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原文連結
論文資訊
- 類型：已發表論文
- 日期：2023
摘要
Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and 氣候. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial 物種. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to pred
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