信号转导网络的生物信息学分析

研究信号转导是了解生命活动过程的重要途径。随着实验方法的改进和实验数据的积累,很多信号转导通路的作用机制已经被揭示,对于已有信号转导数据的分析和利用已成为热点问题。本文综述了最近几年生物信息学在信号转导网络分析方面取得的最新进展,简要介绍了信号转导的特点和作用机制,并对网上相关的数据库资源进行总结,给出了信号转导网络的结构分析方法,包括网络的拓扑属性分析、结构模块搜索及信号通路的自动生成,重点对信号转导网络的建模和仿真方法进行了讨论,分析了该领域的研究现状及可能的发展方向。总体而言,对于信号转导网络的研究已经从小规模的实验研究向大规模的网络分析方向发展,对于网络的动态模拟更加接近真实系统。随着对信号转导的研究更加广泛和深入,对于信号转导网络的生物信息学分析将具有广阔的发展和应用前景。     

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。     

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。     

基于约束的建模方法在代谢网络中的应用研究

代谢网络在各种细胞功能和生命过程中发挥着至关重要的作用。随着细胞网络重建工程的迅速发展,可用的基因组水平代谢网络越来越多,因而计算方法在这些网络的结构功能分析中越来越重要。基于约束的建模方法不像图论方法那样仅考虑代谢模型的纯拓扑结构,也不像各种动力学建模方法那样需求详尽的热力学参数,因而极具优势。采用基于约束的建模方法对一个含619个基因,655个代谢物和743个代谢反应的金黄色葡萄球菌(Staphylococcusaureus)代谢网络进行了分析,主要研究了该模型的网络结构特征,以及其最优生长率、动态生长情况和基因删除学习等。本研究提供了一个对金黄色葡萄球菌代谢网络进行约束建模分析的初步框架。     

基于质谱的蛋白质组学信号网络研究进展

细胞信号转导网络调控着所有细胞和器官的生物学过程。以往信号转导网络的研究主要采用一些生物化学方法开展,如抗体技术。目前,基于质谱的大规模蛋白质组学研究可以在翻译后修饰、蛋白质互作及蛋白质表达水平上,系统地研究信号转导事件。基于蛋白质组学的大规模信号转导的研究将改变我们对信号转导网络的理解。从蛋白质组翻译后修饰、蛋白质互作及蛋白质表达3个方面综述了质谱在信号转导方面的研究。     

基于熵准则的鲁棒的RBF谷胱甘肽发酵建模

在谷胱甘肽的发酵过程建模中, 当试验数据含有噪音时, 往往会导致模型预测精度和泛化能力的下降。针对该问题, 提出了一种新的基于熵准则的RBF神经网络建模方法。与传统的基于MSE准则函数的建模方法相比, 新方法能从训练样本的整体分布结构来进行模型参数学习, 有效地避免了传统的基于MSE准则的RBF网络的过学习和泛化能力差的缺陷。将该模型应用到实际的谷胱甘肽发酵过程建模中, 实验结果表明: 该方法具有较高的预测精度、泛化能力和良好的鲁棒性, 从而对谷胱甘肽的发酵建模有潜在的应用价值。     

基于组学数据构建基因调控网络

在生命体内,基因以及其它分子间相互作用形成复杂调控网络,生命过程都是以调控网络的形式存在,如从代谢通路网络到转录调控网络,从信号转导网络到蛋白质相互作用网络等等。因此,网络现象是生命现象的复杂本质和主要特征。本文系统地介绍了基于表达谱数据构建基因调控网络的布尔网络模型,线性模型,微分方程模型和贝叶斯网络模型,并对各种网络构建模型进行了深入的分析和总结。同时,文章从基因组序列信息、蛋白质相互作用信息和生物医学文献信息等方面讨论了基因调控网络方面构建的研究,这对从系统生物学水平揭示生命复杂机制具有重要的参考价值。     

基于熵准则的鲁棒的RBF谷胱甘肽发酵建模

在谷胱甘肽的发酵过程建模中, 当试验数据含有噪音时, 往往会导致模型预测精度和泛化能力的下降。针对该问题, 提出了一种新的基于熵准则的RBF神经网络建模方法。与传统的基于MSE准则函数的建模方法相比, 新方法能从训练样本的整体分布结构来进行模型参数学习, 有效地避免了传统的基于MSE准则的RBF网络的过学习和泛化能力差的缺陷。将该模型应用到实际的谷胱甘肽发酵过程建模中, 实验结果表明: 该方法具有较高的预测精度、泛化能力和良好的鲁棒性, 从而对谷胱甘肽的发酵建模有潜在的应用价值。     

基因组尺度集成细胞网络模型研究进展

细胞网络研究是系统生物学的一个研究热点,通过结合计算机模型和实验技术,从系统角度分析复杂的生物系统,可以为生物实验提供指导和预测。近十年来,国内外许多研究团队致力于基因组规模代谢网络、基因调控网络和信号转导网络模型的构建和分析,并取得了一定成果。而不同类型网络的集成和分析是当前生物网络研究中一个新的方向,并带来了诸多新的挑战。在本文中,主要对基因组尺度集成细胞网络模型的研究进展,特别是对代谢网络和转录网络的集成进行了详细论述,着重于集成网络的构建和分析方法,最后对该领域研究前景进行了展望。     

基于信息度量的基因网络建模

基因网络作为生物系统三大网络中最基础的组成部分,是目前系统生物学研究的一大热点。本文分析了目前基因网络。建模的各种方法。井着重分析了布尔网络的建模特点。提出了利用信息理论中的信息熵和互信息方法来分析基因网络的各元素之间的布尔逻辑关系。通过实例分析说明了该方法在布尔网络建模中的有效性。     

Computational approaches for modeling regulatory cellular networks

Cellular components interact with each other to form networks that process information and evoke biological responses. A deep understanding of the behavior of these networks requires the development and analysis of mathematical models. In this article, different types of mathematical representations for modeling signaling networks are described, and the advantages and disadvantages of each type are discussed. Two experimentally well-studied signaling networks are then used as examples to illustrate the insight that could be gained through modeling. Finally, the modeling approach is expanded to describe how signaling networks might regulate cellular machines and evoke phenotypic behaviors.     

Short-term information processing, long-term responses: Insights by mathematical modeling of signal transduction. Early activation dynamics of key signaling mediators can be predictive for cell fate decisions

How do cells interpret information from their environment and translate it into specific cell fate decisions? We propose that cell fate is already encoded in early signaling events and thus can be predicted from defined signal properties. Specifically, we hypothesize that the time integral of activated key signaling molecules can be correlated to cellular behavior such as proliferation or differentiation. The identification of these decisive key signal mediators and their connection to cell fate is facilitated by mathematical modeling. A possible mechanistic linkage between signaling dynamics and cellular function is the directed control of gene regulatory networks by defined signals. Targeted experiments in combination with mathematical modeling can increase our understanding of how cells process information and realize distinct cell fates.     

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.     

Network modeling of signal transduction: establishing the global view

Embryonic development and adult tissue homeostasis are controlled through activation of intracellular signal transduction pathways by extracellular growth factors. In the past, signal transduction has largely been regarded as a linear process. However, more recent data from large-scale and high-throughput experiments indicate that there is extensive cross-talk between individual signaling cascades leading to the notion of a signaling network. The behavior of such complex networks cannot be predicted by simple intuitive approaches but requires sophisticated models and computational simulations. The purpose of such models is to generate experimentally testable hypotheses and to find explanations for unexpected experimental results. Here, we discuss the need for, and the future impact of, mathematical models for exploring signal transduction in different biological contexts such as for example development.     

Modeling Regulatory Networks to Understand Plant Development: Small Is Beautiful

We now have unprecedented capability to generate large data sets on the myriad genes and molecular players that regulate plant development. Networks of interactions between systems components can be derived from that data in various ways and can be used to develop mathematical models of various degrees of sophistication. Here, we discuss why, in many cases, it is productive to focus on small networks. We provide a brief and accessible introduction to relevant mathematical and computational approaches to model regulatory networks and discuss examples of small network models that have helped generate new insights into plant biology (where small is beautiful), such as in circadian rhythms, hormone signaling, and tissue patterning. We conclude by outlining some of the key technical and modeling challenges for the future.     

Mass and Information Feedbacks through Receptor Endocytosis Govern Insulin Signaling as Revealed Using a Parameter-free Modeling Framework

Insulin and other hormones control target cells through a network of signal-mediating molecules. Such networks are extremely complex due to multiple feedback loops in combination with redundancy, shared signal mediators, and cross-talk between signal pathways. We present a novel framework that integrates experimental work and mathematical modeling to quantitatively characterize the role and relation between co-existing submechanisms in complex signaling networks. The approach is independent of knowing or uniquely estimating model parameters because it only relies on (i) rejections and (ii) core predictions (uniquely identified properties in unidentifiable models). The power of our approach is demonstrated through numerous iterations between experiments, model-based data analyses, and theoretical predictions to characterize the relative role of co-existing feedbacks governing insulin signaling. We examined phosphorylation of the insulin receptor and insulin receptor substrate-1 and endocytosis of the receptor in response to various different experimental perturbations in primary human adipocytes. The analysis revealed that receptor endocytosis is necessary for two identified feedback mechanisms involving mass and information transfer, respectively. Experimental findings indicate that interfering with the feedback may substantially increase overall signaling strength, suggesting novel therapeutic targets for insulin resistance and type 2 diabetes. Because the central observations are present in other signaling networks, our results may indicate a general mechanism in hormonal control.     

Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis

Mathematical modeling is required for understanding the complex behavior of large signal transduction networks. Previous attempts to model signal transduction pathways were often limited to small systems or based on qualitative data only. Here, we developed a mathematical modeling framework for understanding the complex signaling behavior of CD95(APO-1/Fas)-mediated apoptosis. Defects in the regulation of apoptosis result in serious diseases such as cancer, autoimmunity, and neurodegeneration. During the last decade many of the molecular mechanisms of apoptosis signaling have been examined and elucidated. A systemic understanding of apoptosis is, however, still missing. To address the complexity of apoptotic signaling we subdivided this system into subsystems of different information qualities. A new approach for sensitivity analysis within the mathematical model was key for the identification of critical system parameters and two essential system properties: modularity and robustness. Our model describes the regulation of apoptosis on a systems level and resolves the important question of a threshold mechanism for the regulation of apoptosis.     

A survey on methods for modeling and analyzing integrated biological networks

Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how they operate as an ensemble. With the recent advances in the "omics" technologies, more data is becoming available and, thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and in particular subsystems of various organisms.