Abstract
In systems biology, qualitative data are often generated, but rarely used to parameterize models. We demonstrate an approach in which qualitative and quantitative data can be combined for parameter identification. In this approach, qualitative data are converted into inequality constraints imposed on the outputs of the model. These inequalities are used along with quantitative data points to construct a single scalar objective function that accounts for both datasets. To illustrate the approach, we estimate parameters for a simple model describing Raf activation. We then apply the technique to a more elaborate model characterizing cell cycle regulation in yeast. We incorporate both quantitative time courses (561 data points) and qualitative phenotypes of 119 mutant yeast strains (1647 inequalities) to perform automated identification of 153 model parameters. We quantify parameter uncertainty using a profile likelihood approach. Our results indicate the value of combining qualitative and quantitative data to parameterize systems biology models.
Highlights
In systems biology, qualitative data are often generated, but rarely used to parameterize models
Systems biology models, such as those found in BioModels Database[1], typically have outputs in the form of time courses
We have demonstrated how qualitative and quantitative data may be used together for parameter identification in biological modeling
Summary
Qualitative data are often generated, but rarely used to parameterize models. We demonstrate an approach in which qualitative and quantitative data can be combined for parameter identification In this approach, qualitative data are converted into inequality constraints imposed on the outputs of the model. We apply the technique to a more elaborate model characterizing cell cycle regulation in yeast We incorporate both quantitative time courses (561 data points) and qualitative phenotypes of 119 mutant yeast strains (1647 inequalities) to perform automated identification of 153 model parameters. Our results indicate the value of combining qualitative and quantitative data to parameterize systems biology models Systems biology models, such as those found in BioModels Database[1], typically have outputs in the form of time courses. Parameters in yeast cell cycle models have been estimated by hand-tuning[3] and later refined by automated tuning[7] to maximize the number of mutant strains that the models describe correctly
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