• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Integrated study to infer dynamic protein-gene interactions in human p53 regulatory networks
【24h】

Integrated study to infer dynamic protein-gene interactions in human p53 regulatory networks

机译：整合研究推断人类p53调控网络中动态蛋白质与基因的相互作用

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Investigating the dynamics of genetic regulatory networks through high throughput experimental data, such as microarray gene expression profiles, is very important but challenging. One of the major hindrances in building detailed mathematical models for genetic regulation is the large number of unknown model parameters. To tackle this problem, a new integrated method is proposed by combining both the top-down and bottom-up approaches. Firstly, a top-down approach, using probability graphical models, is employed to predict the network structure of DNA repair pathway that involves p53 regulation. Then, a bottom-up approach, using differential equation models, is applied to study the detailed genetic regulations based on either a fully-connected regulatory network or gene networks inferred with the top-down approach. Optimal network is selected based on model simulation error and robustness property. Overall, the proposed new integrated method is efficient for studying large dynamical genetic regulations.
机译：通过高通量实验数据（例如微阵列基因表达谱）调查遗传调控网络的动力学非常重要，但极具挑战性。建立详细的遗传调控数学模型的主要障碍之一是大量未知的模型参数。为了解决这个问题，提出了一种新的集成方法，将自上而下和自下而上的方法结合在一起。首先，采用自上而下的方法，使用概率图形模型，来预测涉及p53调控的DNA修复途径的网络结构。然后，采用自下而上的方法，使用微分方程模型，基于完全连接的监管网络或通过自上而下的方法推断的基因网络，研究详细的遗传规则。根据模型仿真误差和鲁棒性选择最佳网络。总的来说，所提出的新的综合方法对于研究大型动态遗传规律是有效的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号