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

CASE合作公司TMO Renewables Ltd开发了一种乙醇发酵工艺，该工艺能够使用来自木质纤维素底物的糖，基于工程化热葡糖苷酸土芽孢杆菌的发酵途径。作为工作的一部分，他们已经签约获得这种生物的基因组序列，这几乎是完整的。在初始阶段，基因组序列的注释通常是自动完成的，基于对开放阅读框架（orfs）的搜索和生物信息学同源性搜索。这意味着初始注释通常包含错误和未分类的orfs。在这个项目中，学生将开始建立这种生物体的基因组规模代谢模型，使用初始基因组序列作为起点。最终目标是基于Palsson的通量平衡分析方法（Palsson BO（2006）Systems Biology.重构网络的性质。剑桥大学出版社，纽约，美国）。然而，由于土芽孢杆菌没有在生化和生理水平上得到广泛描述，这将需要大量的实验验证，例如确认基因分配和填补缺失的代谢环节。一旦构建，计算机代谢模型就可以具有预测能力。最初，这些可以用来验证模型的质量，其中总是包括一些假设/估计。然而，从长远来看，这些可以用来预测代谢工程的最佳路线，以实现既定目标，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计算机构建应该产生对这个日益重要的嗜热菌群的生理学和生物化学的新见解。该计划的实验和建模方面将为具有生物学背景的研究生提供一个优秀的培训计划，使他们接触到系统生物学更容易获得的途径之一。

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.