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、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，高通量测序被用于绘制微生物物种的基因组。然而，微生物的DNA序列并不能提供对其功能的完整理解。为了弥合基因型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学工具。代谢通量的分析可以识别生物合成所需产物的瓶颈途径，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，为生物学家提供一个强大的网络基础设施，帮助他们有效地分析大量的表型数据。此外，该平台还包括一个开源数据库，用于存储和传播各种微生物代谢的通量分析数据。该数据库可以帮助新微生物的代谢通量分析，使系统生物学社区能够从“大数据”技术的快速发展中受益。最终，这个平台可以成为未来开发高通量方法来分析不同生物系统的跳板。该项目的更广泛影响不仅包括为高中生（特别是少数民族和代表性不足的群体）开展教育工作，以促进他们对STEM领域的研究和兴趣，还包括开发一个维基风格的网站和讨论论坛，用于通量分析技术和应用。目前的系统生物学研究（例如转录组学）依赖于模式生物进行基因组注释，并且在揭示新的代谢途径方面能力有限。此外，转录后和翻译后调节阻碍了可以从基因组方法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，对揭示微生物的代谢机制和代谢调控具有重要的意义。该项目有三个任务来开发代谢通量分析的新功能。第一个任务将是建立一个全面的碳命运图13 C辅助途径识别，并提供有效的计算算法13 C代谢通量分析。因此，该通量分析平台可以使用来自13 C-示踪实验的标记信息精确地量化途径活动。第二项任务是建立一个已发表的微生物通量组学结果数据库，然后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微生物代谢。第三项任务是创建新的方法，将基因组规模的通量平衡分析与13 C标记和数据挖掘结果相结合，以精确表征微生物代谢。一旦新的模型平台开发完成，将使用案例研究对其进行测试。基于希瓦氏菌代谢通量分析的实验数据，将验证和改进模型的适用性。最终，这个新平台可以被系统生物学协会广泛使用，为不同的微生物物种提供新的见解。该项目的网站链接是：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/