Open Close
Reference
Citation
Pargett, M., Rundell, A.E., Buzzard, G.T., Umulis, D.M. (2014). Model-based analysis for qualitative data: an application in Drosophila germline stem cell regulation.  PLoS Comput. Biol. 10(3): e1003498.
FlyBase ID
FBrf0224398
Publication Type
Research paper
Abstract

Discovery in developmental biology is often driven by intuition that relies on the integration of multiple types of data such as fluorescent images, phenotypes, and the outcomes of biochemical assays. Mathematical modeling helps elucidate the biological mechanisms at play as the networks become increasingly large and complex. However, the available data is frequently under-utilized due to incompatibility with quantitative model tuning techniques. This is the case for stem cell regulation mechanisms explored in the Drosophila germarium through fluorescent immunohistochemistry. To enable better integration of biological data with modeling in this and similar situations, we have developed a general parameter estimation process to quantitatively optimize models with qualitative data. The process employs a modified version of the Optimal Scaling method from social and behavioral sciences, and multi-objective optimization to evaluate the trade-off between fitting different datasets (e.g. wild type vs. mutant). Using only published imaging data in the germarium, we first evaluated support for a published intracellular regulatory network by considering alternative connections of the same regulatory players. Simply screening networks against wild type data identified hundreds of feasible alternatives. Of these, five parsimonious variants were found and compared by multi-objective analysis including mutant data and dynamic constraints. With these data, the current model is supported over the alternatives, but support for a biochemically observed feedback element is weak (i.e. these data do not measure the feedback effect well). When also comparing new hypothetical models, the available data do not discriminate. To begin addressing the limitations in data, we performed a model-based experiment design and provide recommendations for experiments to refine model parameters and discriminate increasingly complex hypotheses.

PubMed ID
PubMed Central ID
PMC3952817 (PMC) (EuropePMC)
Associated Information
Comments
Associated Files
Other Information
Secondary IDs
    Language of Publication
    English
    Additional Languages of Abstract
    Parent Publication
    Publication Type
    Journal
    Abbreviation
    PLoS Comput. Biol.
    Title
    PLoS Computational Biology
    Publication Year
    2005-
    ISBN/ISSN
    1553-7358 1553-734X
    Data From Reference
    Genes (7)