基因调控网络的建模及其结构分解方法研究
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摘要
生物信息学是在从信息处理与分析的角度研究生命现象的一门新兴学科,基因调控网络的建模研究是近年来研究系统生物学的有效信息化手段之一。基因调控网络是在高度联通的细胞环境中,将基因组作为一个整体的组织结构从系统学角度来研究基因的功能与行为。随着信息技术和计算科学的进步,在过去几十年的时间里,基因调控网络得到了广泛的研究和迅猛的发展,在详细解读细胞代谢的功能基础上,对探索生命活动的机理,疾病的起因和治疗发挥了巨大的作用。
本文主要针对基因调控网络的模型的参数构建和结构分析的问题进行了研究,包括布尔基因网络的在随机状态下的拓扑性质的证明,基于概率布尔网络参数构建基础上的结构分解,模糊布尔基因网络的智能计算方法构建,从解构和建构两个角度对基因调控网络进行研究。此外,提出一种适合时序基因表达数据的混合聚类算法。具体来说,本论文的创造性工作主要分述如下:
1结合小世界网络理论,采取一种布尔网络随机化的方法,结合复杂网络的理论,对经典随机布尔网络进行随机采样和仿真,证明作为基因调控网络模型的布尔网络在随机化选择下具有小世界拓扑结构的有序性,生物学意义可解释为细胞生命周期对细胞核中基因在干扰状态下的稳定性,并介绍一种构建布尔网络的简单方法。
2以概率布尔型基因调控网络为例,提出一种大型基因调控网络结构分解的方法。其基本思想是结合对布尔基因网络的拓扑性质的证明,结合概率布尔网络的结构和构建方法,将基因调控网络抽象化,结合先验的和数学的方法制定规则,寻找网络中的关键节点,通过关键节点的中心作用,提出一种最短路径最大信息流的方法计算节点之间最短路径上的信息传递,以确定节点之间的相互调节作用,从而达到将基因调控网络分割成小规模的功能性集中的子网络。
3构建一种模糊化的基因调控网络模型。在布尔型基因调控网络与概率布尔型基因调控网络的结构以及性质的研究基础上,引入对基因相互之间调控作用的模糊层次的划分,结合先验知识建立模糊隶属度函数构建模糊概率布尔网络。并对此网络进行模拟和仿真,讨论其稳定状态和在干扰下的鲁棒性。
4提出了一种对时序基因表达数据动态模糊聚类的新算法。针对于时序基因表达数据的动态关联性特点,在模糊聚类算法的基础上引入了自回归模型,提出了一种动态模糊聚类新算法,将时序基因表达数据作为一组时间序列进行动态的聚类分析。该算法克服了传统硬聚类算法的绝对性,迭代的过程进行了模糊调整和动态预测,并且充分利用了时序基因表达数据在个时间点上的自相关性,使得对基因数据的聚类效果更为理想。
Bioinformatics is a new subject on processing and analysis life information. In recent years, modeling gene regulatory networks become one of the effective means of systems biology research. In the highly connected cells environment, in order to get the gene regulatory networks, we make the genome as a whole organization structure, study function and behavior of gene from the systematic point of view. As the advances in information technology and computational science, gene regulatory network has been extensively studied in the last decade. On the basis of understanding the functions of cell metabolism in detail, it plays a significant role in exploring the mechanism of life activities and causes of disease.
This paper is mainly aimed at several issues based on structure building and analysis of gene regultory networks. Including: proving the ordering of Boolean network in random states, sutructure analysis of probability Boolean gene regulatory network basing on the parameters, building fuzzy Boolean gene regulatory network in intelligent methods. Besides the study on construction and deconstruction on gene regulatory networks, a new hybrid clustering algorithm for analyzing time-course gene expression. To be concrete, the contributions of this dissertation are as follows:
1. Combined with the small-world network theory,take a method for randomized Boolean networks, Based on the theory of complex networks,the classical random sampling random Boolean networks and simulation Prove the model as a Boolean gene regulatory network topology of the network, and presents a simple way to build a Boolean network.
2. In the second part, a new method is presented for decomposing the structure of gene regulatory networks, by taking the probability Boolean network (PBN) as a typical model. Basic steps as follows: first, according to the strcuture of probability Booloean networks, search for key genes in the abstract network with nodes and connections; second, starting from these key nodes(genes), calculating mutual information between geneon the shortest path with maximum average mutual information passing can be found. Each ordinary node can be distinguished which sub-network it belongs to. Ultimately, the choice of function parameters and scale of sub-network depends on the biological consideration.
3. A new gene regulatory network model via the fuzzy logic is proposed. Conmbined with the research on the structure of gene regulatory networks and obtained experience, judging genes expression level on the fuzzy rule, to contribute a new model of gene regulatory network.
4. A novel fuzzy clustering algorithm is proposed for analysing time-course gene expression data. Compared with conventional hard partition clustering algorithms, fuzzy clustering algorithms are robust to the scaling transformation of a dataset. However, they cannot make full use of the important dynamic information in time-course gene expression data. Accordingly, autoregressive (AR) model can be introduced into fuzzy clustering algorithm; we explore the proposed clustering algorithm DFC, which can analyze a time-course gene expression data as a set of time series dynamically. In this way, the important dynamic information in time-course gene expression data is used adequately. And the forecast processes in AR model is adjusted using the corresponding membership functions, such that better clustering results for time-course gene expression data is obtained.
本文主要针对基因调控网络的模型的参数构建和结构分析的问题进行了研究,包括布尔基因网络的在随机状态下的拓扑性质的证明,基于概率布尔网络参数构建基础上的结构分解,模糊布尔基因网络的智能计算方法构建,从解构和建构两个角度对基因调控网络进行研究。此外,提出一种适合时序基因表达数据的混合聚类算法。具体来说,本论文的创造性工作主要分述如下:
1结合小世界网络理论,采取一种布尔网络随机化的方法,结合复杂网络的理论,对经典随机布尔网络进行随机采样和仿真,证明作为基因调控网络模型的布尔网络在随机化选择下具有小世界拓扑结构的有序性,生物学意义可解释为细胞生命周期对细胞核中基因在干扰状态下的稳定性,并介绍一种构建布尔网络的简单方法。
2以概率布尔型基因调控网络为例,提出一种大型基因调控网络结构分解的方法。其基本思想是结合对布尔基因网络的拓扑性质的证明,结合概率布尔网络的结构和构建方法,将基因调控网络抽象化,结合先验的和数学的方法制定规则,寻找网络中的关键节点,通过关键节点的中心作用,提出一种最短路径最大信息流的方法计算节点之间最短路径上的信息传递,以确定节点之间的相互调节作用,从而达到将基因调控网络分割成小规模的功能性集中的子网络。
3构建一种模糊化的基因调控网络模型。在布尔型基因调控网络与概率布尔型基因调控网络的结构以及性质的研究基础上,引入对基因相互之间调控作用的模糊层次的划分,结合先验知识建立模糊隶属度函数构建模糊概率布尔网络。并对此网络进行模拟和仿真,讨论其稳定状态和在干扰下的鲁棒性。
4提出了一种对时序基因表达数据动态模糊聚类的新算法。针对于时序基因表达数据的动态关联性特点,在模糊聚类算法的基础上引入了自回归模型,提出了一种动态模糊聚类新算法,将时序基因表达数据作为一组时间序列进行动态的聚类分析。该算法克服了传统硬聚类算法的绝对性,迭代的过程进行了模糊调整和动态预测,并且充分利用了时序基因表达数据在个时间点上的自相关性,使得对基因数据的聚类效果更为理想。
Bioinformatics is a new subject on processing and analysis life information. In recent years, modeling gene regulatory networks become one of the effective means of systems biology research. In the highly connected cells environment, in order to get the gene regulatory networks, we make the genome as a whole organization structure, study function and behavior of gene from the systematic point of view. As the advances in information technology and computational science, gene regulatory network has been extensively studied in the last decade. On the basis of understanding the functions of cell metabolism in detail, it plays a significant role in exploring the mechanism of life activities and causes of disease.
This paper is mainly aimed at several issues based on structure building and analysis of gene regultory networks. Including: proving the ordering of Boolean network in random states, sutructure analysis of probability Boolean gene regulatory network basing on the parameters, building fuzzy Boolean gene regulatory network in intelligent methods. Besides the study on construction and deconstruction on gene regulatory networks, a new hybrid clustering algorithm for analyzing time-course gene expression. To be concrete, the contributions of this dissertation are as follows:
1. Combined with the small-world network theory,take a method for randomized Boolean networks, Based on the theory of complex networks,the classical random sampling random Boolean networks and simulation Prove the model as a Boolean gene regulatory network topology of the network, and presents a simple way to build a Boolean network.
2. In the second part, a new method is presented for decomposing the structure of gene regulatory networks, by taking the probability Boolean network (PBN) as a typical model. Basic steps as follows: first, according to the strcuture of probability Booloean networks, search for key genes in the abstract network with nodes and connections; second, starting from these key nodes(genes), calculating mutual information between geneon the shortest path with maximum average mutual information passing can be found. Each ordinary node can be distinguished which sub-network it belongs to. Ultimately, the choice of function parameters and scale of sub-network depends on the biological consideration.
3. A new gene regulatory network model via the fuzzy logic is proposed. Conmbined with the research on the structure of gene regulatory networks and obtained experience, judging genes expression level on the fuzzy rule, to contribute a new model of gene regulatory network.
4. A novel fuzzy clustering algorithm is proposed for analysing time-course gene expression data. Compared with conventional hard partition clustering algorithms, fuzzy clustering algorithms are robust to the scaling transformation of a dataset. However, they cannot make full use of the important dynamic information in time-course gene expression data. Accordingly, autoregressive (AR) model can be introduced into fuzzy clustering algorithm; we explore the proposed clustering algorithm DFC, which can analyze a time-course gene expression data as a set of time series dynamically. In this way, the important dynamic information in time-course gene expression data is used adequately. And the forecast processes in AR model is adjusted using the corresponding membership functions, such that better clustering results for time-course gene expression data is obtained.
引文
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