Tun Kyaw, Dhar Pawan K, Palumbo Maria Concetta, Giuliani Alessandro
Systems Biology Group, Bioinformatics Institute, 30 Biopolis Way, 138671, Singapore.
BMC Bioinformatics. 2006 Jan 18;7:24. doi: 10.1186/1471-2105-7-24.
In this work a simple method for the computation of relative similarities between homologous metabolic network modules is presented. The method is similar to classical sequence alignment and allows for the generation of phenotypic trees amenable to be compared with correspondent sequence based trees. The procedure can be applied to both single metabolic modules and whole metabolic network data without the need of any specific assumption.
We demonstrate both the ability of the proposed method to build reliable biological classification of a set of microorganisms and the strong correlation between the metabolic network wiring and involved enzymes sequence space.
The method represents a valuable tool for the investigation of genotype/phenotype correlations allowing for a direct comparison of different species as for their metabolic machinery. In addition the detection of enzymes whose sequence space is maximally correlated with the metabolic network space gives an indication of the most crucial (on an evolutionary viewpoint) steps of the metabolic process.
在这项工作中，提出了一种计算同源代谢网络模块之间相对相似性的简单方法。该方法类似于经典序列比对，能够生成适合与基于对应序列的树进行比较的表型树。该程序无需任何特定假设即可应用于单个代谢模块和整个代谢网络数据。
我们展示了所提出方法构建一组微生物可靠生物分类的能力，以及代谢网络布线与所涉及酶序列空间之间的强相关性。
该方法是研究基因型/表型相关性的有价值工具，可直接比较不同物种的代谢机制。此外，检测其序列空间与代谢网络空间最大程度相关的酶，可指示代谢过程中（从进化角度来看）最关键的步骤。
