Development and application of an in silico metabolic model of Geobacillus thermoglucosidasius

CASE合作公司TMO Renewables Ltd基于对热糖地杆菌发酵途径的工程设计，开发了一种能够利用木质纤维底物中提取的糖进行乙醇发酵的工艺。作为他们合同工作的一部分，他们已经获得了这种有机体的基因组序列，这一序列几乎已经完成。在初始阶段，基因组序列的注释通常是基于开放阅读框架(ORF)搜索和生物信息学同源搜索而自动完成的。这意味着初始注释通常包含错误和未分类的ORF。在这个项目中，学生将使用初始基因组序列作为起点，开始建立这种有机体的基因组规模的代谢模型。最终目标是在Palsson的通量平衡分析方法(Palsson BO(2006)系统生物学)的基础上建立这种有机体代谢能力的计算机模型。重构网络的性质。剑桥大学出版社，纽约，美国)。然而，由于地质杆菌在生化和生理水平上没有得到广泛的描述，这将需要大量的实验验证来确认基因分配和填补缺失的代谢环节。一旦构建，电子代谢模型就可以具有预测能力。最初，这些可用于验证模型的质量，该模型总是包括许多假设/估计。然而，从长远来看，这些可能被用来预测特定目标的代谢工程的最佳路线，这是该公司未来的目标之一。然而，在此期间，模型的逻辑迭代实验和电子构建应该会对这一日益重要的嗜热菌群体的生理和生化产生新的见解。该方案的实验和建模相结合的方面将为具有生物学背景的研究生形成一个极好的培训方案，使他们接触到更容易获得的系统生物学途径之一。

TMO Renewables Ltd, the CASE collaborating company, has developed an ethanol fermentation process capable of using sugars derived from lignocellulosic substrates, based on engineering the fermentation pathways of Geobacillus thermoglucosidasius. As part of the work they have contracted to obtain the genome sequence of this organism, which is almost complete. In the initial stages, the annotation of a genome sequence is usually done automatically, based on the search for open reading frames (orfs) and bio-informatic homology searches. This means that initial annotations often contain errors and unclassified orfs. In this project the student will start to build a genomic scale metabolic model of this organism, using the initial genome sequence as a starting point. The ultimate goal is to build an in silico model of the metabolic capabilities of this organism based on Palsson's flux balance analysis approach (Palsson BO (2006) Systems Biology. Properties of Reconstructed Networks. Cambridge University Press, New York, USA). However, because Geobacillus spp are not extensively described at a biochemical and physiological level, this will require a considerable amount of experimental validation to eg confirm the gene assignments and fill in missing metabolic links. Once constructed, an in silico metabolic model can have predictive capabilities. Initially, these can be used to authenticate the quality of the model, which invariably includes an number of assumptions/estimates. However, in the long term, these may be used to predict the optimal route for metabolic engineering for a defined objective, which is one of the future aims of the company. In the interim, however, the logical iterative experimental and in silico construction of the model should generate new insights into the physiology and biochemistry of this increasingly important group of thermophiles. The combined experimental and modelling aspects of this programme will form an excellent training programme for a postgraduate student with a biological background, exposing them to one of the more accessible avenues of systems biology.