Just try to wrap your head around this impossible task: if each gram of soil holds thousands of different species of microorganisms, how can you detect and describe how those various members interact? A new study just released by mBio offers up one way to approach the problem, using a conceptual framework for analyzing high throughput functional gene array hybridization data.
Zhou et al. applied functional gene hybridization analysis to soils from a long-term ecological experiment site, then used a random matrix theory-based conceptual framework to identify the ecological networks among those genes. The results revealed notable differences between the structure of soil communities exposed to elevated carbon dioxide levels and ambient carbon dioxide levels, suggesting that increases in carbon dioxide that are possible as a result of global climate change can dramatically alter the network interactions among different microbial functional genes/populations.
Zhou et al. applied functional gene hybridization analysis to soils from a long-term ecological experiment site, then used a random matrix theory-based conceptual framework to identify the ecological networks among those genes. The results revealed notable differences between the structure of soil communities exposed to elevated carbon dioxide levels and ambient carbon dioxide levels, suggesting that increases in carbon dioxide that are possible as a result of global climate change can dramatically alter the network interactions among different microbial functional genes/populations.
