Pargett Michael, Rundell Ann E, Buzzard Gregery T, Umulis David M
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, United States of America.
Department of Mathematics, Purdue University, West Lafayette, Indiana, United States of America.
PLoS Comput Biol. 2014 Mar 13;10(3):e1003498. doi: 10.1371/journal.pcbi.1003498. eCollection 2014 Mar.
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.
发育生物学中的发现通常由直觉驱动，这种直觉依赖于多种类型数据的整合，如荧光图像、表型和生化分析结果。随着网络变得越来越大且复杂，数学建模有助于阐明其中起作用的生物学机制。然而，由于与定量模型调整技术不兼容，可用数据常常未得到充分利用。通过荧光免疫组织化学在果蝇卵巢管中探索的干细胞调节机制就是这种情况。为了在这种及类似情况下更好地将生物学数据与建模相结合，我们开发了一种通用的参数估计过程，以用定性数据对模型进行定量优化。该过程采用了社会和行为科学中最优尺度法的改进版本，以及多目标优化来评估拟合不同数据集（如野生型与突变体）之间的权衡。仅使用卵巢管中已发表的成像数据，我们首先通过考虑相同调节因子的替代连接来评估对一个已发表的细胞内调节网络的支持。简单地根据野生型数据筛选网络就发现了数百种可行的替代方案。其中，通过包括突变体数据和动态约束的多目标分析，找到了五个简约变体并进行了比较。有了这些数据，当前模型比替代方案得到了更多支持，但对一个生化观察到的反馈元件的支持较弱（即这些数据不能很好地测量反馈效应）。在比较新的假设模型时，现有数据也无法区分。为了开始解决数据中的局限性，我们进行了基于模型的实验设计，并为实验提供了建议，以完善模型参数并区分日益复杂的假设。
