“生物网络”的版本间的差异

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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.
 
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.
  
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复杂的生物系统能用可计算的网络来表示和分析。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。进一步分解蛋白质，氨基酸可以表示为一个由相互连接的原子构成的网络，如碳、氮和氧。'''<font color="#ff8000"> 节点 Nodes</font>'''和'''<font color="#ff8000">边 Edges</font>'''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。节点可以代表一系列广泛的生物单元，从单个的生物体到大脑中单个的神经元。网络的两个重要性质是'''<font color="#ff8000">度 Degree</font>'''和'''<font color="#ff8000"> 介数中心性 Betweenness Centrality</font>'''。度(或连通性，一个不同于'''<font color="#ff8000">图论 Graph Theory</font>'''的用法)是连接一个节点的边的数量，而介数中心性是衡量一个节点在网络中有多么靠近中心位置。具有高介数的节点本质上充当网络不同部分之间的桥梁(即网络其他部分的交互，必须通过这个节点)。在社会网络中，具有较高度值和较高介数的节点可能在网络的整体组成中发挥重要作用。
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复杂的生物系统能用可计算的网络来表示和分析。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。进一步分解蛋白质，氨基酸可以表示为一个由相互连接的原子构成的网络，如碳、氮和氧。'''<font color="#ff8000"> 顶点 Nodes</font>'''和'''<font color="#ff8000">边 Edges</font>'''是网络的基本组成部分。顶点表示网络中的单元，边表示单元之间的相互作用。顶点可以代表一系列广泛的生物单元，从单个的生物体到大脑中单个的神经元。网络的两个重要性质是'''<font color="#ff8000">度 Degree</font>'''和'''<font color="#ff8000"> 介数中心性 Betweenness Centrality</font>'''。度(或连通性，一个不同于'''<font color="#ff8000">图论 Graph Theory</font>'''的用法)是连接一个顶点的边的数量，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。具有高介数的顶点本质上充当网络不同部分之间的桥梁(即网络其他部分的交互，必须通过这个节点)。在社会网络中，具有较高度值和较高介数的节点可能在网络的整体组成中发挥重要作用。
  
  
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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.
 
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.
  
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在细胞中，大量'''<font color="#ff8000">蛋白质之间相互作用 Protein-Protein Interactions(PPIs)</font>'''形成'''<font color="#ff8000">蛋白质相互作用网络 Protein Interaction Networks(PINs)</font>'''，其中蛋白质是节点，它们的相互作用是连边。蛋白质互作网络是生物学中分析最深入的网络，现有几十种基于PPIs的检测方法来识别蛋白质间的相互作用。'''<font color="#ff8000">酵母双杂交系统 Yeast Two-Hybrid System</font>'''是一种研究二元相互作用的常用实验技术。
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在细胞中，大量'''<font color="#ff8000">蛋白质之间相互作用 Protein-Protein Interactions(PPIs)</font>'''形成'''<font color="#ff8000">蛋白质相互作用网络 Protein Interaction Networks(PINs)</font>'''，其中蛋白质是顶点，它们的相互作用是连边。蛋白质互作网络是生物学中分析最深入的网络，现有几十种基于PPIs的检测方法来识别蛋白质间的相互作用。'''<font color="#ff8000">酵母双杂交系统 Yeast Two-Hybrid System</font>'''是一种研究二元相互作用的常用实验技术。
  
  
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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.
  
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'''<font color="#ff8000">基因共同表达网络 Gene Co-expression Networks </font>'''可以看作是衡量转录丰度的变量之间的关联网络。这些网络已经被用于对DNA微阵列数据、 RNA-seq数据、 miRNA等数据进行系统生物学分析。
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'''<font color="#ff8000">基因共表达网络 Gene Co-expression Networks </font>'''可以看作是衡量转录丰度的变量之间的关联网络。这些网络已经被用于对DNA微阵列数据、 RNA-seq数据、 miRNA等数据进行系统生物学分析。
  
 
[[Weighted correlation network analysis|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 correlation network analysis|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.

2020年8月15日 (六) 16:19的版本

本词条由Ryan初步翻译

Network science
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Network types
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Features
Types
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Models
Topology
Dynamics
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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.

任何应用于生物系统 Biological Systems的网络都可称为 生物网络 Biological Network。网络是指内部子单元可连接成一个整体的系统，如大量单个物种连接成一个整体的食物链网 Food Web。生物网络为在生态学、进化学和生理学研究中发现的联系提供了数学形式的表达，例如 神经网络 Neural Networks。生物网络分析在人类疾病方面的应用，开创了网络医学领域。


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.[4] 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计算的网络来表示和分析。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。进一步分解蛋白质，氨基酸可以表示为一个由相互连接的原子构成的网络，如碳、氮和氧。 顶点 Nodes和边 Edges是网络的基本组成部分。顶点表示网络中的单元，边表示单元之间的相互作用。顶点可以代表一系列广泛的生物单元，从单个的生物体到大脑中单个的神经元。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，一个不同于图论 Graph Theory的用法)是连接一个顶点的边的数量，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。具有高介数的顶点本质上充当网络不同部分之间的桥梁(即网络其他部分的交互，必须通过这个节点)。在社会网络中，具有较高度值和较高介数的节点可能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.[5] 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).[6]

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 或基因组视为一个可存储动态信息的语言系统，因其具有可精确计算的有限状态，所以可被看做一个有限状态机 Finite State Machine。最近的复杂系统 Complex System研究还表明，一些在生物学、计算机科学和物理学等领域的问题，从信息组织的角度来看存在一些意义深远的共性，例如玻色-爱因斯坦凝聚态(一种特殊的物质状态)。


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、蛋白质和搜索算法逐渐转移到像生物组 Biome、交互组 Interactome、基因组 Genome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.[7] 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.[8]

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，大量蛋白质之间相互作用 Protein-Protein Interactions(PPIs)形成蛋白质相互作用网络 Protein Interaction Networks(PINs)，其中蛋白质是顶点，它们的相互作用是连边。蛋白质互作网络是生物学中分析最深入的网络，现有几十种基于PPIs的检测方法来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。


Recent studies have indicated conservation of molecular networks through deep evolutionary time.[9] 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.[10] 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，分子网络在深层进化过程中是保守的（在进化过程中的改变较少）。此外，已经发现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更加重要。这表明网络的各个部分的相互配合(不仅仅是蛋白质之间简单的相互作用)对于有机体的整体功能具有重要意义。


Gene regulatory networks (DNA–protein interaction networks)

基因调控网络（DNA-蛋白质交互网络）

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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.[11] 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，最典型的是负责调控基因表达的转录因子 Transcription Factors。大多数转录因子可与基因组中的多个位点结合。因此，所有的细胞都有复杂的基因调控网络 Gene Regulatory Networks。例如，人类基因组中有1400个左右的基因编码可与DNA结合的转录因子，它们参与到调节超过20000个人类基因的表达过程。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  释义注意统一

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 可以看作是衡量转录丰度的变量之间的关联网络。这些网络已经被用于对DNA微阵列数据、 RNA-seq数据、 miRNA等数据进行系统生物学分析。

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.

基因共表达加权网络分析 Weighted Gene Co-expression Network Analysis 被广泛应用于鉴定共表达模块（具有相似表达模式的基因很可能是紧密共调控的，功能紧密相关的或同一条信号通路或过程的成员，有其特定的生理意义，用网络的方式表示时，可划分到一个模块里）和模块内的核心基因。共表达模块可能与外部特征相关的信息关联在一起，如细胞类型或病症通路。具有大量连接的模块内节点可以代表模块内的其他基因，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.[4]

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.

活细胞中的化合物相互之间进行生物化学反应，从而实现化合物之间的转变。这些反应是由酶催化的。因此，一个细胞中的所有化合物都是一个复杂的生化反应网络的一部分，该网络被称为代谢网络。可使用网络分析来推断筛选是如何影响代谢通路的。


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，从而形成复杂的信号网络。例如，MAPK/ERK 通路通过一系列蛋白质-蛋白质相互作用、磷酸化反应和其他事件将信号从细胞表面传递到细胞核内。信号网络通常包含了蛋白质-蛋白质相互作用网络、基因调控网络和代谢网络。


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.[12] For instance, small-world network properties have been demonstrated in connections between cortical areas of the primate brain[13] or during swallowing in humans.[14] 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.

大脑中神经元之间复杂的相互作用使其成为应用网络理论的绝佳场景。大脑中的神经元彼此之间有着紧密的联系，其相互交织形成的复杂网络是人脑结构和功能的基础。例如，灵长类动物大脑皮层区域之间的连接或者人类吞咽时神经网络的行为已经证明了，这二者都具有小世界网络属性Small-World Network 。这表明大脑皮层各区域之间并不直接进行相互作用，而是大部分区域通过少量的节点区域进行相互沟通。

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.[15] 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.[16] 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.

所有的生物都具有捕食别的物种或被别的物种捕食的情况。也就是说，如果一个物种吃了另一个物种或被另一个物种吃了，它们就在一个复杂的捕食者和被捕食者相互作用的食物网中被联系起来。这些相互作用的稳定性长期困扰着生态学家们。具体来说，如果某些个体被移除，那么网络会发生什么？(即它是崩溃还是重新适应) ？网络分析可以用来探索食物网的稳定性，并确定某些网络特性是否会导致更稳定的网络。此外，网络分析可以用来确定物种的选择性迁移将如何影响整个食物链网。考虑到全球气候变化可能导致大量物种的消失，使用网络分析来研究食物链网的特性是尤其重要的。


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.[17] 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.[18] 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.[19] 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.[19][20] 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.[20] More generally, the structure of species interactions within an ecological network can tell us something about the diversity, richness, and robustness of the network.[21] Researchers can even compare current constructions of species interactions networks with historical reconstructions of ancient networks to determine how networks have changed over time.[22] Recent research into these complex species interactions networks is highly concerned with understanding what factors (e.g., diversity) lead to network stability.[23]

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 历来是研究的重点。随着近几年网络科学的发展，成对相互作用内涵很有可能被丰富，外延甚至能涵盖有许多物种个体参与的多组相互作用，进而帮助我们理解更大生态网络 Ecological Networks 的结构和功能。使用网络分析可以发现和理解这些复杂的交互是如何连接成网络的，该形成过程之前经常被忽略。这个强大的工具使得同一个普世框架可以研究各种类型的交互(从竞争到合作)。例如，植物与传粉者之间的相互作用是互惠互利的，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的生殖和食物链底层初级消费者的资源积累至关重要，然而这些相互作用网络正受到人造变化的威胁。网络分析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。在授粉网络中，内嵌性(某一领域的植物被少数几个昆虫授粉，剩下的各种昆虫只对少数几种植物授粉)、冗余性(即大多数植物是由许多授粉者授粉的)和模块性（一些物种相互之间连接的很紧密，与剩余的物种连接不紧密，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。这些网络属性实际上可以减缓干扰效应在系统上的传播，并可能缓冲授粉网络受到的人造变化的影响。研究人员甚至可以将物种相互作用网络的现有结构与该网络过去的结构进行比较，以确定网络在时间尺度上是如何演化的。最近对这些复杂物种间相互作用网络的研究聚焦于理解是什么因素(如多样性)造就了网络的稳定性。


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.[24] 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.[25]

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.

网络分析可以量化个体之间的关联，这使得在物种和/或种群水平推断整个网络的细节成为可能。网络范式最吸引人的特征之一是，它提供了一个统一的概念框架，使得社会性动物在所有层次上(单，双，组，群)--Ryan（讨论）和所有类型的互动中(攻击，合作，性等等)都可以被研究。


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;[26][27][28] 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.[29] 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).[30] 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.[31]

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，对动物行为学感兴趣的研究人员，开始将网络分析纳入到他们的研究中。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、任务分配和觅食优化; 其他研究人员感兴趣的点则在群体和/或种群水平的某些网络特性如何影响个体的行为。研究表明，环境特征和个体特征，如经验发展和人格等因素，可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决定因素，可能预测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，如频率依赖性选择和疾病及信息的传递。例如，一项针对线尾 manakins (一种小型雀形目鸟)的研究发现，在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(即最终可获得领地和交配机会)。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，比如使用侧翻和头潜入水中尾巴举向空中来表示自己愿意领导团队的迁移; 介数值较高的个体有着更多的连接，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更容易表现出传递这些信号的行为。


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.[32] 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.[33]

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 也可以用来更广泛地描述一个物种内的社会组织，它经常揭示出那些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，网络分析揭示了生活在变化环境中的两个相关的等裂变融合物种——格雷维斑马和骑驴——的群体动力学的细微差异（裂变融合社会是这样一个社会，其规模和组成随着时间的流逝以及动物在整个环境中的移动而变化；动物会合并成一团（融合）-例如在一个地方睡觉-或分裂（裂变）-例如白天在小组中觅食）; 格雷维斑马在分裂为较小的群体时，在关联选择上表现出明显的偏好，而骑驴则不然。同样，对灵长类动物感兴趣的研究人员也利用网络分析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)可能有助于解释我们在某些特定群体中看到的社会行为类型。


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.[34] 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.[35] 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.[36] 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不断变化的环境中的关键波动。例如，对雌性沙卡马狒狒(Papio hamadryas ursinus)的网络分析揭示了以前未知的跨季节的重要动态变化; 结果发现，狒狒之间没有建立稳定、持久的社会联系，相反，它们之间的关系是多变的。这些关系依赖于在动态的群体层面产生的短期偶然事件以及环境中的变化。个体社交网络环境的变化也会影响个体的性格特征，例如，与胆大的邻居挤在一起的蜘蛛也会更加大胆。这只是研究人员利用网络分析来研究动物行为的一小部分。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和计算机视觉技术的广泛发展，这些技术可以用来自动收集个体的社会关系。社会网络分析是研究所有物种的行为的一个有价值的工具，并且有可能发现以前鲜为人知的关于动物行为和社会生态的新信息。


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

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  32. ↑ 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.
  33. ↑ 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.
  34. ↑ 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.
  35. ↑ 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.
  36. ↑ 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)


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