Model-based analysis for qualitative data: an application in Drosophila germline stem cell regulation.
Model-based analysis for qualitative data: an application in Drosophila germline stem cell regulation.
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DOI：
10.1371/journal.pcbi.1003498
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发表时间：
2014-03
影响因子：
4.3
通讯作者：
Umulis DM
中科院分区：
文献类型：
--
作者：
Pargett M;Rundell AE;Buzzard GT;Umulis DM
发育生物学的发现通常是由直觉驱动的，这种直觉依赖于多种类型的数据的集成，如荧光图像、表型和生化分析的结果。数学建模有助于阐明随着网络变得越来越大和复杂而起作用的生物机制。然而，由于与定量模型调整技术不兼容，可用数据经常得不到充分利用。这就是通过荧光免疫组织化学在果蝇生殖室中探索干细胞调节机制的情况。为了在这种和类似的情况下更好地将生物数据与建模相结合，我们开发了一个通用的参数估计过程，以定量地优化具有定性数据的模型。该过程使用了社会科学和行为科学的最优比例方法的修改版本，以及多目标优化来评估适应不同数据集(例如野生型和突变型)之间的权衡。我们只使用细菌馆中公布的成像数据，首先通过考虑相同调节参与者的替代连接来评估对公布的细胞内调节网络的支持。简单地根据野生类型数据筛选网络就确定了数百个可行的替代方案。通过多目标分析，包括突变数据和动态约束条件，发现了5个简约型变异。有了这些数据，当前的模型比备选方案更受支持，但对生物化学观察到的反馈元素的支持很弱(即这些数据没有很好地衡量反馈效果)。当同样比较新的假设模型时，现有的数据不会有区别。为了开始解决数据中的限制，我们进行了基于模型的实验设计，并为实验提供建议，以改进模型参数并区分日益复杂的假设。我们开发了一种使用定性数据定量拟合数学模型的过程，并将其应用于研究干细胞如何在果蝇卵巢中调节。我们收集的现有出版数据是来自基因实验的蛋白质和mRNA表达的荧光图像。尽管缺乏定量数据，但新过程提供了定量模型分析技术，以可靠地比较不同的模型并指导未来的实验。我们发现，目前的共识监管模型得到了支持，但数据确实不足以解决更复杂的假设。利用定量拟合的模型，我们评估了假设实验，并估计了哪些未来的测量应该最好地改进或测试模型。我们开发的模型拟合过程适用于定性数据常见的许多生物学研究，并可以通过更有效的实验来加快进度。
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. We developed a process to quantitatively fit mathematical models using qualitative data, and applied it in the study of how stem cells are regulated in the fruit fly ovary. The available published data we collected are fluorescent images of protein and mRNA expression from genetic experiments. Despite lacking quantitative data, the new process makes available quantitative model analysis techniques to reliably compare different models and guide future experiments. We found that the current consensus regulatory model is supported, but that the data are indeed insufficient to address more complex hypotheses. With the quantitatively fit models, we evaluated hypothetical experiments and estimated which future measurements should best refine or test models. The model fitting process we have developed is applicable to many biological studies where qualitative data are common, and can accelerate progress through more efficient experimentation.