生物网络

来自集智百科 - 复杂系统|人工智能|复杂科学|复杂网络|自组织
Vicky（讨论 | 贡献）2021年2月18日 (四) 14:41的版本
跳到导航 跳到搜索

本词条由Ryan初步翻译，由和光同尘审校

Network science
模板:CSS image crop
模板:Hlist
Network types
Graphs
Features
Types
模板:Hlist
Models
Topology
Dynamics
模板:Hlist

模板:Refimprove

A biological network is any network that applies to biological systems. A network is any system with sub-units that are linked into a whole, such as species units linked into a whole food web. Biological networks provide a mathematical representation of connections found in ecological, evolutionary, and physiological studies, such as neural networks.[1] The analysis of biological networks with respect to human diseases has led to the field of network medicine.[2][3]

A biological network is any network that applies to biological systems. A network is any system with sub-units that are linked into a whole, such as species units linked into a whole food web. Biological networks provide a mathematical representation of connections found in ecological, evolutionary, and physiological studies, such as neural networks. The analysis of biological networks with respect to human diseases has led to the field of network medicine.

我们称生物系统 biological systems中的各个网络为 生物网络 biological network。网络是具有可连接成一个整体的子单元的系统，如由单个物种连接成的食物链网 food web。生物网络为人们在生态学、进化学和生理学研究中发现的联系提供了数学表达方式，例如 神经网络 neural networks这一说法的出现。[1]生物网络分析方法同样应用于人类疾病研究，这开创了网络医学 network medicine[4][5] 。


Network biology and bioinformatics

网络生物学和生物信息学

Complex biological systems may be represented and analyzed as computable networks. For example, ecosystems can be modeled as networks of interacting species or a protein can be modeled as a network of amino acids. Breaking a protein down farther, amino acids can be represented as a network of connected atoms, such as carbon, nitrogen, and oxygen. Nodes and edges are the basic components of a network. Nodes represent units in the network, while edges represent the interactions between the units. Nodes can represent a wide array of biological units, from individual organisms to individual neurons in the brain. Two important properties of a network are degree and betweenness centrality. Degree (or connectivity, a distinct usage from that used in graph theory) is the number of edges that connect a node, while betweenness is a measure of how central a node is in a network.[6] Nodes with high betweenness essentially serve as bridges between different portions of the network (i.e. interactions must pass through this node to reach other portions of the network). In social networks, nodes with high degree or high betweenness may play important roles in the overall composition of a network.

Complex biological systems may be represented and analyzed as computable networks. For example, ecosystems can be modeled as networks of interacting species or a protein can be modeled as a network of amino acids. Breaking a protein down farther, amino acids can be represented as a network of connected atoms, such as carbon, nitrogen, and oxygen. Nodes and edges are the basic components of a network. Nodes represent units in the network, while edges represent the interactions between the units. Nodes can represent a wide array of biological units, from individual organisms to individual neurons in the brain. Two important properties of a network are degree and betweenness centrality. Degree (or connectivity, a distinct usage from that used in graph theory) is the number of edges that connect a node, while betweenness is a measure of how central a node is in a network. Nodes with high betweenness essentially serve as bridges between different portions of the network (i.e. interactions must pass through this node to reach other portions of the network). In social networks, nodes with high degree or high betweenness may play important roles in the overall composition of a network.

复杂的生物系统能用可运算网络来表示和分析。例如，生态系统可以被模拟为相互作用的物种网络，一个蛋白质可以被模拟为氨基酸网络。如果进一步分解蛋白质，氨基酸还可以表示为一个由相互连接的原子构成的网络，例如碳、氮和氧。 节点 nodes和边 edges是网络的基本组成部分。节点表示网络中的单元，边表示单元之间的相互作用。节点可以代表广泛的生物单元——从单个的生物体到大脑中单个的神经元 neurons。网络的两个重要性质是度 degree和 介数中心性 betweenness centrality。度(或连通性，并非图论 graph theory中所使用的)是连接一个节点的边的数量，而介数中心性是衡量一个顶点在网络中有多么靠近中心位置[6]。本质上，具有高介数的节点充当的是网络不同部分之间的桥梁(即网络各个部分的交互必须通过这个节点发生)。在社会网络中，具有较高度值和较高介数的节点可以在网络的整体组成中发挥重要作用。


As early as the 1980s, researchers started viewing DNA or genomes as the dynamic storage of a language system with precise computable finite states represented as a finite state machine.[7] Recent complex systems research has also suggested some far-reaching commonality in the organization of information in problems from biology, computer science, and physics, such as the Bose–Einstein condensate (a special state of matter).[8]

As early as the 1980s, researchers started viewing DNA or genomes as the dynamic storage of a language system with precise computable finite states represented as a finite state machine. Recent complex systems research has also suggested some far-reaching commonality in the organization of information in problems from biology, computer science, and physics, such as the Bose–Einstein condensate (a special state of matter).

早在20世纪80年代，研究人员就开始将 DNA 或基因组genomes视为一个可存储动态信息的语言系统——因其具有可精确运算的有限状态，所以被看做一个有限状态机finite state machine[9]。近期的复杂系统 complex system研究还表明，从信息组织的角度来看，一些生物学biology、计算机科学 computer science和物理学 physics等领域的问题存在一些极有价值的共性，例如玻色-爱因斯坦凝聚态Bose–Einstein condensate (一种特殊的物质状态) [10]。


Bioinformatics has increasingly shifted its focus from individual genes, proteins, and search algorithms to large-scale networks often denoted as -omes such as biome, interactome, genome and proteome. Such theoretical studies have revealed that biological networks share many features with other networks such as the Internet or social networks, e.g. their network topology.

Bioinformatics has increasingly shifted its focus from individual genes, proteins, and search algorithms to large-scale networks often denoted as -omes such as biome, interactome, genome and proteome. Such theoretical studies have revealed that biological networks share many features with other networks such as the Internet or social networks, e.g. their network topology.

生物信息学已逐渐将关注点从单个基因、蛋白质和搜索算法转移到像生物组 biome、交互组 interactome、基因组和蛋白组 proteome这样的大规模网络。这些理论研究揭示了生物网络与其他网络——如互联网或社交网络——有许多共同特征，例如他们的网络拓扑结构 network topology。


Networks in biology

生物学中的网络


Protein–protein interaction networks

蛋白质-蛋白质互作网络 模板:Main article

Many protein–protein interactions (PPIs) in a cell form protein interaction networks (PINs) where proteins are nodes and their interactions are edges.[11] PINs are the most intensely analyzed networks in biology. There are dozens of PPI detection methods to identify such interactions. The yeast two-hybrid system is a commonly used experimental technique for the study of binary interactions.[12]

Many protein–protein interactions (PPIs) in a cell form protein interaction networks (PINs) where proteins are nodes and their interactions are edges. PINs are the most intensely analyzed networks in biology. There are dozens of PPI detection methods to identify such interactions. The yeast two-hybrid system is a commonly used experimental technique for the study of binary interactions.

在细胞中，大量的蛋白质间相互作用 protein-protein interactions(PPIs)形成了蛋白质相互作用网络 protein interaction networks(PINs)，其中蛋白质是节点，它们的相互作用是边[13]。蛋白质互作网络是生物学中人们分析的最深入的网络。现有几十种基于PPIs的检测方法被用于识别蛋白质间的相互作用，酵母双杂交系统 yeast two-hybrid System是一种研究二元相互作用的常用实验技术[14] 。


Recent studies have indicated conservation of molecular networks through deep evolutionary time.[15] Moreover, it has been discovered that proteins with high degrees of connectedness are more likely to be essential for survival than proteins with lesser degrees.[16] This suggests that the overall composition of the network (not simply interactions between protein pairs) is important for the overall functioning of an organism.

Recent studies have indicated conservation of molecular networks through deep evolutionary time. Moreover, it has been discovered that proteins with high degrees of connectedness are more likely to be essential for survival than proteins with lesser degrees. This suggests that the overall composition of the network (not simply interactions between protein pairs) is important for the overall functioning of an organism.

近年来的研究表明，分子网络 molecular networks在深层进化过程中是保守的（在进化过程中的改变较少）[17]。此外，相较于低度值的蛋白质，具有高度值的蛋白质对物种的生存可能更加重要[18]。这表明网络如何组成(不仅仅是简单的蛋白质之间的相互作用)对于有机体的整体功能具有重要影响。


Gene regulatory networks (DNA–protein interaction networks)

基因调控网络（DNA-蛋白质交互网络）

模板:Main article

The activity of genes is regulated by transcription factors, proteins that typically bind to DNA. Most transcription factors bind to multiple binding sites in a genome. As a result, all cells have complex gene regulatory networks. For instance, the human genome encodes on the order of 1,400 DNA-binding transcription factors that regulate the expression of more than 20,000 human genes.[19] Technologies to study gene regulatory networks include ChIP-chip, ChIP-seq, CliP-seq, and others.

The activity of genes is regulated by transcription factors, proteins that typically bind to DNA. Most transcription factors bind to multiple binding sites in a genome. As a result, all cells have complex gene regulatory networks. For instance, the human genome encodes on the order of 1,400 DNA-binding transcription factors that regulate the expression of more than 20,000 human genes. Technologies to study gene regulatory networks include ChIP-chip, ChIP-seq, CliP-seq, and others.

与DNA结合的蛋白质中，负责管理基因的转录因子 transcription factors是十分典型的一类。大多数转录因子可以与基因组中的位点结合。因此，所有的细胞都有复杂的基因调控网络 gene regulatory Networks。例如，人类基因组编码出1400个左右可与DNA结合的转录因子，它们调节超20000个人类基因的表达。研究基因调控网络的技术包括 ChIP-chip、 ChIP-seq、 CliP-seq 等。


Gene co-expression networks (transcript–transcript association networks)

基因共表达网络（转录-转录关联网络） 模板:Main article 

 --～～～Gene co-expression networks  基因共表达网络

Gene co-expression networks can be interpreted as association networks between variables that measure transcript abundances. These networks have been used to provide a systems biologic analysis of DNA microarray data, RNA-seq data, miRNA data etc.

Gene co-expression networks can be interpreted as association networks between variables that measure transcript abundances. These networks have been used to provide a systems biologic analysis of DNA microarray data, RNA-seq data, miRNA data etc.

基因共表达网络 Gene co-expression networks 是一种变量之间的关系网络，用于衡量转录丰度transcript abundances。这些网络被用来为DNA微阵列数据DNA microarray data、 RNA-seq数据 RNA-seq data、 miRNA数据 miRNA data等分析提供系统生物学支持。

weighted gene co-expression network analysis is widely used to identify co-expression modules and intramodular hub genes. Co-expression modules may correspond to cell types or pathways. Highly connected intramodular hubs can be interpreted as representatives of their respective module.

weighted gene co-expression network analysis is widely used to identify co-expression modules and intramodular hub genes. Co-expression modules may correspond to cell types or pathways. Highly connected intramodular hubs can be interpreted as representatives of their respective module.

加权基因表达网络分析 weighted gene co-expression network analysis 被广泛应用于鉴定共表达模块co-expression modules以及模块内的核心基因。共表达模块可能会对应细胞类型或病症通路。模块内四通八达的枢纽可以被视作本模块内其他基因的代表，显示出这些基因共有的特点。

Metabolic networks

代谢网络 模板:Main article

The chemical compounds of a living cell are connected by biochemical reactions which convert one compound into another. The reactions are catalyzed by enzymes. Thus, all compounds in a cell are parts of an intricate biochemical network of reactions which is called metabolic network. It is possible to use network analyses to infer how selection acts on metabolic pathways.[6]

The chemical compounds of a living cell are connected by biochemical reactions which convert one compound into another. The reactions are catalyzed by enzymes. Thus, all compounds in a cell are parts of an intricate biochemical network of reactions which is called metabolic network. It is possible to use network analyses to infer how selection acts on metabolic pathways.

化合物在活细胞中会发生大量生物化学反应，这使得化合物发生转变。这些反应是由酶 enzymes催化的。因此，细胞中所有的化合物都是一个复杂生化反应网络的一部分，该网络被称为代谢网络 metabolic network。我们可以使用网络分析方法来推断“筛选”是如何影响代谢通路的[6]。


Signaling networks

信号网络 模板:Main article

Signals are transduced within cells or in between cells and thus form complex signaling networks. For instance, in the MAPK/ERK pathway is transduced from the cell surface to the cell nucleus by a series of protein–protein interactions, phosphorylation reactions, and other events. Signaling networks typically integrate protein–protein interaction networks, gene regulatory networks, and metabolic networks.

Signals are transduced within cells or in between cells and thus form complex signaling networks. For instance, in the MAPK/ERK pathway is transduced from the cell surface to the cell nucleus by a series of protein–protein interactions, phosphorylation reactions, and other events. Signaling networks typically integrate protein–protein interaction networks, gene regulatory networks, and metabolic networks.

信号在细胞内或细胞之间传递，形成了复杂的信号网络。例如，MAPK/ERK 通路 MAPK/ERK pathway通过一系列蛋白质之间的相互作用、磷酸化反应 phosphorylation reactions和其他事件将信号从细胞表面传递到细胞核cell nucleus内。信号网络 signaling networks通常包含蛋白质相互作用网络、基因调控网络 gene regulatory networks和代谢网络 metabolic networks。


Neuronal networks

神经网络 模板:Main article

The complex interactions in the brain make it a perfect candidate to apply network theory. Neurons in the brain are deeply connected with one another and this results in complex networks being present in the structural and functional aspects of the brain.[20] For instance, small-world network properties have been demonstrated in connections between cortical areas of the primate brain[21] or during swallowing in humans.[22] This suggests that cortical areas of the brain are not directly interacting with each other, but most areas can be reached from all others through only a few interactions.

The complex interactions in the brain make it a perfect candidate to apply network theory. Neurons in the brain are deeply connected with one another and this results in complex networks being present in the structural and functional aspects of the brain. For instance, small-world network properties have been demonstrated in connections between cortical areas of the primate brain or during swallowing in humans. This suggests that cortical areas of the brain are not directly interacting with each other, but most areas can be reached from all others through only a few interactions.

大脑中的神经元之间有着复杂的相互作用，这使其成为网络理论应用的绝佳场合。大脑中的神经元联系紧密，其相互交织形成的复杂网络是人脑结构和功能的基础[23]。例如，灵长类动物大脑皮层各区域之间的连接[24]或者人类吞咽时神经网络的行为都具有小世界网络small-world network 属性[25]。这表明大脑皮层各区域并不直接相互作用，大部分通过少量节点区域相互沟通。

Food webs

食物链网络 模板:Main article

All organisms are connected to each other through feeding interactions. That is, if a species eats or is eaten by another species, they are connected in an intricate food web of predator and prey interactions. The stability of these interactions has been a long-standing question in ecology.[26] That is to say, if certain individuals are removed, what happens to the network (i.e. does it collapse or adapt)? Network analysis can be used to explore food web stability and determine if certain network properties result in more stable networks. Moreover, network analysis can be used to determine how selective removals of species will influence the food web as a whole.[27] This is especially important considering the potential species loss due to global climate change.

All organisms are connected to each other through feeding interactions. That is, if a species eats or is eaten by another species, they are connected in an intricate food web of predator and prey interactions. The stability of these interactions has been a long-standing question in ecology. That is to say, if certain individuals are removed, what happens to the network (i.e. does it collapse or adapt)? Network analysis can be used to explore food web stability and determine if certain network properties result in more stable networks. Moreover, network analysis can be used to determine how selective removals of species will influence the food web as a whole. This is especially important considering the potential species loss due to global climate change.

生物可以通过进食相互作用产生关联[28]。也就是说，不管一个生物成为食物还是找到食物，它都会处于一个复杂的食物网 food web中——在网中捕食者与被捕食者相互作用。这些相互作用的稳定性长期困扰着生态学家们。具体来说，人们想知道，某些个体被移除后会发生什么(即它是崩溃还是适应) ？网络分析方法可以用来研究食物网的稳定性，并确定某些网络特性是否会让网络更稳定。此外，网络分析方法还可以用来研究物种的选择性迁移如何影响整个食物网[29]。考虑到全球气候变化可能导致大量物种消失，运用网络分析方法来研究食物链网的特性是十分重要的。


Between-species interaction networks

物种间交互网络 模板:Main article

In biology, pairwise interactions have historically been the focus of intense study. With the recent advances in network science, it has become possible to scale up pairwise interactions to include individuals of many species involved in many sets of interactions to understand the structure and function of larger ecological networks.[30] The use of network analysis can allow for both the discovery and understanding how these complex interactions link together within the system’s network, a property which has previously been overlooked. This powerful tool allows for the study of various types of interactions (from competitive to cooperative) using the same general framework.[31] For example, plant-pollinator interactions are mutually beneficial and often involve many different species of pollinators as well as many different species of plants. These interactions are critical to plant reproduction and thus the accumulation of resources at the base of the food chain for primary consumers, yet these interaction networks are threatened by anthropogenic change. The use of network analysis can illuminate how pollination networks work and may in turn inform conservation efforts.[32] Within pollination networks, nestedness (i.e., specialists interact with a subset of species that generalists interact with), redundancy (i.e., most plants are pollinated by many pollinators), and modularity play a large role in network stability.[32][33] These network properties may actually work to slow the spread of disturbance effects through the system and potentially buffer the pollination network from anthropogenic changes somewhat.[33] More generally, the structure of species interactions within an ecological network can tell us something about the diversity, richness, and robustness of the network.[34] Researchers can even compare current constructions of species interactions networks with historical reconstructions of ancient networks to determine how networks have changed over time.[35] Recent research into these complex species interactions networks is highly concerned with understanding what factors (e.g., diversity) lead to network stability.[36]

In biology, pairwise interactions have historically been the focus of intense study. With the recent advances in network science, it has become possible to scale up pairwise interactions to include individuals of many species involved in many sets of interactions to understand the structure and function of larger ecological networks. The use of network analysis can allow for both the discovery and understanding how these complex interactions link together within the system’s network, a property which has previously been overlooked. This powerful tool allows for the study of various types of interactions (from competitive to cooperative) using the same general framework. For example, plant-pollinator interactions are mutually beneficial and often involve many different species of pollinators as well as many different species of plants. These interactions are critical to plant reproduction and thus the accumulation of resources at the base of the food chain for primary consumers, yet these interaction networks are threatened by anthropogenic change. The use of network analysis can illuminate how pollination networks work and may in turn inform conservation efforts. Within pollination networks, nestedness (i.e., specialists interact with a subset of species that generalists interact with), redundancy (i.e., most plants are pollinated by many pollinators), and modularity play a large role in network stability. These network properties may actually work to slow the spread of disturbance effects through the system and potentially buffer the pollination network from anthropogenic changes somewhat. Researchers can even compare current constructions of species interactions networks with historical reconstructions of ancient networks to determine how networks have changed over time. Recent research into these complex species interactions networks is highly concerned with understanding what factors (e.g., diversity) lead to network stability.

在生物学中，成对相互作用 pairwise interactions 历来是研究重点。随着近几年网络科学的发展，我们可以进一步丰富成对相互作用这一概念，其外延甚至能涵盖有许多物种参与的多组相互作用，进而帮助我们理解生态网络 ecological networks 的结构和功能[37]。运用网络分析方法可以帮助我们理解这些复杂的交互是如何连接成网络的——这是一个之前经常被忽略的过程。这个强大的工具让处于同一普适框架下的各类型的相互作用(从竞争到合作)研究成为可能[38]。例如，植物与传粉者之间的相互作用是互惠互利的，这通常会涉及到许多不同种类的传粉者以及许多不同种类的植物。这些相互作用对植物生殖和食物链底层初级消费者的资源积累来说至关重要，然而这些相互作用网络正受到人为变化的威胁。网络分析方法的使用可以说明授粉网络是如何工作的，反过来也可以为相关植物保护工作提供必要信息[32]。在授粉网络中，内嵌性 nestedness (某一领域的植物被少数昆虫授粉，剩下的昆虫只对少数几种植物授粉)、冗余性redundancy (大多数植物是由许多授粉者授粉的)和模块性 modularity（特定的物种之间有着紧密的连接，由此形成一个个团簇，也可称之为“模块”）在网络稳定性中扮演着重要角色[32][33]。实际上，这些网络属性不仅可以减缓干扰效应在系统中的传播，还可能会缓冲授粉网络受到的人造变化的影响[39]。研究人员甚至可以将物种相互作用网络的现有结构与过去结构进行比较，以确定网络在时间尺度上是如何演化的[40]。最近对这些复杂物种相互作用网络的研究集中于分析是什么因素(如多样性diversity)造就了网络的稳定性[41]。


Within-species interaction networks

物种内交互网络 模板:Main article

Network analysis provides the ability to quantify associations between individuals, which makes it possible to infer details about the network as a whole at the species and/or population level.[42] One of the most attractive features of the network paradigm would be that it provides a single conceptual framework in which the social organisation of animals at all levels (individual, dyad, group, population) and for all types of interaction (aggressive, cooperative, sexual etc.) can be studied.[43]

Network analysis provides the ability to quantify associations between individuals, which makes it possible to infer details about the network as a whole at the species and/or population level. One of the most attractive features of the network paradigm would be that it provides a single conceptual framework in which the social organisation of animals at all levels (individual, dyad, group, population) and for all types of interaction (aggressive, cooperative, sexual etc.) can be studied.

网络分析方法可以量化个体之间的关联，我们由此得以在物种和/或种群水平推断整个网络的细节[44]。网络范式最吸引人的特征之一就是，它提供了一个统一的概念框架，各层次(单，双，组，群)的动物和各类型的互动中(攻击，合作，性等等)都被囊括其中[45] 。


Researchers interested in ethology across a multitude of taxa, from insects to primates, are starting to incorporate network analysis into their research. Researchers interested in social insects (e.g., ants and bees) have used network analyses to better understand division of labor, task allocation, and foraging optimization within colonies;[46][47][48] Other researchers are interested in how certain network properties at the group and/or population level can explain individual level behaviors. Studies have demonstrated how animal social network structure can be influenced by factors ranging from characteristics of the environment to characteristics of the individual, such as developmental experience and personality. At the level of the individual, the patterning of social connections can be an important determinant of fitness, predicting both survival and reproductive success. At the population level, network structure can influence the patterning of ecological and evolutionary processes, such as frequency-dependent selection and disease and information transmission.[49] For instance, a study on wire-tailed manakins (a small passerine bird) found that a male’s degree in the network largely predicted the ability of the male to rise in the social hierarchy (i.e. eventually obtain a territory and matings).[50] In bottlenose dolphin groups, an individual’s degree and betweenness centrality values may predict whether or not that individual will exhibit certain behaviors, like the use of side flopping and upside-down lobtailing to lead group traveling efforts; individuals with high betweenness values are more connected and can obtain more information, and thus are better suited to lead group travel and therefore tend to exhibit these signaling behaviors more than other group members.[51]

Researchers interested in ethology across a multitude of taxa, from insects to primates, are starting to incorporate network analysis into their research. Researchers interested in social insects (e.g., ants and bees) have used network analyses to better understand division of labor, task allocation, and foraging optimization within colonies; Other researchers are interested in how certain network properties at the group and/or population level can explain individual level behaviors. Studies have demonstrated how animal social network structure can be influenced by factors ranging from characteristics of the environment to characteristics of the individual, such as developmental experience and personality. At the level of the individual, the patterning of social connections can be an important determinant of fitness, predicting both survival and reproductive success. At the population level, network structure can influence the patterning of ecological and evolutionary processes, such as frequency-dependent selection and disease and information transmission. For instance, a study on wire-tailed manakins (a small passerine bird) found that a male’s degree in the network largely predicted the ability of the male to rise in the social hierarchy (i.e. eventually obtain a territory and matings). In bottlenose dolphin groups, an individual’s degree and betweenness centrality values may predict whether or not that individual will exhibit certain behaviors, like the use of side flopping and upside-down lobtailing to lead group traveling efforts; individuals with high betweenness values are more connected and can obtain more information, and thus are better suited to lead group travel and therefore tend to exhibit these signaling behaviors more than other group members.

从昆虫到灵长类动物，对动物行为学 ethology感兴趣的研究人员开始将网络分析纳入到研究之中。对社会性昆虫(如蚂蚁和蜜蜂)感兴趣的研究人员利用网络分析来更好地理解群体内昆虫的分工、任务分配和最优化觅食 foraging optimization[52][53][54]; 其他研究人员感兴趣的点则在于群体和/或种群水平的某些网络特性如何影响个体行为。研究表明，环境特征和个体特征——如经验发展和人格等因素——会影响动物的社会网络结构。在个体层面上，社会联系的模式是个体适应性的一个关键因素，可能决定着个体生存和繁殖的成功概率。在种群水平上，网络结构可以影响生态和进化过程的模式，如频率依赖性选择、疾病、信息的传递[55]。例如，一项针对线尾 (一种小型雀形目鸟)的研究发现，网络中雄性个体的度值在很大程度上预测了雄性在社会等级中进阶的能力(即最终获得领地和交配机会的能力) [56]。在宽吻海豚群体中，个体的度值和介数中心性可以预测个体是否会表现出某些行为，比如使用侧翻和潜入水中尾巴举向空中来表达自己领导团队迁移的意愿; 介数值较高的个体与外界有更多的关联，可以获得更多信息，因此更适合领导团队迁行，因此比其他团队成员相比，它们更倾向于去传递这些信号[57]。


Social network analysis can also be used to describe the social organization within a species more generally, which frequently reveals important proximate mechanisms promoting the use of certain behavioral strategies. These descriptions are frequently linked to ecological properties (e.g., resource distribution). For example, network analyses revealed subtle differences in the group dynamics of two related equid fission-fusion species, Grevy’s zebra and onagers, living in variable environments; Grevy’s zebras show distinct preferences in their association choices when they fission into smaller groups, whereas onagers do not.[58] Similarly, researchers interested in primates have also utilized network analyses to compare social organizations across the diverse primate order, suggesting that using network measures (such as centrality, assortativity, modularity, and betweenness) may be useful in terms of explaining the types of social behaviors we see within certain groups and not others.[59]

Social network analysis can also be used to describe the social organization within a species more generally, which frequently reveals important proximate mechanisms promoting the use of certain behavioral strategies. These descriptions are frequently linked to ecological properties (e.g., resource distribution). For example, network analyses revealed subtle differences in the group dynamics of two related equid fission-fusion species, Grevy’s zebra and onagers, living in variable environments; Grevy’s zebras show distinct preferences in their association choices when they fission into smaller groups, whereas onagers do not. Similarly, researchers interested in primates have also utilized network analyses to compare social organizations across the diverse primate order, suggesting that using network measures (such as centrality, assortativity, modularity, and betweenness) may be useful in terms of explaining the types of social behaviors we see within certain groups and not others.

社会网络分析 social network analysis 也可以用来广泛描述物种内的社会组织，它经常揭示出那些能够促进某些行为策略使用的重要邻近机制。这些描述经常与生态属性(例如资源分配)联系在一起。例如，网络分析方法揭示了生活在变化环境中的两个相关等裂变融合物种——格雷维斑马和骑驴——的群体动力学的细微差异; 格雷维斑马在分裂为较小的群体时，在同伴选择上表现出明显的偏好，而骑驴则不然[60]。同样，对灵长类动物感兴趣的研究人员也利用网络分析方法来比较不同灵长类动物的社会组织，这表明以网络的视角进行测量(如集中性、协调性、模块性和中介性)可能有助于我们理解在某些特定群体中看到的社会行为[61] 。


Finally, social network analysis can also reveal important fluctuations in animal behaviors across changing environments. For example, network analyses in female chacma baboons (Papio hamadryas ursinus) revealed important dynamic changes across seasons which were previously unknown; instead of creating stable, long-lasting social bonds with friends, baboons were found to exhibit more variable relationships which were dependent on short-term contingencies related to group level dynamics as well as environmental variability.[62] Changes in an individual's social network environment can also influence characteristics such as 'personality': for example, social spiders that huddle with bolder neighbours tend to increase also in boldness.[63] This is a very small set of broad examples of how researchers can use network analysis to study animal behavior. Research in this area is currently expanding very rapidly, especially since the broader development of animal borne tags and computer vision that can be used to automate the collection of social associations.[64] Social network analysis is a valuable tool for studying animal behavior across all animal species, and has the potential to uncover new information about animal behavior and social ecology that was previously poorly understood.

Finally, social network analysis can also reveal important fluctuations in animal behaviors across changing environments. For example, network analyses in female chacma baboons (Papio hamadryas ursinus) revealed important dynamic changes across seasons which were previously unknown; instead of creating stable, long-lasting social bonds with friends, baboons were found to exhibit more variable relationships which were dependent on short-term contingencies related to group level dynamics as well as environmental variability. Changes in an individual's social network environment can also influence characteristics such as 'personality': for example, social spiders that huddle with bolder neighbours tend to increase also in boldness. This is a very small set of broad examples of how researchers can use network analysis to study animal behavior. Research in this area is currently expanding very rapidly, especially since the broader development of animal borne tags and computer vision that can be used to automate the collection of social associations. Social network analysis is a valuable tool for studying animal behavior across all animal species, and has the potential to uncover new information about animal behavior and social ecology that was previously poorly understood.

最后，社会网络分析还可以揭示动物行为在环境变化时的关键波动。例如，对雌性沙卡马狒狒的网络分析揭示了该群体中重要的跨季动态变化; 研究结果发现，狒狒之间没有建立稳定、持久的社会联系。恰恰相反，它们之间的关系是多变的。这些关系取决于动态群体层面发生的短期偶然事件以及环境的变化[65]。个体社交网络环境的变化也会影响个体的性格特点。例如，与胆大的邻居挤在一起的蜘蛛也会更加大胆[66]。关于运用网络分析方法研究动物行为的实践，以上展示的是一小部分研究成果。这一领域的研究目前正在迅速扩展。特别是随着动物身体标记技术 animal borne tags和计算机视觉技术computer vision的发展，个体社会关系的收集变得更加自动化[67]。社会网络分析是研究物种行为的有效工具，并且有可能帮助我们发现鲜为人知的与动物行为和社会生态相关的新信息。


See also

生物学中的组学

生物网络推理

应用统计学

生物统计学

计算生物学

系统生物学

权重相关性网络分析

交互组

网络医学


External links


References

  1. ↑ 1.0 1.1 Proulx, S. R.; Promislow, D. E. L.; Phillips, P. C. (2005). "Network thinking in ecology and evolution" (PDF). Trends in Ecology and Evolution. 20 (6): 345–353. doi:10.1016/j.tree.2005.04.004. PMID 16701391. Archived from the original (PDF) on 2011-08-15.
  2. ↑ Barabási, A. L.; Gulbahce, N.; Loscalzo, J. (2011). "Network medicine: a network-based approach to human disease". Nature Reviews Genetics. 12 (1): 56–68. doi:10.1038/nrg2918. PMC 3140052. PMID 21164525.
  3. ↑ Habibi, Iman; Emamian, Effat S.; Abdi, Ali (2014-10-07). "Advanced Fault Diagnosis Methods in Molecular Networks". PLOS ONE. 9 (10): e108830. Bibcode:2014PLoSO...9j8830H. doi:10.1371/journal.pone.0108830. ISSN 1932-6203. PMC 4188586. PMID 25290670.
  4. ↑ Barabási, A. L.; Gulbahce, N.; Loscalzo, J. (2011). "Network medicine: a network-based approach to human disease". Nature Reviews Genetics. 12 (1): 56–68. doi:10.1038/nrg2918. PMC 3140052. PMID 21164525.
  5. ↑ Habibi, Iman; Emamian, Effat S.; Abdi, Ali (2014-10-07). "Advanced Fault Diagnosis Methods in Molecular Networks". PLOS ONE. 9 (10): e108830. Bibcode:2014PLoSO...9j8830H. doi:10.1371/journal.pone.0108830. ISSN 1932-6203. PMC 4188586. PMID 25290670.
  6. ↑ 6.0 6.1 6.2 6.3 Proulx, S.R.; et al. (2005). "Network thinking in ecology and evolution". Trends in Ecology and Evolution. 20 (6): 345–353. doi:10.1016/j.tree.2005.04.004. PMID 16701391.
  7. ↑ Searls, D. (1993). Artificial intelligence and molecular biology. Cambridge, MA: MIT Press. 
  8. ↑ Bianconi, G.; Barabasi A. (2001). "Bose-Einstein condensation in complex networks". Phys. Rev. Lett. 86 (24): 5632–5635. arXiv:cond-mat/0011224. Bibcode:2001PhRvL..86.5632B. doi:10.1103/physrevlett.86.5632. hdl:2047/d20000696. PMID 11415319. {{cite journal}}: Unknown parameter |lastauthoramp= ignored (help)
  9. ↑ Searls, D. (1993). Artificial intelligence and molecular biology. Cambridge, MA: MIT Press. 
  10. ↑ Bianconi, G.; Barabasi A. (2001). "Bose-Einstein condensation in complex networks". Phys. Rev. Lett. 86 (24): 5632–5635. arXiv:cond-mat/0011224. Bibcode:2001PhRvL..86.5632B. doi:10.1103/physrevlett.86.5632. hdl:2047/d20000696. PMID 11415319. {{cite journal}}: Unknown parameter |lastauthoramp= ignored (help)
  11. ↑ Habibi, Iman; Emamian, Effat S.; Abdi, Ali (2014-01-01). "Quantitative analysis of intracellular communication and signaling errors in signaling networks". BMC Systems Biology. 8: 89. doi:10.1186/s12918-014-0089-z. ISSN 1752-0509. PMC 4255782. PMID 25115405.
  12. ↑ Mashaghi, A.; et al. (2004). "Investigation of a protein complex network". European Physical Journal. 41 (1): 113–121. arXiv:cond-mat/0304207. Bibcode:2004EPJB...41..113M. doi:10.1140/epjb/e2004-00301-0.
  13. ↑ Habibi, Iman; Emamian, Effat S.; Abdi, Ali (2014-01-01). "Quantitative analysis of intracellular communication and signaling errors in signaling networks". BMC Systems Biology. 8: 89. doi:10.1186/s12918-014-0089-z. ISSN 1752-0509. PMC 4255782. PMID 25115405.
  14. ↑ Mashaghi, A.; et al. (2004). "Investigation of a protein complex network". European Physical Journal. 41 (1): 113–121. arXiv:cond-mat/0304207. Bibcode:2004EPJB...41..113M. doi:10.1140/epjb/e2004-00301-0.
  15. ↑ Sharan, R.; et al. (2005). "Conserved patterns of protein interaction in multiple species". Proceedings of the National Academy of Sciences of the United States of America. 102 (6): 1974–1979. Bibcode:2005PNAS..102.1974S. doi:10.1073/pnas.0409522102. PMC 548573. PMID 15687504.
  16. ↑ Jeong, H.; et al. (2001). "Lethality and centrality in protein networks". Nature. 411 (6833): 41–42. arXiv:cond-mat/0105306. Bibcode:2001Natur.411...41J. doi:10.1038/35075138. PMID 11333967.
  17. ↑ Sharan, R.; et al. (2005). "Conserved patterns of protein interaction in multiple species". Proceedings of the National Academy of Sciences of the United States of America. 102 (6): 1974–1979. Bibcode:2005PNAS..102.1974S. doi:10.1073/pnas.0409522102. PMC 548573. PMID 15687504.
  18. ↑ Jeong, H.; et al. (2001). "Lethality and centrality in protein networks". Nature. 411 (6833): 41–42. arXiv:cond-mat/0105306. Bibcode:2001Natur.411...41J. doi:10.1038/35075138. PMID 11333967.
  19. ↑ Vaquerizas, J.-M.; et al. (2009). "A census of human transcription factors: function, expression and evolution". Nature Reviews Genetics. 10 (4): 252–263. doi:10.1038/nrg2538. PMID 19274049.
  20. ↑ Bullmore, E.; O. Sporns (2009). "Complex brain networks: graph theoretical analysis of structural and functional systems". Nature Reviews Neuroscience. 10 (3): 186–198. doi:10.1038/nrn2575. PMID 19190637. {{cite journal}}: Unknown parameter |lastauthoramp= ignored (help)
  21. ↑ Stephan, K.E.; et al. (2000). "Computational analysis of functional connectivity between areas of primate cerebral cortex". Philosophical Transactions of the Royal Society B. 355 (1393): 111–126. doi:10.1098/rstb.2000.0552. PMC 1692715. PMID 10703047.
  22. ↑ Jestrović, Iva; Coyle, James L; Perera, Subashan; Sejdić, Ervin (2016-12-01). "Functional connectivity patterns of normal human swallowing: difference among various viscosity swallows in normal and chin-tuck head positions". Brain Research. 1652: 158–169. doi:10.1016/j.brainres.2016.09.041. ISSN 0006-8993. PMC 5102805. PMID 27693396.
  23. ↑ Bullmore, E.; O. Sporns (2009). "Complex brain networks: graph theoretical analysis of structural and functional systems". Nature Reviews Neuroscience. 10 (3): 186–198. doi:10.1038/nrn2575. PMID 19190637. {{cite journal}}: Unknown parameter |lastauthoramp= ignored (help)
  24. ↑ Stephan, K.E.; et al. (2000). "Computational analysis of functional connectivity between areas of primate cerebral cortex". Philosophical Transactions of the Royal Society B. 355 (1393): 111–126. doi:10.1098/rstb.2000.0552. PMC 1692715. PMID 10703047.
  25. ↑ Jestrović, Iva; Coyle, James L; Perera, Subashan; Sejdić, Ervin (2016-12-01). "Functional connectivity patterns of normal human swallowing: difference among various viscosity swallows in normal and chin-tuck head positions". Brain Research. 1652: 158–169. doi:10.1016/j.brainres.2016.09.041. ISSN 0006-8993. PMC 5102805. PMID 27693396.
  26. ↑ MacArthur, R.H. (1955). "Fluctuations in animal populations and a measure of community stability". Ecology. 36 (3): 533–536. doi:10.2307/1929601. JSTOR 1929601.
  27. ↑ Dunne, J.A.; et al. (2002). "Network structure and biodiversity loss in food webs: robustness increases with connectance". Ecology Letters. 5 (4): 558–567. doi:10.1046/j.1461-0248.2002.00354.x.
  28. ↑ MacArthur, R.H. (1955). "Fluctuations in animal populations and a measure of community stability". Ecology. 36 (3): 533–536. doi:10.2307/1929601. JSTOR 1929601.
  29. ↑ Dunne, J.A.; et al. (2002). "Network structure and biodiversity loss in food webs: robustness increases with connectance". Ecology Letters. 5 (4): 558–567. doi:10.1046/j.1461-0248.2002.00354.x.
  30. ↑ Bascompte, J. (2009). "Disentangling the web of life". Science. 325 (5939): 416–419. Bibcode:2009Sci...325..416B. doi:10.1126/science.1170749. PMID 19628856.
  31. ↑ Krause, J.; et al. (2009). "Animal social networks: an introduction". Behav. Ecol. Sociobiol. 63 (7): 967–973. doi:10.1007/s00265-009-0747-0.
  32. ↑ 32.0 32.1 32.2 32.3 Memmott, J.; et al. (2004). "Tolerance of pollination networks to species extinctions". Philosophical Transactions of the Royal Society B. 271 (1557): 2605–261. doi:10.1098/rspb.2004.2909. PMC 1691904. PMID 15615687.
  33. ↑ 33.0 33.1 33.2 Olesen, J.; et al. (2007). "The modularity of pollination networks". PNAS. 104 (50): 19891–19896. Bibcode:2007PNAS..10419891O. doi:10.1073/pnas.0706375104. PMC 2148393. PMID 18056808.
  34. ↑ Campbell, V.; et al. (2011). "Experimental design and the outcome and interpretation of diversity-stability relations". Oikos. 120 (3): 399–408. doi:10.1111/j.1600-0706.2010.18768.x.
  35. ↑ Lotze, H.; et al. (2011). "Historical changes in marine resources, food-web structure and ecosystem functioning in the Adriatic Sea, Mediterranean". Ecosystems. 14 (2): 198–222. doi:10.1007/s10021-010-9404-8.
  36. ↑ Romanuk, T.; et al. (2010). "Maintenance of positive diversity-stability relations along a gradient of environmental stress". PLoS ONE. 5 (4): e10378. Bibcode:2010PLoSO...510378R. doi:10.1371/journal.pone.0010378. PMC 2860506. PMID 20436913.
  37. ↑ Bascompte, J. (2009). "Disentangling the web of life". Science. 325 (5939): 416–419. Bibcode:2009Sci...325..416B. doi:10.1126/science.1170749. PMID 19628856.
  38. ↑ Krause, J.; et al. (2009). "Animal social networks: an introduction". Behav. Ecol. Sociobiol. 63 (7): 967–973. doi:10.1007/s00265-009-0747-0.
  39. ↑ Campbell, V.; et al. (2011). "Experimental design and the outcome and interpretation of diversity-stability relations". Oikos. 120 (3): 399–408. doi:10.1111/j.1600-0706.2010.18768.x.
  40. ↑ Lotze, H.; et al. (2011). "Historical changes in marine resources, food-web structure and ecosystem functioning in the Adriatic Sea, Mediterranean". Ecosystems. 14 (2): 198–222. doi:10.1007/s10021-010-9404-8.
  41. ↑ Romanuk, T.; et al. (2010). "Maintenance of positive diversity-stability relations along a gradient of environmental stress". PLoS ONE. 5 (4): e10378. Bibcode:2010PLoSO...510378R. doi:10.1371/journal.pone.0010378. PMC 2860506. PMID 20436913.
  42. ↑ Croft, D.P; et al. (2004). "Social networks in the guppy (Poecilia reticulate)". Philosophical Transactions of the Royal Society B. 271 (Suppl): S516–S519. doi:10.1098/rsbl.2004.0206. PMC 1810091. PMID 15801620.
  43. ↑ Krause, Lusseau, James, Jens, David, Richard (1 May 2009). "Animal social networks: an introduction". Behavioral Ecology and Sociobiology. 63: 967–973. doi:10.1007/s00265-009-0747-0.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  44. ↑ Croft, D.P; et al. (2004). "Social networks in the guppy (Poecilia reticulate)". Philosophical Transactions of the Royal Society B. 271 (Suppl): S516–S519. doi:10.1098/rsbl.2004.0206. PMC 1810091. PMID 15801620.
  45. ↑ Krause, Lusseau, James, Jens, David, Richard (1 May 2009). "Animal social networks: an introduction". Behavioral Ecology and Sociobiology. 63: 967–973. doi:10.1007/s00265-009-0747-0.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  46. ↑ Dornhaus, A.; et al. (2006). "Benefits of recruitment in honey bees: Effects of ecology and colony size in an individual-based model". Behavioral Ecology. 17 (3): 336–344. doi:10.1093/beheco/arj036.
  47. ↑ Linksvayer, T.; et al. (2012). "Developmental evolution in social insects: Regulatory networks from genes to societies". Journal of Experimental Zoology Part B: Molecular and Developmental Evolution. 318 (3): 159–169. doi:10.1002/jez.b.22001. PMID 22544713.
  48. ↑ Mullen, R.; et al. (2009). "A review of ant algorithms". Expert Systems with Applications. 36 (6): 9608–9617. doi:10.1016/j.eswa.2009.01.020.
  49. ↑ Croft, Darden, Wey, Darren P., Safi K., Tina W. (2016). "Current directions in animal social networks". Current Opinion in Behavioral Sciences. 12: 52–58. doi:10.1016/j.cobeha.2016.09.001. hdl:10871/23348.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  50. ↑ Ryder, T.B.; et al. (2008). "Social networks in the lek-mating wire-tailed manakin (Pipra filicauda)". Philosophical Transactions of the Royal Society B. 275 (1641): 1367–1374. doi:10.1098/rspb.2008.0205. PMC 2602714. PMID 18381257.
  51. ↑ Lusseau, D. (2007). "Evidence for social role in a dolphin social network". Evolutionary Ecology. 21 (3): 357–366. arXiv:q-bio/0607048. doi:10.1007/s10682-006-9105-0.
  52. ↑ Dornhaus, A.; et al. (2006). "Benefits of recruitment in honey bees: Effects of ecology and colony size in an individual-based model". Behavioral Ecology. 17 (3): 336–344. doi:10.1093/beheco/arj036.
  53. ↑ Linksvayer, T.; et al. (2012). "Developmental evolution in social insects: Regulatory networks from genes to societies". Journal of Experimental Zoology Part B: Molecular and Developmental Evolution. 318 (3): 159–169. doi:10.1002/jez.b.22001. PMID 22544713.
  54. ↑ Mullen, R.; et al. (2009). "A review of ant algorithms". Expert Systems with Applications. 36 (6): 9608–9617. doi:10.1016/j.eswa.2009.01.020.
  55. ↑ Croft, Darden, Wey, Darren P., Safi K., Tina W. (2016). "Current directions in animal social networks". Current Opinion in Behavioral Sciences. 12: 52–58. doi:10.1016/j.cobeha.2016.09.001. hdl:10871/23348.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  56. ↑ Ryder, T.B.; et al. (2008). "Social networks in the lek-mating wire-tailed manakin (Pipra filicauda)". Philosophical Transactions of the Royal Society B. 275 (1641): 1367–1374. doi:10.1098/rspb.2008.0205. PMC 2602714. PMID 18381257.
  57. ↑ Lusseau, D. (2007). "Evidence for social role in a dolphin social network". Evolutionary Ecology. 21 (3): 357–366. arXiv:q-bio/0607048. doi:10.1007/s10682-006-9105-0.
  58. ↑ Sundaresan, S.; et al. (2007). "Network metrics reveal differences in social organization between two fission-fusion species, Grevy's zebra and onager". Oecologia. 151 (1): 140–149. Bibcode:2007Oecol.151..140S. doi:10.1007/s00442-006-0553-6. PMID 16964497.
  59. ↑ Kasper, C.; Voelkl, B. (2009). "A social network analysis of primate groups". Primates. 50 (4): 343–356. doi:10.1007/s10329-009-0153-2. PMID 19533270.
  60. ↑ Sundaresan, S.; et al. (2007). "Network metrics reveal differences in social organization between two fission-fusion species, Grevy's zebra and onager". Oecologia. 151 (1): 140–149. Bibcode:2007Oecol.151..140S. doi:10.1007/s00442-006-0553-6. PMID 16964497.
  61. ↑ Kasper, C.; Voelkl, B. (2009). "A social network analysis of primate groups". Primates. 50 (4): 343–356. doi:10.1007/s10329-009-0153-2. PMID 19533270.
  62. ↑ Henzi, S.; et al. (2009). "Cyclicity in the structure of female baboon social networks". Behavioral Ecology and Sociobiology. 63 (7): 1015–1021. doi:10.1007/s00265-009-0720-y.
  63. ↑ Hunt, ER.; et al. (2018). "Social interactions shape individual and collective personality in social spiders". Proceedings of the Royal Society B. 285 (1886): 20181366. doi:10.1098/rspb.2018.1366. PMC 6158534. PMID 30185649.
  64. ↑ Krause J, Krause S, Arlinghaus R, Psorakis I, Roberts S, Rutz C (2013). "Reality mining of animal social systems". Trends in Ecology and Evolution. 28: 541–551. doi:10.1016/j.tree.2013.06.002.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  65. ↑ Henzi, S.; et al. (2009). "Cyclicity in the structure of female baboon social networks". Behavioral Ecology and Sociobiology. 63 (7): 1015–1021. doi:10.1007/s00265-009-0720-y.
  66. ↑ Hunt, ER.; et al. (2018). "Social interactions shape individual and collective personality in social spiders". Proceedings of the Royal Society B. 285 (1886): 20181366. doi:10.1098/rspb.2018.1366. PMC 6158534. PMID 30185649.
  67. ↑ Krause J, Krause S, Arlinghaus R, Psorakis I, Roberts S, Rutz C (2013). "Reality mining of animal social systems". Trends in Ecology and Evolution. 28: 541–551. doi:10.1016/j.tree.2013.06.002.{{cite journal}}: CS1 maint: multiple names: authors list (link)


Books

取自“https://wiki.swarma.org/index.php?title=生物网络&oldid=21869”