首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Prediction of Growth Factor-Dependent Cleft Formation During Branching Morphogenesis Using A Dynamic Graph-Based Growth Model
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Prediction of Growth Factor-Dependent Cleft Formation During Branching Morphogenesis Using A Dynamic Graph-Based Growth Model

机译:基于动态图的生长模型预测分支形态发生过程中依赖生长因子的裂隙形成

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This study considers the problem of describing and predicting cleft formation during the early stages of branching morphogenesis in mouse submandibular salivary glands (SMG) under the influence of varied concentrations of epidermal growth factors (EGF). Given a time-lapse video of a growing SMG, first we build a descriptive model that captures the underlying biological process and quantifies the ground truth. Tissue-scale (global) and morphological features related to regions of interest (local features) are used to characterize the biological ground truth. Second, we devise a predictive growth model that simulates EGF-modulated branching morphogenesis using a dynamic graph algorithm, which is driven by biological parameters such as EGF concentration, mitosis rate, and cleft progression rate. Given the initial configuration of the SMG, the evolution of the dynamic graph predicts the cleft formation, while maintaining the local structural characteristics of the SMG. We determined that higher EGF concentrations cause the formation of higher number of buds and comparatively shallow cleft depths. Third, we compared the prediction accuracy of our model to the Glazier-Graner-Hogeweg (GGH) model, an on-lattice Monte-Carlo simulation model, under a specific energy function parameter set that allows new rounds of de novo cleft formation. The results demonstrate that the dynamic graph model yields comparable simulations of gland growth to that of the GGH model with a significantly lower computational complexity. Fourth, we enhanced this model to predict the SMG morphology for an EGF concentration without the assistance of a ground truth time-lapse biological video data; this is a substantial benefit of our model over other similar models that are guided and terminated by information regarding the final SMG morphology. Hence, our model is suitable for testing the impact of different biological parameters involved with the process of branching morphogenesis
机译:本研究考虑了在表皮生长因子(EGF)浓度变化的影响下,预测和预测小鼠下颌唾液腺(SMG)分支形态发生早期裂隙形成的问题。给出了一个不断增长的SMG的延时录像,首先,我们建立一个描述性模型,该模型捕获潜在的生物过程并量化基本事实。与感兴趣区域有关的组织尺度(整体)和形态特征(局部特征)用于表征生物学基础真相。其次,我们设计了一种预测性生长模型,该模型使用动态图算法模拟EGF调节的分支形态发生,该算法由诸如EGF浓度,有丝分裂率和c裂进展率等生物学参数驱动。给定SMG的初始配置,动态图的演变会预测裂缝形成,同时保持SMG的局部结构特征。我们确定较高的EGF浓度会导致形成较高数量的芽和相对浅的裂口深度。第三,我们将模型的预测准确性与Glazier-Graner-Hogeweg(GGH)模型进行了比较,后者是在蒙特卡洛模拟的格点上的模型,在一个特定的能量函数参数集下,可以进行新一轮的新生裂缝形成。结果表明,动态图模型可产生与GGH模型相当的腺体生长模拟,且计算复杂度低得多。第四,我们增强了该模型以预测EGF浓度的SMG形态,而无需借助地面真实延时生物视频数据。这是我们模型相对于其他类似模型(从有关最终SMG形态的信息指导和终止)获得的实质性好处。因此,我们的模型适用于测试与分支形态发生过程有关的不同生物学参数的影响

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