Biological Information as Set-Based Complexity.
Biological Information as Set-Based Complexity.
复制标题
生物信息作为基于集合的复杂性。
DOI：
10.1109/tit.2009.2037046
复制
发表时间：
2010-02
影响因子：
2.5
通讯作者：
Shmulevich I
中科院分区：
文献类型：
--
作者：
Galas DJ;Nykter M;Carter GW;Price ND;Shmulevich I
存在于生命系统的分子和结构中的所有潜在信息中，有多少是重要的或对系统有意义的，这一点并不明显。例如，随机序列或相同重复序列的集合对细胞的有用信息贡献很少或没有。这个信息的定量问题很重要，因为生物意义信息的潮起潮落对我们对生物功能和进化的定量理解至关重要。受到这些生物信息问题的特别激励，我们在这里提出了一类基于Kolmogorov内在复杂性的度量来量化对象集合中信息的上下文性质。这样的措施既不考虑随机信息，也不考虑冗余信息，并且不需要定义状态空间来量化信息，这是固有的。这种新度量的最大化可以用普遍信息距离来表述，它似乎具有几个有用和有趣的性质，我们用例子来说明其中的一些性质。
It is not obvious what fraction of all the potential information residing in the molecules and structures of living systems is significant or meaningful to the system. Sets of random sequences or identically repeated sequences, for example, would be expected to contribute little or no useful information to a cell. This issue of quantitation of information is important since the ebb and flow of biologically significant information is essential to our quantitative understanding of biological function and evolution. Motivated specifically by these problems of biological information, we propose here a class of measures to quantify the contextual nature of the information in sets of objects, based on Kolmogorov's intrinsic complexity. Such measures discount both random and redundant information and are inherent in that they do not require a defined state space to quantify the information. The maximization of this new measure, which can be formulated in terms of the universal information distance, appears to have several useful and interesting properties, some of which we illustrate with examples.