The emergent properties of information processing in Bow Tie complex networks

该项目将致力于开发新的措施和方法来识别复杂网络中的节点，这些措施和方法将决定网络的最终(稳定)状态，重点是(但不限于)作为目标网络类型的生物网络。复杂网络主要由两种表示形式组成，离散(布尔)网络和连续(常微分方程式)网络。人们已经注意到，许多生物系统显示蝴蝶结结构。使得网络中有三个不同的层；输入组件、强连接核心和输出组件。文献假设信息处理是蝴蝶结网络的一个新特征。当前的方法利用动态或结构信息来识别给定网络的一组控制节点。我将开发一种新的方法，通过实施遗传算法来识别将控制复杂网络的一组节点，从而同时使用结构和动态措施。特别是以输出节点的状态为目标。研究的重点是利用网络的动态特征(如最大熵)和结构特征(如中心性、临界性或连通性度量)对复杂网络进行分解的算法，并建立一组主题(如反馈环、前馈环等)以进一步理解网络结构的复杂特征。母题可以被描述为微观层面的网络效应和宏观效应。将特别关注网络的强连接核心，因为由于较高的边密度，可能会出现更复杂的主题。了解特定基序对网络宏观行为的影响可能会允许新的方法来决定最终状态。在一句话中，我的研究将开发出新的测量和方法，利用结构和动态信息来找到复杂网络中可以决定稳态的节点。这将通过发展对基本主题的理解并使用诸如进化算法等人工智能技术来识别目标节点来实现。

This project will work to develop novel measures and methods for identifying nodes in complex networks that will dictate the final (steady) states of the network, with a focus on (but not limited to) biological networks as a target network type. Complex networks are comprised primarily of two types of representation, discrete (Boolean) and continuous (Ordinary differential equation) networks. It has been noted that many biological systems display bow tie architectures. Such that there are three distinct layers in the network; an input component, a strongly connected core and an output component. Literature hypothesises that information processing is an emergent feature of bow tie networks. Currently methods utilise either dynamical or structural information to identify a set of control nodes of a given network. I will develop a novel method that will use both structural and dynamical measures via the implementation of a genetic algorithm to identify a set of nodes that will control a complex network. Particularly targeting the states of output nodes. Research will focus on the development of an algorithm decomposing complex networks utilizing dynamical features of a network such as maximum entropy and structural features i.e centrality, criticality or connectedness measures.A set of motifs (such as feedback loops, feed forward loops etc), will be established to further understand complex features of network structures. Motifs can be described as micro level effects with macro effects on networks. Particular attention will be paid to the strongly connected core of a network, as more complex motifs are likely to appear due to the higher edge density. Understanding the effects of specific motifs on the macro behaviour of the network may allow for new methods to dictate final states.In one sentence my research will develop novel measures and methods that utilise both structural and dynamical information to find nodes in complex networks that can dictate steady states. This will be achieved by developing understanding of the underlying motifs and using artificial intelligence techniques such as evolutionary algorithms to identify target nodes.