环境普查:用元基因组酶数据和高性能计算建模合成生物学生态风险
John Docter1, Cresten Mansfeldt1,2
1University of Colorado Boulder, Environmental Engineering Program, 4001 Discovery Drive, Boulder, Colorado 80309, United States.
ACS synthetic biology
|December 3, 2025
概括
一个新的Python软件包EnCen预测了工程微生物的环境范围,以评估生物安全风险. 这个工具有助于管理合成生物学.
科学领域:
- 合成生物学 合成生物学
- 环境微生物学 环境微生物学
- 计算生物学 计算生物学
背景情况:
- 生物技术中的工程微生物带来了生物安全和环境管理的挑战.
- 合成生物学市场的增长需要工具来预测逃脱的工程生物的环境影响.
- 目前的方法缺乏足够的预测能力来评估企业的风险和环境位置.
研究的目的:
- 开发和介绍EnCen,一个用于工程微生物风险评估的Python软件包.
- 预测工程微生物的环境范围和建立风险.
- 帮助安全设计和管理合成生物学应用.
主要方法:
- EnCen使用工程和居民微生物之间的功能性一热编码相似性来预测环境范围.
- 使用公开可用的复合元基因组,代表农业水循环和可定制环境.
- 与文学案例研究和商业细菌生物农药相对应.
主要成果:
- 在案例研究中成功地重新获取了先前报告的动态.
- 确定了在多种环境中具有更广泛的建立风险的商业生物农药.
- 将关键酶类映射到环境中,优先考虑建立风险或有效性的修改.
结论:
- EnCen有效地总结了元基因组数据,以优先考虑监测不良影响的环境.
- 提供对微生物群落组成和功能潜在影响的分析.
- 展示了用于管理工程微生物的计算方法,支持安全的工业合成生物学部署.
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