• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Frontiers in Genetics >Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response
【2h】

Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response

机译：使用人工神经网络建模调控级联：在酵母应激反应过程中形成的转录调控网络的情况

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Over the last decade, numerous computational methods have been developed in order to infer and model biological networks. Transcriptional networks in particular have attracted significant attention due to their critical role in cell survival. The majority of network inference methods use genome-wide experimental data to search for modules of genes with coherent expression profiles and common regulators, often ignoring the multi-layer structure of transcriptional cascades. Modeling methodologies on the other hand assume a given network structure and vary significantly in their algorithmic approach, ranging from over-simplified representations (e.g., Boolean networks) to detailed -but computationally expensive-network simulations (e.g., with differential equations). In this work we use Artificial Neural Networks (ANNs) to model transcriptional regulatory cascades that emerge during the stress response in Saccharomyces cerevisiae and extend in three layers. We confine the structure of the ANNs to match the structure of the biological networks as determined by gene expression, DNA-protein interaction and experimental evidence provided in publicly available databases. Trained ANNs are able to predict the expression profile of 11 target genes across multiple experimental conditions with a correlation coefficient >0.7. When time-dependent interactions between upstream transcription factors (TFs) and their indirect targets are also included in the ANNs, accurate predictions are achieved for 30/34 target genes. Moreover, heterodimer formation is taken into account. We show that ANNs can be used to (1) accurately predict the expression of downstream genes in a 3-layer transcriptional cascade based on the expression of their indirect regulators and (2) infer the condition- and time-dependent activity of various TFs as well as during heterodimer formation. We show that a three-layer regulatory cascade whose structure is determined by co-expressed gene modules and their regulators can successfully be modeled using ANNs with a similar configuration.
机译：在过去的十年中，已经开发了许多计算方法以推断和建模生物网络。转录网络由于其在细胞存活中的关键作用，尤其引起了人们的极大关注。大多数网络推论方法都使用全基因组实验数据来搜索具有连贯表达谱和共同调控因子的基因模块，而这些模块通常会忽略转录级联的多层结构。另一方面，建模方法采用给定的网络结构，其算法方法也有很大不同，从过度简化的表示形式（例如布尔网络）到详细但计算量大的网络模拟（例如使用微分方程）。在这项工作中，我们使用人工神经网络（ANN）来模拟转录调节级联，这些级联在酿酒酵母的应激反应过程中出现，并延伸为三层。我们限制了人工神经网络的结构，以匹配由基因表达，DNA-蛋白质相互作用和公开数据库中提供的实验证据确定的生物网络的结构。经过训练的人工神经网络能够在多种实验条件下预测11个靶基因的表达谱，相关系数> 0.7。当上游转录因子（TFs）与它们的间接靶标之间的时间依赖性相互作用也包括在ANN中时，可以对30/34个靶标基因进行准确的预测。此外，考虑了异二聚体的形成。我们显示ANN可以用于（1）根据其间接调节子的表达准确预测下游基因在3层转录级联中的表达，以及（2）推断各种TF的条件和时间依赖性活性为以及在异二聚体形成过程中。我们显示了三层调节级联，其结构由共表达的基因模块及其调节剂决定，可以使用具有类似配置的ANN成功建模。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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