Department of Physics and Biological Design Center, Boston University, Boston, Massachusetts, United States of America.
Carl R. Woese Institute for Genomic Biology and Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
PLoS Comput Biol. 2023 Jan 10;19(1):e1010570. doi: 10.1371/journal.pcbi.1010570. eCollection 2023 Jan.
Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities grows exponentially with the number of species, and so an exhaustive search cannot be performed even for a dozen species. A heuristic search that improves community function by adding or removing one species at a time is more practical, but it is unknown whether this strategy can discover an optimal or nearly optimal community. Using consumer-resource models with and without cross-feeding, we investigate how the efficacy of search depends on the distribution of resources, niche overlap, cross-feeding, and other aspects of community ecology. We show that search efficacy is determined by the ruggedness of the appropriately-defined ecological landscape. We identify specific ruggedness measures that are both predictive of search performance and robust to noise and low sampling density. The feasibility of our approach is demonstrated using experimental data from a soil microbial community. Overall, our results establish the conditions necessary for the success of the heuristic search and provide concrete design principles for building high-performing microbial consortia.
组装最佳微生物群落对于生物燃料生产、农业和人类健康的各种应用至关重要。然而，由于可能的群落数量随着物种数量呈指数级增长，即使对于十几个物种，也无法进行详尽的搜索。因此，通过一次添加或删除一个物种来提高群落功能的启发式搜索更为实际，但尚不清楚这种策略是否可以发现最佳或接近最佳的群落。我们使用具有和不具有交叉喂养的消费者-资源模型，研究了搜索的功效如何取决于资源分布、生态位重叠、交叉喂养以及群落生态学的其他方面。我们表明，搜索功效取决于适当定义的生态景观的崎岖程度。我们确定了特定的崎岖度度量，这些度量既可以预测搜索性能，又可以抵抗噪声和低采样密度的影响。我们使用来自土壤微生物群落的实验数据证明了我们方法的可行性。总的来说，我们的结果确定了启发式搜索成功的必要条件，并为构建高性能微生物联合体提供了具体的设计原则。
