时间延迟基因调控网络重构的决策树方法研究

基因调控网络的重构是功能基因组中最具挑战性的课题之一. 针对基因间转录调控的时间延迟性, 提出了一种寻找时间延迟调控关系的方法: 多点延迟调控网络算法, 简称TdGRN (time-delayed gene regulatory networking). 该方法根据时间序列基因表达谱数据, 构建时间延迟基因表达矩阵, 利用有监督决策树分类器方法和随机重排技术挖掘基因之间的时间延迟调控关系, 从而构建时间延迟的基因调控网络. 该方法是一种不依赖模型的基因网络重建方法, 相对于目前采用的基于模型的网络重建方法有显著优势, 可直接利用连续的基因表达谱数据发现延迟任一时间单位差的基因表达调控关系, 并避免了目前一些研究方法中需要人为设定基因的最大调控子数目(k)的问题. 将该方法应用于酿酒酵母细胞周期的基因表达谱数据, 并构建时间延迟的基因调控网络, 结果发现多数时间延迟调控关系获得了已有知识的支持.     

肝癌基因调控网络研究进展

肝癌(Hepatocellular carcinoma,HCC)是我国常见的恶性肿瘤之一。肝癌基因调控网络(HCC regulatory network,HCC GRN)是研究肝癌分子机制的重要途径之一,其节点包括肝癌相关的分子,如mi RNA、TF等,网络的边由节点间相互作用关系构成。基于不同类型的数据构建的肝癌基因调控网络其类型及特征各有不同。综合近年来肝癌基因调控网络研究发现,由TF与mi RNA构建的肝癌转录调控网络更能揭露肝癌关键基因,反映关键基因在调控网络中的扰动情况。整合基因变异信息与调控网络成为研究肝癌基因调控网络的趋势,但相应的研究几乎是空白的。本文从HCC GRN的数据来源、分类及特征,及各类型调控网络的近年研究情况等方面进行综述,并结合相关研究工作对肝癌基因调控网络研究现状进行分析与讨论,对前景进行展望,为这一领域研究工作提供参考。     

基因表达时间延迟调控关系识别软件ITdGR

基因表达调控网络的深入研究有利于分子药物靶标的发现以及推新药的研发,是未来生物医学研究的重要内容。针对基因表达调控的时间延迟问题,我们初步设计开发了一套基于基因表达谱数据识别基因表达时间延迟调控关系的软件ITdGR(Identification of Time-delayed Gene Regulations)。并已经成功地将该软件应用于酿酒酵母细胞周期的基因表达谱数据中,识别出的调控关系与已有的知识相符。该软件为基因调控网络重构以及基因表达动态研究提供了一个方便和快捷的工具。     

基于Petri网T不变量的PHB新陈代谢建模分析(英文)

从拓扑结构的角度分析生化反应网络是生物信息学研究中的一个热点问题。通过将两种传统的途径分析方法(基元模式和极端途径)与Petri网的T不变量分析进行了比较,结果表明:它们本质上是一致的,但是采用Petri网的T不变量分析更便捷。然后,利用Petri网技术构建了PHB代谢模型。对该模型作了结构分析,将计算得到的23个T不变量进行了分组:I组表示简单的可逆反应,II组表示循环的反应,III组可用于调控ATP/ADP比率,IV组是与PHB生产直接相关的反应,可用于代谢工程以提高PHB的产率。最后讨论了Petri网的T不变量分析在这个领域中的应用。     

microRNA调控的生物网络

生物网络是生物体内各种分子通过相互作用来完成各种复杂的生物功能的一个体系。网络水平的研究,有助于我们从整体上理解生物体内各种复杂事件发生的内在机制。microRNA（miRNA）是一类在转录后水平调控基因表达的小RNA分子。研究结果表明,miRNA调控的靶基因分布范围很广,因此必然与目前所研究的生物网络有着各种各样的联系。对这种关系的揭示,将对阐明miRNA的调控规律起到重要的作用。本文重点讨论了miRNA调控的基因调控网络、蛋白质相互作用网络以及细胞信号传导网络的特征。此外,还总结了miRNA调控的网络模体（motif）和miRNA协同作用网络的特征。     

一种基于基因表达模型识别酵母细胞周期条件特异调控子网的方法

由高通量微阵列技术产生的数据集可以用于解释生物系统基因调控的未知机制.生物过程是动态的,所以很有必要关注某些条件下特异的基因调控子网络.细胞周期是一个基本的细胞过程,识别酵母的细胞周期特异调控子网是理解细胞周期过程的基础,并且有助于揭示其他细胞条件的基因调控机理.使用一个基因表达微分方程模型(GEDEM),从静态网络中识别了动态的细胞周期相关调控关系.与已经报道的细胞周期相关调控相互作用相比,该方法识别了更多的真实存在的条件特异调控关系,取得了比当前的方法更好的性能.在大数据集上,GEDEM 识别了具有高敏感性和特异性的调控子网.组合调控的深入分析显示,条件特异调控子网的转录因子之间的相关性呈现出比静态网络中转录因子相关性更强,这说明条件特异网络比静态网络更加接近真实情况.另外,GEDEM 方法还识别更多潜在的共调控转录因子.     

RASSF1A对黑色素瘤A375细胞基因网络的影响

RASSF1A(Ras association domain family 1 isoform A)是定位于染色体3p21.3区域的抑瘤基因,编码一个由340个氨基酸残基构成的微管相关蛋白.该基因在包括恶性黑色素瘤在内的多种肿瘤中因启动子高甲基化而表达沉默.本研究建立了RASSF1A稳定表达的恶性黑色素瘤A375细胞系,通过全基因组表达谱基因芯片分析RASSF1A过表达对A375细胞基因表达谱的影响,发现RASSF1A引起184个基因表达上调,26个基因表达下调.通过Realtime RT-PCR对部分差异表达基因进行验证,结果表明与芯片筛选结果一致.RASSF1A影响的差异表达基因功能上归属于细胞生长与增殖、细胞周期、细胞凋亡、细胞间黏附、信号传导等生物过程.采用STRING软件构建了RASSF1A影响的差异表达基因调控网络,结果表明RASSF1A调控的差异表达基因构成一个高连接度的基因网络.其中,炎症细胞因子、转录因子位于网络中央.RASSF1A通过影响炎症细胞因子与转录因子之间的表达,影响A375细胞基因网络,调节黑色素瘤恶性生物学行为.     

一个基于基因表达谱的基因逻辑网络模型的建立与应用

基因之间除了线性关联作用关系外,还存在着非线性的逻辑关系.用这种逻辑关系构建的生物系统网络模型对研究细胞内的各种生物通路和细胞分子网络非常重要.首先,根据图着色原理确定了基因的低阶和高阶逻辑关系,然后应用结肠癌基因表达谱数据分析了51个癌基因和抑癌基因的逻辑关系,在此基础上构建了结肠癌基因表达的逻辑网络.通过这个网络模型发现了与KEGG数据库中结肠癌通路一致的转化生长因子信号通路,并分析了各生物通路成员之间错综复杂的关系.实验结果表明,基因逻辑网络模型在一定程度上揭示了结肠癌基因和抑癌基因之间并行、分叉等复杂的相互作用关系,反映了结肠癌发病的复杂分子机制,为分子生物医学家提供了一个参考模型.     

基因逻辑网络研究进展

海量生物数据的涌现,使得通过数据分析和理论方法探索生物机理成为理论生物学研究的重要途径．特别是对于基因的复杂的功能系统,建立基因网络这种理论方法的意义更为突出．Bowers在蛋白质相互作用的分析中引入了高阶逻辑关系,从而建立了系统发生谱数据的逻辑分析（LAPP）的系统方法．LAPP和通常建立模型的方法不同,它给出了一个从复杂网络的元素（或部件）的表达数据出发,通过逻辑分析,找到元素之间逻辑关联性的建模方法．这种方法能够从蛋白质表达谱数据出发,利用信息熵的算法发现两种蛋白质对一种蛋白质的联合作用,对于发现蛋白质之间新的作用机理有重要意义．由于涉及功能的基因组通常是一个大的群体构成的系统,因此LAPP方法也是一个生成复杂的基因逻辑网络的方法．基因逻辑网络的建立,方便实现通过逻辑调控进行基因调控的目的．这种方法可以应用在很多方面,如物种进化、肿瘤诊疗等等．系统阐述并分析了LAPP方法,并指出其在方法和应用方面的新进展以及评述．     

面向异步多时延基因调控网络建模的高阶动态贝叶斯网络模型及其结构 学习算法

目的：由基因芯片数据精确学习建模具有异步多时延表达调控关系的基因调控网络。方法：提出了一种高阶动态贝叶斯网 络模型,并给出了网络结构学习算法,该模型假定基因的调控过程为多阶马尔科夫过程,从而能够建模基因调控网络中的异步多 时延特性。结果：由酵母基因调控网络一个子网络人工生成了加入10%含噪声的表达数据用于调控网络结构学习。在75%的后验 概率下,本文提出的高阶动态贝叶斯网络模型能够正确建模实际网络中全部的异步多时延调控关系,而经典动态贝叶斯网络仅 能够正确建模实际网络中1/3的调控关系;ROC曲线对比表明在各个后验概率水平上高阶动态贝叶斯网络模型的效果均优于经 典动态贝叶斯网络。结论：本文提出的高阶动态贝叶斯网络模型能够精确学习建模具有异步多时延表达调控关系的基因调控网 络。     

Simulation of a Petri net-based Model of the Terpenoid Biosynthesis Pathway

Background  

The development and simulation of dynamic models of terpenoid biosynthesis has yielded a systems perspective that provides new insights into how the structure of this biochemical pathway affects compound synthesis. These insights may eventually help identify reactions that could be experimentally manipulated to amplify terpenoid production. In this study, a dynamic model of the terpenoid biosynthesis pathway was constructed based on the Hybrid Functional Petri Net (HFPN) technique. This technique is a fusion of three other extended Petri net techniques, namely Hybrid Petri Net (HPN), Dynamic Petri Net (HDN) and Functional Petri Net (FPN).     

Neural system modeling and simulation using Hybrid Functional Petri Net

The Petri net formalism has been proved to be powerful in biological modeling. It not only boasts of a most intuitive graphical presentation but also combines the methods of classical systems biology with the discrete modeling technique. Hybrid Functional Petri Net (HFPN) was proposed specially for biological system modeling. An array of well-constructed biological models using HFPN yielded very interesting results. In this paper, we propose a method to represent neural system behavior, where biochemistry and electrical chemistry are both included using the Petri net formalism. We built a model for the adrenergic system using HFPN and employed quantitative analysis. Our simulation results match the biological data well, showing that the model is very effective. Predictions made on our model further manifest the modeling power of HFPN and improve the understanding of the adrenergic system. The file of our model and more results with their analysis are available in our supplementary material.     

Biopathways representation and simulation on hybrid functional Petri net

The following two matters should be resolved in order for biosimulation tools to be accepted by users in biology/medicine: (1) remove issues which are irrelevant to biological importance, and (2) allow users to represent biopathways intuitively and understand/manage easily the details of representation and simulation mechanism. From these criteria, we firstly define a novel notion of Petri net called Hybrid Functional Petri Net (HFPN). Then, we introduce a software tool, Genomic Object Net, for representing and simulating biopathways, which we have developed by employing the architecture of HFPN. In order to show the usefulness of Genomic Object Net for representing and simulating biopathways, we show two HFPN representations of gene regulation mechanisms of Drosophila melanogaster (fruit fly) circadian rhythm and apoptosis induced by Fas ligand. The simulation results of these biopathways are also correlated with biological observations. The software is available to academic users from http://www.GenomicObject.Net/.     

Constructing biological pathway models with hybrid functional Petri nets

In many research projects on modeling and analyzing biological pathways, the Petri net has been recognized as a promising method for representing biological pathways. From the pioneering works by Reddy et al., 1993, and Hofest?dt, 1994, that model metabolic pathways by traditional Petri net, several enhanced Petri nets such as colored Petri net, stochastic Petri net, and hybrid Petri net have been used for modeling biological phenomena. Recently, Matsuno et al., 2003b, introduced the hybrid functional Petri net (HFPN) in order to give a more intuitive and natural modeling method for biological pathways than these existing Petri nets. Although the paper demonstrates the effectiveness of HFPN with two examples of gene regulation mechanism for circadian rhythms and apoptosis signaling pathway, there has been no detailed explanation about the method of HFPN construction for these examples. The purpose of this paper is to describe method to construct biological pathways with the HFPN step-by-step. The method is demonstrated by the well-known glycolytic pathway controlled by the lac operon gene regulatory mechanism.     

Phenomenological and molecular-level Petri net modeling and simulation of long-term potentiation

Petri net-based modeling methods have been used in many research projects to represent biological systems. Among these, the hybrid functional Petri net (HFPN) was developed especially for biological modeling in order to provide biologists with a more intuitive Petri net-based method. In the literature, HFPNs are used to represent kinetic models at the molecular level. We present two models of long-term potentiation previously represented by differential equations which we have transformed into HFPN models: a phenomenological synapse model and a molecular-level model of the CaMKII regulation pathway. Through simulation, we obtained results similar to those of previous studies using these models. Our results open the way to a new type of modeling for systems biology where HFPNs are used to combine different levels of abstraction within one model. This approach can be useful in fully modeling a system at the molecular level when kinetic data is missing or when a full study of a system at the molecular level it is not within the scope of the research.     

Communication and complexity in a GRN-based multicellular system for graph colouring

Artificial Genetic Regulatory Networks (GRNs) are interesting control models through their simplicity and versatility. They can be easily implemented, evolved and modified, and their similarity to their biological counterparts makes them interesting for simulations of life-like systems as well. These aspects suggest they may be perfect control systems for distributed computing in diverse situations, but to be usable for such applications the computational power and evolvability of GRNs need to be studied. In this research we propose a simple distributed system implementing GRNs to solve the well known NP-complete graph colouring problem. Every node (cell) of the graph to be coloured is controlled by an instance of the same GRN. All the cells communicate directly with their immediate neighbours in the graph so as to set up a good colouring. The quality of this colouring directs the evolution of the GRNs using a genetic algorithm. We then observe the quality of the colouring for two different graphs according to different communication protocols and the number of different proteins in the cell (a measure for the possible complexity of a GRN). Those two points, being the main scalability issues that any computational paradigm raises, will then be discussed.