General Form for Interaction Measures and Framework for Deriving Higher-Order Emergent Effects
General Form for Interaction Measures and Framework for Deriving Higher-Order Emergent Effects
复制标题
互动措施的通用形式和产生高阶紧急效应的框架
DOI：
10.3389/fevo.2018.00166
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发表时间：
2018
影响因子：
3
通讯作者：
V. Savage
中科院分区：
文献类型：
--
作者：
Elif Tekin;P. Yeh;V. Savage
相互作用是普遍存在的，并已在许多生态，进化和生理系统中进行了广泛的研究。各种测量方差分析，协方差，上位加性，互信息，联合累积量，布利斯独立性，存在计算跨领域的相互作用。然而，这些并不是在一个单一的一般框架内讨论和得出的。这种缺失的框架可能会导致对高阶相互作用的正确表述和解释的混乱。有趣的是，尽管高阶相互作用很少受到关注，但最近发现它们非常普遍，并可能影响复杂生物系统的动态。在这里，我们引入一个单一的，明确的数学框架，同时包括所有这些措施的成对相互作用。这个框架的一般性和简单性使我们能够建立一个严格的方法，用于推导高阶相互作用的措施的基础上，任何成对的相互作用上面列出的。这些广义的高阶相互作用度量使得能够探索跨系统的涌现现象，例如多重捕食者效应、基因上位性和环境压力源。这些结果为更好地解释相互作用如何影响生物系统提供了机制基础。我们的理论进步为理解复杂系统中的多组分相互作用提供了基础，例如生态系统或社区内不断演变的种群。
Interactions are ubiquitous and have been extensively studied in many ecological, evolutionary, and physiological systems. A variety of measures—ANOVA, covariance, epistatic additivity, mutual information, joint cumulants, Bliss independence—exist that compute interactions across fields. However, these are not discussed and derived within a single, general framework. This missing framework likely contributes to the confusion about proper formulations and interpretations of higher-order interactions. Intriguingly, despite higher-order interactions having received little attention, they have been recently discovered to be highly prevalent and to likely impact the dynamics of complex biological systems. Here, we introduce a single, explicit mathematical framework that simultaneously encompasses all of these measures of pairwise interactions. The generality and simplicity of this framework allows us to establish a rigorous method for deriving higher-order interaction measures based on any of the pairwise interactions listed above. These generalized higher-order interaction measures enable the exploration of emergent phenomena across systems such as multiple predator effects, gene epistasis, and environmental stressors. These results provide a mechanistic basis to better account for how interactions affect biological systems. Our theoretical advance provides a foundation for understanding multi-component interactions in complex systems such as evolving populations within ecosystems or communities.
影响因子：
6.8
作者：
Chen, C. Y.;Hathaway, K. M.;Folt, C. L.
通讯作者：
Folt, C. L.
DOI：
10.1016/j.tig.2014.09.001
发表时间：
2015-01
期刊：
Trends in genetics : TIG
影响因子：
--
作者：
Taylor MB;Ehrenreich IM
通讯作者：
Ehrenreich IM