of how, when, and where various rules of life operate.
生物过程在时间和空间尺度上的难以置信的复杂性，阻碍了确定驱动生命模式的共同潜在机制。然而，测序、大数据分析、机器学习和分子动力学模拟的最新进展，重新燃起了寻找潜在生命隐藏规则的希望和紧迫性。目前还没有制定这种综合研究的框架。一些努力旨在确定跨越时间、空间和生物组织层次的统一生命规则，但并非所有现象都发生在这些层次的所有层次上。我们不是在不同层次上确定相同的参数和规则，而是假设时间和空间尺度的每个层次以及每个生物组织层次都有独特的参数和规则，这些规则可能会或可能不会预测相邻层次的结果。我们将这个邻域或规则起作用的层次集定义为影响范围。在这里，我们引入影响范围框架，并通过三个例子进行解释：（a）生物学中的随机性，我们使用泊松过程来描述从蛋白质动力学到 DNA 突变再到基因表达的过程；（b）岛屿生物地理学；（c）动物颜色。影响范围框架可以使研究人员能够确定哪些层次值得为特定现象进行调查，并重新构建寻找统一生命规则的叙述，以调查各种生命规则何时以及在何处起作用。
