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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.
复杂的生物系统可以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组成的网络，如碳、氮和氧。节点和边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度和中间性。度(或连通性，一个不同于图论的用法)是连接一个节点的边的数量，而中间性是衡量一个节点在网络中有多么靠近中心位置。具有高中间性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中间性的节点可能在网络的整体组成中发挥重要作用。