一种可视化代谢网络建模工具

针对现有代谢网络建模工具在分析方法和模型描述方面的不足,基于Cytoscape插件开发一种可视化代谢网络建模工具,定义通用的支持FBA和^13C—MFA模型的代谢网络描述语言,实现图形用户界面下的代谢网络建模功能。应用结果表明,该工具能较好解决现有建模软件面临的问题。     

基于Android的代谢网络可视化和生化反应搜索的实现

代谢网络和生化反应代谢路径的研究是合成生物领域的重要内容,目前亟需相关计算机软件来辅助分析。针对这种情况以及目前移动互联网发展趋势,开发了一种基于Android的代谢网络可视化和生化反应搜索引擎系统。该系统将Android作为展示平台,显示代谢网络的三维模型,提供生化反应的搜索,这些都为生物合成和药物发现领域的研究人员提供了参考。系统用美观的三维模型显示代谢网络,同时提供简单的便捷的操作界面供用户查询生化反应。     

一种多变元网络可视化方法

提出一种多变元网络可视化方法MulNetVisBasc,根据节点的多变元属性,使用高级星形坐标法布局网络节点,以边融合及路由技术为基础设计算法,自动有效布局网络边,实现友好的人机交互界面辅助用户进一步对数据进行分析挖掘.实验结果表明,MulNetVisBasc的可视化结果能够在直观揭示数据集多变元分布特性的同时清晰展现其网络关联特性,有助于用户发掘多变元网络数据集中潜在的隐性知识.边布局算法能够有效减少视图中的边交叉数量,且复杂度较低,适用于较大规模数据集,人机交互界面灵活方便.     

一种基于KEGG数据库重构代谢网络的新方法

从代谢物、酶和生化反应信息重新构建正确的代谢网络是各项代谢网络相关研究非常关键的第一步。针对以往重构方法存在的数据难以及时更新、数据有冗余、获取数据慢等问题,本文采用分而治之的递归策略,提出了一种基于KEGG数据库自下而上重构全物种代谢网络的新方法。与以前的方法相比,本方法的优点在于:使用KEGG的Web服务获取数据,以保证数据的准确性和及时更新;依靠KEGG/PATHWAY库的数据选择机制选取数据,以保证构建网络的数据无冗余;整个方法基于Java实现,保证程序的跨平台通用性;通过构建MySQL本地数据库将远程数据本地化,大大降低数据读取的时耗。评估结果显示,该方法不仅能够保证重建网络数据的准确性和及时更新,而且有效地提高了多物种多次重构情况下的网络重构效率。     

代谢网络可视化系统的力导向布局算法设计

针对生物化学代谢反应网络可视化系统的优化设计,提出了一个基于力导向模型的解决方案。新方案根据具体的可视化目标,综合拓扑信息进行参数调整。实验表明,这些调整给可视化的布局质量带来很大的改善。另外,代谢网络具有鲜明的无尺度网络特性。据此,进一步提出了针对枢纽结点的优化方案,在视觉效果上突出了代谢反应网络的无尺度特性。为了融合力导向方案和基于无尺度网络特性的局部调整方案,最后总结了一个三阶段的布局算法框架。     

基于信息论方法的代谢网络模块化分析

预测代谢网络中的功能模块对于理解这些网络的结构与功能关系具有重要的意义。但是,当前的模块化分析方法大多将代谢网络考虑为普通的复杂网络,所得模块的生物学意义较低.本文简单介绍了一种基于信息论的分析技术,该方法通过描述图中信息的扩散过程,考察随机行走轨迹描述长度的变化来识别网络的模块化结构。随后,构建了苏云金杆菌的代谢网络模型,并提取了模型中的核心成分.该部分包含了118个节点和158条连线。最后,使用上述方法对苏云金杆菌代谢网络的核心成分进行了模块化分析,得到了11个功能模块,这些模块均对应于1-2个独特的KEGG途径,结果表明识别的模块均具备较好的生物学意义,该方法可用于识别代谢网络中的功能模块。     

一种基于Petri网的代谢流分析方法

基于Petri网理论构建生物代谢网络模型,提出一种新的采用不等式的代谢流分析方法。以克鲁斯假丝酵母分批发酵,生产甘油的代谢网络为例,验证这种分析方法。结果表明,新分析方法,在前提假设和分析性能两方面均比采用方程式的传统方法明显优越。首先,方法允许实验数据有一定误差,使模型更加接近真实的实验系统。其次,新方法还可以做代谢模型更详细的分析:代谢流分裂比分析、敏感性分析。实际上,分析结果还可直接用于校正代谢物浓度、酶活力的边界值,为进一步研究代谢途径优化提供约束条件。     

复杂网络的中心化及其在代谢网络中的应用

分析基因组规模的生化网络是后基因组时代的一项重要研究任务.由于缺乏详尽的热力学参数,近年来科研人员已经开发出了大量基于网络拓扑结构的分析方法.其中,中心化指标可用于确定网络中的重要节点,因而有助于理解代谢网络的交互和调控机制.本文首先比较地分析了10种不同的中心化指标,随后将它们运用于分析苏云金芽孢杆菌的代谢网络,确定了其代谢网络巨强连通成分中的10个关键节点并分析了它们的生物学功能意义.     

一种RFID读写器网络的设计及其实现

提出了一种基于中心控制节点的读写器网络结构。中心控制节点使整个读写器网络内的读写器能够协调工作，并负责网络内标签信息的采集和采集到的数据的输出。在此结构基础上提出了解决读写器冲突的算法。最后，给出了该读写器网络在Chipcon公司的低功耗射频无线收发芯片CC1010上的实现方案。     

求解泛函方程的泛函网络方法

给出一种求解泛函方程的泛函网络方法,设计了一种泛函网络模型用于逼近一类泛函方程的实根问题,并给出了相应地学习算法.该算法通过求解线性方程组可得到网络参数.相对于传统方法,该方法不但能够快速求出泛函方程的精确解,而且可获得所求泛函方程的近似解.计算机仿真结果表明,该算法可行有效.     

VSAN: A new visualization method for super-large-scale academic networks

As a carrier of knowledge, papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and technology. With the booming development of science and technology, the number of papers has been growing exponentially. Just like the fact that Internet of Things (IoT) allows the world to be connected in a flatter way, how will the network formed by massive academic papers look like? Most existing visualization methods can only handle up to hundreds of thousands of node size, which is much smaller than that of academic networks which are usually composed of millions or even more nodes. In this paper, we are thus motivated to break this scale limit and design a new visualization method particularly for super-large-scale academic networks (VSAN). Nodes can represent papers or authors while the edges means the relation (e.g., citation, coauthorship) between them. In order to comprehensively improve the visualization effect, three levels of optimization are taken into account in the whole design of VSAN in a progressive manner, i.e., bearing scale, loading speed, and effect of layout details. Our main contributions are two folded: 1) We design an equivalent segmentation layout method that goes beyond the limit encountered by state-of-the-arts, thus ensuring the possibility of visually revealing the correlations of larger-scale academic entities. 2) We further propose a hierarchical slice loading approach that enables users to observe the visualized graphs of the academic network at both macroscopic and microscopic levels, with the ability to quickly zoom between different levels. In addition, we propose a “jumping between nebula graphs” method that connects the static pages of many academic graphs and helps users to form a more systematic and comprehensive understanding of various academic networks. Applying our methods to three academic paper citation datasets in the AceMap database confirms the visualization scalability of VSAN in the sense that it can visualize academic networks with more than 4 million nodes. The super-large-scale visualization not only allows a galaxy-like scholarly picture unfolding that were never discovered previously, but also returns some interesting observations that may drive extra attention from scientists.     

时序网络构建的理论和方法

20世纪末复杂网络小世界与无标度特性的发现,使多类复杂系统的结构特性、动力学、决策与控制在21世纪初得到了前所未有的关注与发展.鉴于复杂网络在刻画复杂系统拓扑结构方面的有效性,首先介绍构建具有典型特征静态复杂网络的重要模型与方法,这些模型与方法使复杂网络的构建不再依赖有限且高成本的个体真实交互数据,为多领域研究人员探讨相关科学问题提供了便利条件.其次,随着高精度海量群体交互数据构建采集能力的不断提升,构建随时间演化的动态时序复杂网络成为可能,作为时序网络的一个典型特征,个体交互的时间间隔往往呈现幂律分布,即具有爆发特性,这种爆发特性可显著改变系统中的信息传播、博弈决策过程,鉴于此,总结对真实个体交互数据进行幂律分布定量检验的参数估计方法,介绍泊松过程与排队系统,给出几类时序网络构建的理论与方法.     

Stability analysis for coupled systems of fractional differential equations on networks

This paper considers the stability problem for some coupled systems of fractional differential equations on networks (CSFDENs). We provide a systematic method for constructing a Lyapunov function for CSFDENs by using graph theory and the Lyapunov method. Consequently, some sufficient conditions for stability, uniform stability and uniform asymptotic stability of CSFDENs are obtained. Finally, an example and some numerical simulations are presented to verify the effectiveness of the theoretical results.     

Application of the differential equations method for identifying communities in sparse networks

In our previous work a new method of identifying communities in networks was presented. The method is based on a time evolution of the network according to a set of differential equations. It was applied to networks consisting of fully connected sub-networks. However, networks describing real systems are often sparse. Here the method is applied to sparse networks. The results are compared with those of an agglomerative hierarchical method based on modularity maximisation proposed by Newman and a divisive method proposed by Duch and Arenas based also on optimisation of the modularity. Obtained results show that the differential equation method usually works better than two remaining methods, allowing for more appropriate identification of the network structure.     

A note on constructing binary heaps with periodic networks

We consider the problem of constructing binary heaps on constant degree networks performing compare-exchange operations only. The heap data structure, introduced by William and Williams [Comm. ACM 7 (6) (1964) 347-348], has many applications and, therefore, has been intensively studied in sequential and parallel context. In particular, Brodal and Pinotti [Theoret. Comput. Sci. 250 (2001) 235-245] have recently presented two families of comparator networks: the first of depth 4logN and the second of size O(NloglogN) for constructing binary heaps of size N. In this note, we give an new construction of such a network with the running time improved to 3logN. Moreover, the network has a novel property of being 3-periodic, that is, for each unit of time i the same sets of operations are performed in units i and i+3. Then we argue that our construction is optimal with respect to the length of the period, that is, we prove that there is no 2-periodic network that is able to build a binary heap in sublinear time. Finally, we show that our construction can be used to decrease also the depth of the networks with O(NloglogN) size.     

Legendre spectral method for solving integral and integro-differential equations

A new spectral approximation of an integral based on Legendre approximation at the zeros of the first term of the residual is presented. The method is used to solve integral and integro-differential equations. The method generates approximations to the lower order derivatives of the function through successive integrations of the Legendre polynomial approximation to the highest order derivative. Numerical results are included to confirm the efficiency and accuracy of the method.     

Application of abductive ILP to learning metabolic network inhibition from temporal data

In this paper we use a logic-based representation and a combination of Abduction and Induction to model inhibition in metabolic networks. In general, the integration of abduction and induction is required when the following two conditions hold. Firstly, the given background knowledge is incomplete. Secondly, the problem must require the learning of general rules in the circumstance in which the hypothesis language is disjoint from the observation language. Both these conditions hold in the application considered in this paper. Inhibition is very important from the therapeutic point of view since many substances designed to be used as drugs can have an inhibitory effect on other enzymes. Any system able to predict the inhibitory effect of substances on the metabolic network would therefore be very useful in assessing the potential harmful side-effects of drugs. In modelling the phenomenon of inhibition in metabolic networks, background knowledge is used which describes the network topology and functional classes of inhibitors and enzymes. This background knowledge, which represents the present state of understanding, is incomplete. In order to overcome this incompleteness hypotheses are considered which consist of a mixture of specific inhibitions of enzymes (ground facts) together with general (non-ground) rules which predict classes of enzymes likely to be inhibited by the toxin. The foreground examples are derived from in vivo experiments involving NMR analysis of time-varying metabolite concentrations in rat urine following injections of toxins. The model’s performance is evaluated on training and test sets randomly generated from a real metabolic network. It is shown that even in the case where the hypotheses are restricted to be ground, the predictive accuracy increases with the number of training examples and in all cases exceeds the default (majority class). Experimental results also suggest that when sufficient training data is provided, non-ground hypotheses show a better predictive accuracy than ground hypotheses. The model is also evaluated in terms of the biological insight that it provides. Editor: Rui Camacho     

New Rothe-wavelet method for solving telegraph equations

In this article, we present a new competitive scheme to solve one of the most important cases in hyperbolic partial differential equations which is called telegraph equations. This method is based on Rothe's approximation in time discretisation and on the wavelet-Galerkin in the spatial discretisation. For comparison of wavelets, we use sin–cos, Legendre and Daubechies wavelets as basis in projection methods. For showing efficiency of method, a numerical experiment, for which the exact solution is known, is considered.