ABI innovation: Integration of flux balance analyses with data mining and 13C-labeling experiments to decipher microbial metabolisms

微生物在生态学、生物地球化学循环、人类疾病、生物修复和生物能源等方面发挥着重要作用。目前，高通量测序正被用于绘制微生物物种的基因组图。然而，微生物的DNA序列并不能提供对其功能的完整了解。为了弥合基因和表型之间的知识鸿沟，代谢通量分析是研究体内酶活性的重要表型工具。对代谢通量的分析可以确定理想产物生物合成的瓶颈途径，破译未知基因的功能，发现新的酶，并揭示疾病的机制。在这个项目中，将开发一个用户友好的代谢通量分析平台，为生物学家提供强大的网络基础设施，帮助他们高效地分析大量的表观数据。此外，该平台还包括一个开源数据库，用于存储和传播关于不同微生物代谢的通量分析数据。这个数据库可以帮助分析新的微生物的代谢流量，使系统生物界能够受益于“大数据”技术的快速进步。最终，这个平台可以成为未来开发高通量分析各种生物系统的方法的跳板。该项目的更广泛影响不仅包括对高中生(特别是少数群体和代表性不足的群体)进行教育，促进他们对STEM领域的探究和兴趣，还包括开发一个维基风格的网站和通量分析技术和应用的讨论论坛。目前的系统生物学研究(例如，转录学)依赖于模型生物进行基因组注释，揭示新的代谢途径的能力有限。此外，转录后和翻译后调节阻碍了从基因组方法可以确定的可能的表型信息。因此，建立一个新的代谢通量分析平台来破译微生物的代谢和代谢规律具有重要的价值。该项目有三个任务来开发新陈代谢流量分析的新能力。第一个任务将是构建一个全面的碳宿图，用于13C辅助途径的识别，并为13C代谢通量分析提供有效的计算算法。因此，这个通量分析平台可以利用13C-示踪剂实验的标记信息准确地定量途径的活性。第二个任务将是建立一个已发表的微生物流动组结果的数据库，然后可以通过数据挖掘方法进行分析，以预测新物种中的途径联系和新的微生物代谢。第三项任务将是创造新的方法，将基因组规模的通量平衡分析与13C标记和数据挖掘结果相结合，以准确地表征微生物的代谢。一旦开发出新的模型平台，将使用案例研究对其进行测试。基于希瓦纳氏菌代谢通量分析的现有实验数据，模型的适用性将得到验证和改进。最终，这个新的平台可以被系统生物学学会广泛使用，为不同的微生物物种提供新的见解。项目网络链接为：http://tang.eece.wustl.edu/

Microorganisms play important roles in ecology, biogeochemical cycles, human diseases, bioremediation and bioenergy. Currently, high-throughput sequencing is being used to map the genomes of microbial species. However, the DNA sequence of a microbe does not provide a complete understanding of its functioning. To bridge the knowledge gap between genotype and phenotype, metabolic flux analysis is an important phenomic tool to investigate in vivo enzymatic activities. Analysis of metabolic fluxes can identify bottleneck pathways in the biosynthesis of desirable products, decipher the function of unknown genes, discover new enzymes, and reveal the mechanisms of diseases. In this project, a user-friendly metabolic flux analysis platform will be developed to provide a robust cyberinfrastructure for biologists, helping them analyze large amounts of phenomic data efficiently. In addition, this platform includes an open source database for storing and disseminating flux analysis data on diverse microbial metabolisms. This database can assist metabolic flux analyses of new microorganisms, enabling the systems biology community to benefit from the fast advancement in "Big Data" technology. Ultimately, this platform can be a springboard for future development of high throughput methodologies to analyze diverse biological systems. The broader impact of this project not only includes educational efforts for high school students (especially minority and under-represented groups) to promote their inquiry and interests in STEM fields, but also development of a wiki-styled website and discussion forum for flux analysis technologies and applications. Current systems biology studies (for example, transcriptomics) rely on model organisms for genome annotation and have limited power to reveal novel metabolic pathways. In addition, post-transcriptional and post-translational regulations hinder the possible phenotypic information that can be determined from genomic approaches. Thereby, it is of great value to build a new metabolic flux analysis platform to decipher microbial metabolisms and metabolic regulations. This project has three tasks to develop novel capabilities for metabolic flux analysis. The first task will be to build a comprehensive carbon-fate map for 13C-assisted pathway identification and to provide effective computational algorithms for 13C-metabolic flux analysis. Thereby, this flux analysis platform can precisely quantify the pathway activities using the labeling information from 13C-tracer experiments. The second task will be to build a database of published microbial fluxomic results, which can then be analyzed via data mining approaches to predict pathway linkages and novel microbial metabolisms in new species. The third task will be to create new approaches to integrate genome-scale flux balance analysis with both 13C-labeling and data mining results to precisely characterize microbial metabolisms. Once the new model platform is developed, it will be tested using case studies. Based on available experimental data on Shewanella metabolic flux analyses, the model applicability will be validated and improved. Ultimately, this new platform can be widely used by the systems biology society to provide new insights into diverse microbial species. The project web link is: http://tang.eece.wustl.edu/