进入二十一世纪以来，人们对复杂网络的研究持续升温。越来越多的学者加入到复杂网络的研究当中，这些学者来自于各个领域，包括：图论、统计物理学、计算机网络研究、生态学、社会学以及经济学等。它所涉及的网络主要有：生命科学领域的各种网络（如细胞网络、基因转录调控网络、神经网络，蛋白质―蛋白质作用网络）、社会网络（如流行性疾病传播网络、科学家合作网络）等。所使用的主要方法是数学上的图论、物理学中的统计物理学方法和社会网络分析方法。复杂网络通俗地说就是具有复杂特点的网络。它的复杂性表现在许多方面。例如结构复杂：表现在节点数目巨大，网络结构呈现多种不同特征；连接多样性：节点之间的连接权重和相互作用方式存在差异，且有可能存在方向性；节点多样性及其动力学的复杂性：复杂网络中的节点可以代表不同事物，其动力学通常具有非线性特征；多重复杂性融合：即以上多重复杂性相互影响，导致更为难以预料的结果。基因调控网络无疑是一种典型的复杂网络。它具有复杂网络中以上所列的所有特征。本报告主要研究了基因调控网络的拓扑特征以及对其的控制问题。对基因调控网络拓扑方面的研究，我们主要是从环的角度来进行的。对基因调控网络的控制主要运用了多相位超前驱动方法和带负反馈环权重的多相位超前驱动控制方法，经过大量的数值模拟，结果表明这两种方法对于基因调控网络无论是系统参数已经确定，还是系统参数没有确定，都可以达到非常好的控制效果。研究报告的第一章，主要介绍了与基因调控相关的一些知识以及最新的各种研究成果。首先介绍了研究基因调控网络的意义，指出我们为什么要研究基因调控网络，如何来研究基因调控网络，以及到目前为止，各方面专家在该领域已经做了哪些研究，并取得了哪些成果；接着介绍了基因调控网络的联合调控方式以及不同联合调控作用下的动力学模型；然后介绍了对基因调控网络中各种环的研究（包括正反馈环的生物作用和负反馈环的生物作用）；同时还介绍了运用主相位超前驱动方法寻找网络振荡源的理论基础，以及简要的操作步骤；最后是对整个研究报告的内容安排。在研究报告的第二章中，我们研究了大量的可以产生自持续振荡的基因调控网络，试图找出它们当中的一般特征。首先我们通过对大量可自持续振荡的基因调控网络进行测试，发现了基因网络中有利于振荡运动的参数区间。然后我们发现了自持续振荡网络结构的一些带有普遍性的特点，包括：自持续振荡网络中负反馈环个数比正反馈环个数多的概率要大；存在诸如三节点相继抑制负反馈环等很少几个对振荡有强支持的振荡模体并存在对一些振荡起强抑制作用的正反馈环。在研究报告的第三章中，我们研究了如何在主相位超前驱动方法的基础上对其进行改进，并最终提出了多相位超前驱动方法，对基因调控网络进行控制。我们首先介绍了多相位超前驱动方法的基本控制思想以及具体的操作步骤。接下来用一个实例来介绍具体的操作流程和控制的结果。我们通过对大量的基因调控网络进行数值模拟，结果表明：对于系统参数已经给定的基因调控网络，运用多相位超前驱动方法可以高效地找到网络中的重要节点，对其进行很好地控制。同时，当系统参数不确定时，我们也可以采取对参数空间进行化分的方法，运用该方法对网络进行有效的控制。大量的数值结果数据表明：对于节点数少于10个的基因调控网络，该种方法控制效率非常高。在研究报告的第四章中，我们对多节点的基因调控网络进行了研究。对于多节点的基因调控网络，因为其网络节点数众多，可以找到的环也特别多，所以如果再单纯地使用多相位超前驱动方法，工作量就会非常大。我们将有关负反馈环的结论与多相位超前驱动相结合，发现运用该方法可以很好地解决对多节点基因调控网络的控制问题。我们首先介绍了带负反馈环权重的多相位超前驱动方法的主体控制思想以及具体的操作步骤，接着用一个10节点的基因调控网络对该方法进行详细的讲解。通过对大量的多节点基因调控网络进行数值模拟控制，结果表明：对于系统参数确定的基因调控网络，运用带负反馈环权重的多相位超前驱动方法找到网络中重要节点的效率非常高，同时对于系统参数没有确定的网络，运动该方法也可以很好地对其进行控制。研究报告的最后一章，是对全文的总结和对未来研究的展望。

Exploring the principle and relationship of complex networks has becoming a generally researched issue. The investigators come from various fields, such as:graph theory, statistical physics, computer network, ecology, sociology, economics and so on. Complex networks contain life science networks (for examples: cell networks, gene transcriptional regulation networks, neural networks and Proteininteraction networks), social networks (for examples: epidemic spreading networks, and scientific collaboration networks) and so on. The main methods of research are graph theory which is used in mathematics, statistical physics.Simply speaking, a complex network is a rather complex network. Some main complexities are listed as : Structural Complexity. There are large numberof nodes in it and network configurations have different characteristics. Connective complexity. The linking structures and the weights of connects between various nodes may vary in complicated manners. Dynamics Complexity. Dynamics of nodes is usually nonlinear, leading to complicated pattern dynamics.Gene regulatory networks have indeed all characters of complexities mentioned above. In this thesis, we mainly studied self-sustained oscillations of genetranscription regulatory networks, including topological characteristic and how to control them. For the topological characteristic of gene regulatory network,we mainly researched it from the point of negative feedback loop. For the control of gene regulatory network, we mainly applied the method of multiple phase advanced driving, and negative feedback loop and multiple phase advanced driving. The numerical simulation results show that the control efficiency of both methods are very high in gene regulatory network.In Chapter 1, we introduced the research background about the gene regulations and updated research results. Firstly, we introduced the research significance of gene regulatory network, Secondly, we introduced the mode of combinatorial regulation and the model of dynamics. Thirdly, we introduced thenegative feedback loop and positive feedback loop in gene regulatory network, and the method of dominant phase advanced driving. Finally, we gave the arrangement of full thesis.In Chapter 2, we performed simulations studying large numbers of selfsustained oscillations of gene transcription regulatory networks, to reveal somespecial facts considerably influencing oscillations. First, we tested a mass of self-sustained oscillations of gene transcription regulatory networks, and found some parameter domains which are favorable to generate oscillations. Then, we revealed some characteristics of network structures generally existing in selfsustained oscillations of gene transcription regulatory networks, such as, in oscillatory networks the probability that number of negative feedback loops is larger than the number of positive feedback loops is rather large; there are some oscillatory motifs appearing high frequency; and there are also some counter-oscillatorymotifs, defined by various positive feedback loops, may play role of effectively suppressing oscillations.In Chapter 3, we researched how to control the gene regulatory network by the method of multiple phase advanced driving. Firstly, we introduced themethod of multiple phase advanced driving. Secondly, we introduce the process of the method by citing specific example. The numerical simulation results show that for the gene regulatory network in which system parameter is fixed, the control efficiency is very high, and the method will also applicable for the network in which system parameter is unfixed.In chapter 4, we research the multinodal gene regulatory network. For the multinodal gene regulatory network, because of the number of node in the work is very large, we could find plentiful loop. So it is very hard to research the network by using the method of multiple phase advanced driving. We combined the conclusion of negative feedback loop and multiple phase advanced driving, and find the method could resolve the problem primely.In Chapter 5, we made a summarization of full thesis, and presented a brief discussion looking into the possible future works.