Hypothesis testing via integrated computer modeling and digital fluorescence microscopy.
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.
Gardner
Melissa K
MK
Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Odde
David J
DJ
Bloom
Kerry
K
D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D049452:Green Fluorescent Proteins / Q000235:genetics / Q000502:physiology; D008856:Microscopy, Fluorescence / Q000379:methods*; D008954:Models, Biological; D012440:Saccharomyces / Q000166:cytology / Q000235:genetics / Q000502:physiology; D013269:Stochastic Processes
D049452:Green Fluorescent Proteins