Neural-network-based parameter estimation in S-system models of biological networks.
Neural-network-based parameter estimation in S-system models of biological networks.
复制标题
DOI：
10.11234/gi1990.14.114
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Jonas S. Almeida;E. Voit
中科院分区：
文献类型：
--
作者：
Jonas S. Almeida;E. Voit
基因组时代和后基因组时代已经为我们带来了大量的数据，这些数据的质量越来越高。挑战在于，这些数据中的大多数仅仅是功能有机体的快照，并没有揭示特定基因和代谢物所贡献的组织结构。为了了解它们在细胞和生物体中的作用和功能，需要将基因组和代谢数据整合到系统模型中，以测试假设，生成实验可测试的预测，并最终导致真正的解释。特别适合于这种整合的一种类型的数据包括时间曲线，其显示在密集系列时间点的基因活性、代谢物浓度或蛋白质流行率。我们展示了一个具体的例子，这样的时间序列可以分析和评估，如果一些结构信息的数据是可用的，即使这些信息是不完整的。该方法由三个部分组成。第一个是一个特别合适的数学建模框架，即生化系统理论，其中参数是组织的基本现象的直接指标，第二个是训练的人工神经网络的数据平滑和补充，第三个是一种技术，重新解释微分方程的方式，有利于参数估计。这些分析的原型网络工具可在https://bioinformatics.musc.edu/webmetabol/上获得。
The genomic and post-genomic eras have been blessing us with overwhelming amounts of data that are of increasing quality. The challenge is that most of these data alone are mere snapshots of the functioning organism and do not reveal the organizational structure of which the particular genes and metabolites are contributors. To gain an appreciation of their roles and functions within cells and organisms, genomic and metabolic data need to be integrated in systems models that allow the testing of hypotheses, generate experimentally testable predictions, and ultimately lead to true explanations. One type of data that is particularly well suited for such integration consists of time profiles, which show gene activities, metabolite concentrations, or protein prevalences at dense series of time points. We show with a specific example how such time series can be analyzed and evaluated, if some structural information about the data is available, even if this information is incomplete. The method consists of three components. The first is a particularly suitable mathematical modeling framework, namely Biochemical Systems Theory, in which parameters are direct indicators of the organization of the underlying phenomenon, the second is the training of an artificial neural network for data smoothing and complementation, and the third is a technique for reinterpreting differential equations in a fashion that facilitates parameter estimation. A prototype webtool for these analyses is available at https://bioinformatics.musc.edu/webmetabol/.