Hypothesis testing via integrated computer modeling and digital fluorescence microscopy

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18 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)232-237
Number of pages6
Issue number2
StatePublished - Feb 2007

Bibliographical note

Funding Information:
This work was supported by the Whitaker Foundation, the National Science Foundation, and the National Institutes of Health.


  • Model-convolution
  • Modeling
  • Simulation
  • Stochastic
  • Yeast


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