Reasoning to Predict Fate of Chemicals in the Environment

推理预测环境中化学品的归宿

• 批准号: 0543416
• 负责人: Lawrence Wackett
• 金额: $ 61.47万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0543416&HistoricalAwards=false
• 关键词: Reasoning; Predict; Fate; Chemicals; Environment

The Universityof Minesota is awarded a grant to further develop a prototype computational system that predicts metabolic pathways for any given compound. The current system predicts all possible pathways based on metabolic rules; hundreds of pathway branches are generated. To use this effectively to predict the fate of chemicals in the environment, reasoning capabilities will need to be added. The reasoning tools, developed during the research proposed here, will select likely pathways, and be a platform for discerning different chemical fates in different environments, as needed by regulators and industry. The research project is entirely in the public domain and will connect to many partners worldwide. It will be synergistic with the efforts of Proctor & Gamble, Brussels and the ALARM project of the 6th European Union Framework Program. The Pathway Predictive System (PPS) is based on 250 metabolic rules. PPS rules simulate virtually all plausible biochemical pathways for a compound, based on comparison with human experts, known pathways and bench experimental verification. To move toward a true depiction of environmental fate, the first standard condition to be simulated will be the aerobic environment, with moderate moisture and salinity, no competing chemical toxicants, and at 25oC. A comprehensive approach will be used to prioritize biotransformation rules. First, human experts will rate a rule as applying under those conditions on a 1-5 scale: 1) Highly likely; 2) Likely; 3) Neutral; 4) Unlikely; 5) Highly unlikely. This will provide an ordinal ranking of reactions. Expert knowledge will be augmented using thermodynamic techniques. Preliminary data suggests that thermodynamics can minimally select against futile metabolic cycles and thermodynamically unfavorable isomerization reactions. Rapidly-growing microbial genomic data will also be studied to improve biotransformation prioritization. The project will quantitatively assess the prevalence of genes and enzymes that participate in reactions covered by current PPS rules. There will be a further assessment of whether those genes and enzymes are found in bacteria specific to certain environments, such as aerobes, anaerobes, and halophiles. The system will be developed using the resources of the University of Minnesota Supercomputing Institute and be publicly mirrored in Europe, for use by participants in the ALARM program and other clients. The rules and use of the decision-making software will be freely available to all users on the web. The system will also be used educationally in University classes throughout the nation via a network of collaborating academicians. This will be used to teach students chemistry, metabolic biochemistry, genomics and environmental sciences, and help us validate the pathway priority prediction system. All of the results will be made available to the scientific community through our website.

Minesota大学获得了一笔赠款，以进一步开发一个原型计算系统，预测任何给定化合物的代谢途径。目前的系统基于代谢规则预测所有可能的途径;生成数百个途径分支。为了有效地利用这一点来预测环境中化学品的命运，需要增加推理能力。在这里提出的研究期间开发的推理工具将选择可能的途径，并根据监管机构和行业的需要，成为在不同环境中识别不同化学品命运的平台。该研究项目完全属于公共领域，并将与世界各地的许多合作伙伴建立联系。它将与普罗克特·甘布尔、布鲁塞尔和第六个欧盟框架计划的警报项目的努力产生协同作用。路径预测系统（PPS）基于250条代谢规则。PPS规则基于与人类专家、已知途径和实验室实验验证的比较，模拟了化合物几乎所有可能的生化途径。为了更真实地描述环境的命运，第一个要模拟的标准条件将是有氧环境，适度的湿度和盐度，没有竞争性的化学毒物，温度为25 ℃。将采用一种综合办法来确定生物转化规则的优先次序。首先，人类专家会在1-5级的范围内对规则进行评级：1）很可能; 2）可能; 3）中性; 4）不太可能; 5）极不可能。这将提供反应的顺序排序。专业知识将增加使用热力学技术。初步数据表明，热力学可以最低限度地选择对无用的代谢循环和有害的异构化反应。还将研究快速增长的微生物基因组数据，以提高生物转化的优先级。该项目将定量评估参与当前PPS规则涵盖的反应的基因和酶的流行率。将进一步评估这些基因和酶是否存在于特定环境的细菌中，如需氧菌，厌氧菌和嗜盐菌。该系统将利用明尼苏达大学超级计算研究所的资源开发，并在欧洲公开镜像，供ALARM计划的参与者和其他客户使用。决策软件的规则和使用将在网上免费提供给所有用户。该系统还将通过合作学者网络在全国各地的大学课堂上进行教育。这将用于教授学生化学，代谢生物化学，基因组学和环境科学，并帮助我们验证路径优先预测系统。所有结果将通过我们的网站提供给科学界。