Martinez Neo D
Center for Complex Networks and Systems, School of Informatics, Computing, and Engineering Indiana University, Bloomington Indiana Bloomington USA.
Pacific Ecoinformatics and Computational Ecology Lab CA Berkeley USA.
Ecol Evol. 2023 Mar 8;13(3):e9872. doi: 10.1002/ece3.9872. eCollection 2023 Mar.
Elucidating how an organism's characteristics emerge from its DNA sequence has been one of the great triumphs of biology. This triumph has cumulated in sophisticated computational models that successfully predict how an organism's detailed phenotype emerges from its specific genotype. Inspired by that effort's vision and empowered by its methodologies, a grand challenge is described here that aims to predict the biotic characteristics of an ecosystem, its metaphenome, from nucleic acid sequences of all the species in its community, its metagenome. Meeting this challenge would integrate rapidly advancing abilities of environmental nucleic acids (eDNA and eRNA) to identify organisms, their ecological interactions, and their evolutionary relationships with advances in mechanistic models of complex ecosystems. Addressing the challenge would help integrate ecology and evolutionary biology into a more unified and successfully predictive science that can better help describe and manage ecosystems and the services they provide to humanity.
阐明生物体的特征如何从其DNA序列中显现出来，一直是生物学的重大成就之一。这一成就已汇聚成复杂的计算模型，这些模型成功地预测了生物体的详细表型如何从其特定基因型中产生。受这一努力的愿景启发，并借助其方法，本文描述了一项重大挑战，即旨在从生态系统群落中所有物种的核酸序列（其宏基因组）预测该生态系统的生物特征，即其元表型。应对这一挑战将把环境核酸（eDNA和eRNA）在识别生物体、它们的生态相互作用以及它们与复杂生态系统机制模型进展之间的进化关系方面迅速提升的能力整合起来。应对这一挑战将有助于把生态学和进化生物学整合为一门更统一且能成功预测的科学，从而更好地帮助描述和管理生态系统以及它们为人类提供的服务。
