基于图表的深度学习,用于预测无氧消化系统中微生物组和生物气产量的变化
Hyo Gyeom Kim1, Sung Il Yu2, Seung Gu Shin3
1Future and Fusion Lab of Architectural, Civil and Environmental Engineering, Korea University, Seoul 02841, Korea.
Water research
|January 18, 2025
概括
图表卷积网络 (GCNs) 通过分析微生物相互作用和挥发性脂肪酸 (VFA) 来模拟无氧消化 (AD). 这种方法准确地预测了生物气生产和微生物动态,为优化AD系统提供了洞察力.
科学领域:
- 生物技术是生物技术.
- 环境科学 环境科学
- 微生物学 微生物学
背景情况:
- 无氧消化 (AD) 对可再生能源和废物处理至关重要,但微生物复杂性和挥发性脂肪酸 (VFA) 阻碍了优化.
- 了解复杂的微生物相互作用是提高AD效率的关键.
研究的目的:
- 应用图形卷积网络 (GCNs) 进行AD过程的全面建模.
- 通过整合网络分析和VFA抑制数据来预测微生物动态和沼气生产.
主要方法:
- 开发了集成高通量测序数据和VFA效应的GCN模型.
- 在各种养条件下 (冲击,饥饿,生物增量) 在281天的无氧消化器数据上训练模型.
- 利用下一代测序和图形拓特征来分析微生物转移和VFA-微生物合.
主要成果:
- 成功识别的微生物社区跨AD阶段和条件的转移.
- 在VFA和微生物家族之间发现了显著的合,其中食古生物经常连接在一起.
- GCN模型准确地预测了微生物丰度 (R2=0.72) 和气体产量 (R2=0.87).
结论:
- 通过整合微生物网络和化学参数,GCN为AD系统建模提供了一个新的框架.
- 这项研究提供了对饥饿和生物增加对微生物群的影响的见解.
- 这种方法增强了对无氧处理过程和反应堆生产率的理解和优化.
相关概念视频
Microbes and Methanogenesis
Methanogenesis is a critical microbial process in anaerobic ecosystems responsible for the biological production of methane, a potent greenhouse gas and valuable biofuel. This metabolic pathway is primarily facilitated by methanogenic archaea, which thrive in anoxic environments such as wetlands, sediments, and animal gastrointestinal tracts. The absence of oxygen in these habitats prevents aerobic respiration, thereby favoring alternative biochemical pathways for organic matter degradation.In...
Microbes and Climate Change
Microorganisms are pivotal agents in Earth's biogeochemical cycles, significantly influencing climate dynamics through their metabolic activities. These microbes modulate the levels of key greenhouse gases by both contributing to and helping mitigate climate change.Microbial Contributions to Greenhouse Gas EmissionsRising global temperatures accelerate microbial metabolism, which, in turn, speeds up the decomposition of organic matter. This process releases carbon dioxide (CO₂) through...
Microbial Biosensors
Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...
Automated Microbial Diagnostics
Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...


