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Information Technoloy for Gene Network Analysis

基因网络分析信息技术

基本信息

批准号：
15014205
负责人：
MIYANO Satoru
金额：
$ 20.48万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research on Priority Areas
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15014205/
关键词：
gene network Bayesian network nonparametric regression simulation microarray analysis Petri net systems biology search algorithm

项目摘要

We developed information technology for estimating gene networks accurately from multiple source of genetic information including microarray gene expression data. We also developed an XML format for describing dynamic models of gene networks. The following results are obtained:(1)We developed a computational method for estimating gene networks from microarray data obtained from various perturbations such as gene disruptions, gene overexpressions, drug responses, etc. The method combines the Bayesian network approach with nonparametric regression, where genes are regarded as random variables and the nonparametric regression enables us to capture from linear to nonlinear structures between genes. As a criterion for choosing good networks, we introduced the BNRC (Bayesian network and Nonparametric Regression Criterion) score. Naturally, the sole use of microarray data has limitations on gene network estimation. For improving the biological accuracy of estimated gene networks, we have made … More a general framework by extending this method so that it can employ genome-wide other biological information such as sequence information on promoter regions, protein-protein interactions. Computational experiments were conducted with yeast data and they show that cascades of gene regulations were effectively extracted.(2)The problem of finding an optimal Bayesian network is known computationally intractable for BNRC, BDe, MDL scores. For example, in order to find an optimal Bayesian network of 20 genes from 100 microarray data, the brute force algorithm employing all computing resources in the world even requires the time exceeding the life time of the solar system. Our recent computational challenge has made possible to search and enumerate optimal and suboptimal Bayesian networks in feasible time on supercomputers. Computational experiments with this search algorithm have provided evidences of the biological rationality of our computational strategy We obtained the following scientific knowledge: (i) Optimal Bayesian networks do not necessarily reflect the most accurate biological knowledge. (ii) Gene・gene relations which appear very frequently in optimal and suboptimal networks are biologically very likely. (iii) BNRC score is much better than BDe and MDL scores for gene network estimation. Furthermore, by putting constraints arising from biological knowledge, a faster algorithm is also developed.(3)We designed and developed an XML called CSML Version 1.0 (Cell System ML) for describing and simulating dynamic models of gene networks. Based on this, we developed programs which automatical convert pathway models in KEGG and BioCyc to CSML models so that dynamic models can be constructed. Cell Illustrator is used for modeling and simulation of the models which employs CSML for model description. This framework enables us to develope large-scale metabolic pathway models for simulation. Less

我们开发了从包括微阵列基因表达数据在内的多种遗传信息来源准确估计基因网络的信息技术。我们还开发了一种用于描述基因网络动态模型的XML格式。(1)发展了一种从基因干扰、基因过度表达、药物反应等各种扰动下获得的微阵列数据估计基因网络的计算方法，该方法将贝叶斯网络方法与非参数回归相结合，其中基因被视为随机变量，非参数回归使我们能够捕捉基因之间从线性到非线性的结构。作为选择好网络的标准，我们引入了贝叶斯网络和非参数回归准则(BNRC)得分。自然，单独使用微阵列数据在基因网络估计方面有局限性。为了提高估计基因网络的生物学准确性，我们制作了…通过扩展该方法以使其能够利用基因组范围内的其他生物信息，例如启动子区域的序列信息、蛋白质-蛋白质相互作用。使用酵母数据进行了计算实验，结果表明，该方法能够有效地提取基因调控的级联。(2)对于BNRC、BDE、MDL分数，寻找最优贝叶斯网络的问题是众所周知的计算难题。例如，为了从100个微阵列数据中找到一个由20个基因组成的最优贝叶斯网络，使用世界上所有计算资源的蛮力算法甚至需要超过太阳系寿命的时间。我们最近的计算挑战使得在可行的时间内在超级计算机上搜索和列举最优和次优贝叶斯网络成为可能。用这种搜索算法进行的计算实验证明了我们的计算策略的生物合理性。我们获得了以下科学知识：(I)最优贝叶斯网络不一定反映最准确的生物知识。(Ii)在最优和次优网络中经常出现的基因·基因关系在生物学上是非常可能的。(3)对于基因网络的估计，BNRC评分明显优于BDE和MDL评分。设计并开发了一个用于描述和模拟基因网络动态模型的可扩展标记语言CSML Version 1.0(Cell System ML)。在此基础上，我们开发了将KEGG和BioCyc中的通路模型自动转换为CSML模型的程序，从而可以构建动态模型。使用Cell Illustrator对模型进行建模和仿真，并使用CSML进行模型描述。这一框架使我们能够开发用于模拟的大规模代谢途径模型。较少