EAGER: A System Identification Based Metabolic Flux Analysis and its Application to Production of Advanced Biofuels

随着基因组规模代谢网络模型的可用，识别生物燃料和其他有价值的化学品过量生产的突变菌株的计算程序得到了广泛的使用。然而，大规模计算模型产生的复杂结果与生物学家可以轻松理解和直接利用的具有生物学意义的知识之间存在着显著的差距。这一差距限制了基因组规模模型对代谢工程的潜在影响。这一迫切的项目将通过开发一种新的基于系统识别的代谢通量分析工具来推动系统代谢工程的最新发展。这样的工具不仅使我们能够弥合上述差距，还有助于推断细胞调控机制。值得注意的是，尽管系统识别是一个成熟的领域，并在许多领域得到了广泛的应用，但由于缺乏系统识别工具所需的高质量数据，其在代谢工程中的应用尚属首次。通过将在电子微扰实验中专门设计的与系统识别工具相结合，复杂网络结构中包含的具有生物意义的信息将被提取并以生物学家容易解释的形式呈现。提出的方法将以大肠杆菌和酿酒酵母为模型系统进行验证，重点是生产先进的生物燃料。这一迫切的项目将是第一个将系统识别技术扩展到研究基因组规模的代谢网络的项目。提取的生物信息不仅将提供对细胞调控机制的见解，还将使利用现有的生物学知识验证代谢网络模型成为可能，这些知识通常是定性的。通过用大肠杆菌和酿酒酵母验证所提出的方法生产先进的生物燃料，将获得关于突变菌株如何调节其细胞代谢的有价值的知识，并将作为未来常规研究的基础的一部分。所提出的方法可以用于研究各种微生物以及其他活细胞(如癌细胞)的细胞代谢，只要它们的代谢网络模型可用。已发现的细胞调控机制知识将对代谢工程、先进生物燃料、应用微生物学、药物发现等领域产生重大影响。此外，该方法的基本思想可以扩展到研究其他细胞网络，如基因调控网络和信号转导网络，从电子实验中产生的海量数据中提取有价值的信息。

1248388WangWith the availability of the genome-scale metabolic network models, computational procedures that identify mutant strains for over production of biofuels and other valuable chemicals are widely in use. However, there is a significant gap between the complicated results generated from large-scale computational models and biologically meaningful knowledge that can be easily appreciated and directly utilized by biologists. Such a gap limits the potential impact of genome-scale models on metabolic engineering.This EAGER project will advance the state-of-the-art in systems metabolic engineering through the development of a new systems identification based metabolic flux analysis tool. Such a tool will not only enable us to bridge the aforementioned gap, but also help infer cellular regulatory mechanisms. It is worth noting that although system identification is a well-established field and has been widely used in many areas, its proposed application to metabolic engineering is the first due to the lack of quality data required by the system identification tools. By combining the specially designed in silico perturbation experiments with system identification tools, biologically meaningful information contained in the complex network structure will be extracted and presented in the form that is easily interpretable by biologists. The proposed approach will be validated using E. coli and S. cerevisiae as the model systems with the focus on the production of advanced biofuels.This EAGER project will be the first to extend the system identification techniques to study genome-scale metabolic networks. The extracted biological information will not only provide insights on cellular regulatory mechanism, but also enable the validation of metabolic network models using existing biological knowledge that are often qualitative in nature. By validating the proposed approach with E. coli and S. cerevisiae on advanced biofuels production, valuable knowledge on how mutant strains regulate their cellular metabolism will be obtained and will serve as part of the foundation for a future regular proposal.The proposed approach can be applied to study cellular metabolism of various microorganisms as well as other living cells, such as cancer cells, as long as their metabolic network models are available. The discovered knowledge on cellular regulation mechanisms will have significant impact on metabolic engineering, advanced biofuels, applied microbiology, drug discovery and other areas. In addition, the basic idea of the proposed approach can be extended to study other cellular networks, such as gene regulatory networks and signal transduction networks, to extract valuable information from massive amount of data generated through in silico experiments.