Genetic Neural Network Model of Flowering Time Control in Arabidopsis thaliana
Stephen M. Welch, Judith L. Roe, Zhanshan Dong. Genetic Neural Network Model of Flowering Time Control in Arabidopsis thaliana. Agronomy Journal. 2003, 95(1):71-81 [PDF]
Abstract
Crop simulation models incorporate many physiological processes within sophisticated mathematical frameworks. However, the control mechanisms for these processes tend to be ad hoc, empirical, and indirectly inferred from data and many lack realistic plasticity. Using model organisms like Arabidopsis thaliana, genomic scientists are rapidly disentangling the networks of genes that exert physiological control. As yet, however, these networks are qualitative in nature, depicting promotion and inhibition pathways but not supporting quantitative predictions of overall integrated effects. We believe (i) that neural networks can provide the quantification that current genetic networks lack and (ii) that taxonomic conservation of central genetic mechanisms will make networks developed for model plant also useful in crops. This paper presents evidence supporting the first point based on a neural network with eight nodes corresponding to A. thaliana genes controlling inflorescence timing. The nodes were linked into photoperiod and autonomous pathways abstracted from an existing qualitative genetic network model. Growth chamber data on transition timing were collected at 16 and 24oC for seven A. thaliana strains possessing loss-of-function mutations at the network loci. An eight strain served as a common wild-type control. The neural network model reproduced the time course of the transition at both temperatures for all eight genotypes. Results included tracking a novel, temperature-dependent exchange in transition order exhibited by two mutants whose duplication is not possible by usual crop simulation methods. Furthermore, the ability to imitate the data appeared to have a desirable sensitivity to assumed network structure.
文章摘要
作物模拟模型使用复杂的数学公式表达了许多生理过程。然而，这些生理过程的控制机理都是凭经验从数据间接推出的，缺少真实世界中的弹性。利用模型植物拟南芥，基因组科学家正在迅速揭开控制生理过程的基因网络。到目前为止，这些基因网络都是描述促进和抑制途径的定性网络，不支持整合各种效应的定量预测。我们相信(i)神经网络可以提供目前基因网络缺乏的定量功能和（ii）由于模式植物中的核心遗传机制在植物分类上的保守性，这些基因网络对作物也是有用的。本文用控制拟南芥开花的有八个基因节点的神经网络为第一方面提供了证据。这些基因节点是从现有的定性遗传网络模型中选取用来将连接到光周期路径和自动路径。开花期数据是在16或24摄氏度的生长箱中收集的。试验材料为七个丧失功能的拟南芥基因突变体和野生对照种。神经网络模型重复了所有八个基因型在两个不同温度下的开花时间。本模型还可以模拟有两个突变体表现出来的一种全新的依赖温度变化出现的花期顺序互换，这对普通的作物模型来说是不可能。此外，模仿数据的能力显示了对网络结构的理想的敏感性。
