Larsen Peter E, Cseke Leland J, Miller R Michael, Collart Frank R
Argonne National Laboratory, Biosciences Division, 9700 South Cass Avenue, Argonne, IL 60439, USA.
Department of Biological Sciences, University of Alabama in Huntsville, Huntsville, AL 35899, USA.
J Theor Biol. 2014 Oct 21;359:61-71. doi: 10.1016/j.jtbi.2014.05.047. Epub 2014 Jun 10.
Rising atmospheric levels of carbon dioxide and ozone will impact productivity and carbon sequestration in forest ecosystems. The scale of this process and the potential economic consequences provide an incentive for the development of models to predict the types and rates of ecosystem responses and feedbacks that result from and influence of climate change. In this paper, we use phenotypic and molecular data derived from the Aspen Free Air CO2 Enrichment site (Aspen-FACE) to evaluate modeling approaches for ecosystem responses to changing conditions. At FACE, it was observed that different aspen clones exhibit clone-specific responses to elevated atmospheric levels of carbon dioxide and ozone. To identify the molecular basis for these observations, we used artificial neural networks (ANN) to examine above and below-ground community phenotype responses to elevated carbon dioxide, elevated ozone and gene expression profiles. The aspen community models generated using this approach identified specific genes and subnetworks of genes associated with variable sensitivities for aspen clones. The ANN model also predicts specific co-regulated gene clusters associated with differential sensitivity to elevated carbon dioxide and ozone in aspen species. The results suggest ANN is an effective approach to predict relevant gene expression changes resulting from environmental perturbation and provides useful information for the rational design of future biological experiments.
大气中二氧化碳和臭氧水平的上升将影响森林生态系统的生产力和碳固存。这一过程的规模和潜在的经济后果促使人们开发模型，以预测气候变化产生的影响以及受气候变化影响而产生的生态系统响应和反馈的类型及速率。在本文中，我们使用从白杨自由空气二氧化碳富集试验点（Aspen - FACE）获得的表型和分子数据，来评估生态系统对变化条件响应的建模方法。在FACE试验点，人们观察到不同的白杨无性系对大气中二氧化碳和臭氧水平升高呈现出特定无性系的响应。为了确定这些观察结果的分子基础，我们使用人工神经网络（ANN）来研究地上和地下群落表型对二氧化碳升高、臭氧升高和基因表达谱的响应。使用这种方法生成的白杨群落模型确定了与白杨无性系可变敏感性相关的特定基因和基因子网络。ANN模型还预测了与白杨树种对二氧化碳和臭氧升高的不同敏感性相关的特定共调控基因簇。结果表明，ANN是预测环境扰动导致的相关基因表达变化的有效方法，并为未来生物学实验的合理设计提供了有用信息。
