Hypothesis testing via integrated computer modeling and digital fluorescence microscopy

被引：19

作者：

Gardner, Melissa K.

Odde, David J.

Bloom, Kerry ^{[1
]}

机构：

[1] Univ Minnesota, Dept Biomed Engn, Minneapolis, MN 55455 USA

[2] Univ N Carolina, Dept Biol, Chapel Hill, NC 27599 USA

来源：

METHODS | 2007年 / 41卷 / 02期

基金：

美国国家卫生研究院; 美国国家科学基金会;

关键词：

modeling; yeast; stochastic; model-convolution; simulation;

D O I：

10.1016/j.ymeth.2006.08.002

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Computational modeling has the potential to add an entirely new approach to hypothesis testing in yeast cell biology. Here, we present a method for seamless integration of computational modeling with quantitative digital fluorescence microscopy. This integration is accomplished by developing computational models based on hypotheses for underlying cellular processes that may give rise to experimentally observed fluorescent protein localization patterns. Simulated fluorescence images are generated from the computational models of underlying cellular processes via a "model-convolution" process. These simulated images can then be directly compared to experimental fluorescence images in order to test the model. This method provides a framework for rigorous hypothesis testing in yeast cell biology via integrated mathematical modeling and digital fluorescence microscopy. (c) 2006 Elsevier Inc. All rights reserved.

引用

页码：232 / 237

页数：6

共 18 条

[1] Contributions of low molecule number and chromosomal positioning to stochastic gene expression [J].