Diagnostics for stochastic genome-scale modeling via model slicing and debugging

Kevin J. Tsai, Chuan Hsiung Chang

研究成果: Article同行評審

摘要

Modeling of biological behavior has evolved from simple gene expression plots represented by mathematical equations to genome-scale systems biology networks. However, due to obstacles in complexity and scalability of creating genome-scale models, several biological modelers have turned to programming or scripting languages and away from modeling fundamentals. In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis. We have developed a model diagnostic methodology inspired by program slicing and debugging and demonstrate the effectiveness of the methodology on a genome-scale metabolic network model published in the BioModels database. The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production. We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction. A software application that implements the methodology is available at http://gel.ym.edu. tw/gcs/.

原文English
文章編號e110380
期刊PLoS ONE
9
發行號11
DOIs
出版狀態Published - 11月 2014

指紋

深入研究「Diagnostics for stochastic genome-scale modeling via model slicing and debugging」主題。共同形成了獨特的指紋。

引用此