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==Network biology and bioinformatics==
==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 [[atom]]s, such as [[carbon]], [[nitrogen]], and [[oxygen]]. [[vertex (graph theory)|Nodes]] and [[vertex (graph theory)|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 [[vertex (graph theory)|degree]] and [[betweenness centrality]]. Degree (or connectivity, a distinct usage from that used in [[Connectivity (graph theory)|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.<ref name="Proulx">{{cite journal |author= Proulx, S.R. |title= Network thinking in ecology and evolution |journal= Trends in Ecology and Evolution |year= 2005 |volume= 20 |issue= 6 |pages= 345–353 |doi=10.1016/j.tree.2005.04.004 |pmid=16701391|display-authors=etal}}</ref> 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 [[atom]]s, such as [[carbon]], [[nitrogen]], and [[oxygen]]. [[vertex (graph theory)|Nodes]] and [[vertex (graph theory)|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 [[vertex (graph theory)|degree]] and [[betweenness centrality]]. Degree (or connectivity, a distinct usage from that used in [[Connectivity (graph theory)|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.<ref name="Proulx">{{cite journal |author= Proulx, S.R. |title= Network thinking in ecology and evolution |journal= Trends in Ecology and Evolution |year= 2005 |volume= 20 |issue= 6 |pages= 345–353 |doi=10.1016/j.tree.2005.04.004 |pmid=16701391|display-authors=etal}}</ref> 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.
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).
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 或基因组视为一个语言系统的动态存储，其精确的可计算有限状态表示为一个有限状态机。最近的复杂系统研究还表明，在生物学、计算机科学和物理学等问题的信息组织方面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)。
早在20世纪80年代，研究人员就开始将 DNA 或基因组视为一个动态存储信息的语言系统，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。最近的复杂系统研究还表明，一些在生物学、计算机科学和物理学等领域的问题在信息组织方面，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大规模网络，这些网络通常被称为生物组、交叉组、基因组和蛋白质组。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。
===Protein–protein interaction networks===
===Protein–protein interaction networks===
蛋白质-蛋白质互作网络
{{main article|interactome}}
{{main article|interactome}}
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.
在细胞中，大量蛋白质之间相互作用(PPIs)形成蛋白质相互作用网络(PINs) ，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的检测方法来识别这种相互作用。酵母双杂交系统是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. 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.
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。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是重要的。
===Gene regulatory networks (DNA–protein interaction networks)===
===Gene regulatory networks (DNA–protein interaction networks)===
基因调控网络（DNA-蛋白质交互网络）
{{main article| Gene regulatory network}}
{{main article| Gene regulatory network}}
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.
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结合的蛋白中，最典型的是转录因子，它们负责调控基因的表达。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。例如，人类基因组中有1400个左右得基因编码，可与DNA结合的转录因子，调节超过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)===
===Gene co-expression networks (transcript–transcript association networks)===
基因共表达网络（转录-转录关联网络）
{{main article| Gene co-expression networks}}
{{main article| Gene co-expression networks}}
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.
基因共同表达网络可以解释为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.