文献类型：期刊文献

中文题名：基于Matlab对解脂耶氏酵母基因组规模代谢网络模型仿真结果的可视化分析

英文题名：Matlab-based visualization analysis of genome-scale metabolic network model simulation of Yarrowia lipolytica

作者：简星星[1];高琪[1];花强[1,2]

机构：[1]生物反应器工程国家重点实验室华东理工大学生物工程学院,上海200237;[2]上海生物制造技术协同创新中心,上海200237

年份：2015

卷号：42

期号：9

起止页码：1752

中文期刊名：微生物学通报

外文期刊名：Microbiology China

收录：CSTPCD;;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：国家973计划项目(No.2012CB721101);国家自然科学基金项目(No.31200025)

语种：中文

中文关键词：基因组规模代谢网络模型;解脂耶氏酵母;可视化;Cell;Designer;RAVEN;toolbox

外文关键词：Genome-scale metabolic network model, Yarrowia lipolytica, Visualization, CellDesigner, RAVEN toolbox

摘要：【目的】近十年来,基因组代谢网络模型迅速发展。通过构建基因组代谢网络模型进行计算机仿真模拟已成为研究生物体复杂的生理代谢不可或缺的工具。实现对仿真结果的可视化分析,可以直观地追踪模型中的代谢流向,从而更好地对仿真结果进行分析。【方法】在简要概述目前可视化方法的基础上,提出了一种基于Matlab实现基因组规模代谢网络模型仿真结果可视化的方法:通过Cell Designer预先绘制与模型相匹配的图,通过RAVEN toolbox中的函数于Matlab进行读图、并实现仿真结果的可视化。【结果】以解脂耶氏酵母基因组规模代谢网络模型i YL619_PCP v1.7为对象,实现并阐明其仿真结果的可视化。【结论】通过该方法可以清晰地监测模型中的流量和流向变化,提高仿真结果的分析效率。
[Objective] In recent decade, genome-scale metabolic network model （GSMM） has been flourishing rapidly. It has been an indispensable tool to investigate complex physiology of organisms through reconstruction of GSMMs and prediction cellular characteristics based on computational simulation. Visualization of simulation results can help to trace intuitively metabolic flux changes in the model and generate possible metabolic engineering strategies. [Methods] In this study, we first briefly summarized current methods for metabolic network visualization, followed by proposing a new method to realize visualization of GSMMs simulation based on Matlab. A pre-drawing of the metabolic network was performed first via CellDesigner software. Two functions of RAVEN toolbox were employed to integrate the raw map into Matlab and to realize visualization of simulation. [Results] As an example, the visualization of iYL619_PCP vl.7, a GSMM of Yarrowia lipolytica was realized. [Conclusion] According to the maps of visualization, we could monitor clearly flux changes in pathways under different environmental and genetic conditions to analyze efficiently simulation results.