Describing the Complexity of Systems: Multivariable "Set Complexity" and the Information Basis of Systems Biology
Describing the Complexity of Systems: Multivariable "Set Complexity" and the Information Basis of Systems Biology
复制标题
DOI：
10.1089/cmb.2013.0039
复制
发表时间：
2014-02-01
影响因子：
1.7
通讯作者：
Ignac, Tomasz
中科院分区：
文献类型：
--
作者：
Galas, David J.;Sakhanenko, Nikita A.;Ignac, Tomasz
上下文依赖性是描述复杂性的核心。基于集合复杂性的成对定义，我们使用信息论的方法来制定系统复杂性的一般措施。我们研究的多变量依赖性的概念开始的相互作用信息的属性。然后，我们提出了一个新的措施，无偏检测的多变量依赖，差分交互信息。这两个变量的数量减少到以前提出的作为生物系统中的信息的上下文相关的措施的成对集合的复杂性。我们在这里将其推广到任意数量的变量。微分相互作用信息的临界极限性质是推广的关键。这种测量方法扩展了以前关于生物信息的想法，并为复杂性的研究提供了更复杂的基础。微分相互作用信息的性质也提出了新的数据分析方法。给定系统测量的数据集，差分交互信息可以提供集体依赖性的度量，其可以用描述复杂系统交互模式的超图来表示。我们调查这种分析使用模拟数据集。广义集的复杂性措施，多变量依赖分析，超图的结合是我们的中心结果。虽然我们的重点是复杂的生物系统，但我们的结果适用于任何复杂的系统。
Context dependence is central to the description of complexity. Keying on the pairwise definition of set complexity, we use an information theory approach to formulate general measures of systems complexity. We examine the properties of multivariable dependency starting with the concept of interaction information. We then present a new measure for unbiased detection of multivariable dependency, differential interaction information. This quantity for two variables reduces to the pairwise set complexity previously proposed as a context-dependent measure of information in biological systems. We generalize it here to an arbitrary number of variables. Critical limiting properties of the differential interaction information are key to the generalization. This measure extends previous ideas about biological information and provides a more sophisticated basis for the study of complexity. The properties of differential interaction information also suggest new approaches to data analysis. Given a data set of system measurements, differential interaction information can provide a measure of collective dependence, which can be represented in hypergraphs describing complex system interaction patterns. We investigate this kind of analysis using simulated data sets. The conjoining of a generalized set complexity measure, multivariable dependency analysis, and hypergraphs is our central result. While our focus is on complex biological systems, our results are applicable to any complex system.