Collaborative Research: In Silico Analysis of the Escherichia coli Metabolic Genotype and the Construction of Selected Isogenic Strains

该项目的目标是开发可应用于基因组序列信息的分析工具，以预测生物体的完整表型。 这项研究是加州大学圣地亚哥分校（UCSD）的Palsson博士和哈佛大学（HU）的Church博士共同努力的结果。 该研究将涉及算法开发和表型的数学表示（计算机分析）以及实验确定的基因表达和生长模式之间的动态相互作用。 计算机模拟工作将在UCSD进行，实验工作将在HU进行。 概述的三个具体目标是：（1）开发可以使用基于生物信息学数据的算法来识别基因及其功能，预测细胞可以执行的代谢生物化学，并基于其代谢来表示给定菌株的生理行为，（2）开发将预测改变基因型对生长、代谢通量和途径利用的影响的算法，预测全基因组基因表达模式随生长条件变化的变化; coli K-12中进行直接测试，通过计算机分析进行预测。这些方法将涉及使用DNA芯片技术来监测在明确定义的环境条件下野生型细胞中基因表达的全基因组变化，以及严格构建等基因菌株，其代谢活性可以通过实验确定，并与计算机分析产生的预测进行比较。该奖项是代谢工程机构间活动（NSF 98-49）的一部分。

The objective of this project is to develop analytical tools that could be applied to genomic sequence information in order to predict the complete phenotype of an organism. This research is a combined effort between Dr. Palsson at the University of California, San Diego (UCSD), NSF award 9814092, and Dr. Church at Harvard University (HU). The research will involve a dynamic interaction between algorithm development and mathematical representations of phenotypes (in silico analysis) and experimentally determined patterns of gene expression and growth. The in silico work will be performed at UCSD and the experimental work will be performed at HU. The three specific goals outlined are: (1) to develop algorithms that can use bioinformatic data based to identify genes and their functions, predict the metabolic biochemistry a cell can perform, and represent the physiological behavior of a given strain bases on its metabolism, (2) to develop algorithms that will predict the impact of altering the genotype on growth, metabolic flux and pathway utilization, and predict changes in genome-wide patterns of gene expression as a function of changes in growth conditions, and (3) to develop experimental approaches using E. coli K-12 to directly test the predictions made by in silico analysis. The approaches will involve the use of DNA chip technology to monitor genome-wide changes in gene expression in wild type cells under well-defined environment conditions and the rigorous construction of isogenic strains whose metabolic activities can be experimentally determined and compared to the predictions generated by the in silico analysis. This award is part of the Interagency Activity in Metabolic Engineering (NSF 98-49).