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A novel dynamic graph-based computational model for predicting salivary gland branching morphogenesis

机译:一种基于动态图的新型预测唾液腺分支形态发生的计算模型

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In this paper, we introduce a biologically motivated dynamic graph-based growth model to describe and predict the stages of cleft formation during the process of branching morphogenesis in the submandibular mouse gland (SMG) from 3 hrs after embryonic day E12 to 8 hrs after embryonic day E12, which can be considered as E12.5. Branching morphogenesis is the process by which many mammalian exocrine and endocrine glands undergo significant morphological transformations, from a primary bud to an adult organ. Although many studies have investigated the cellular and molecular mechanisms driving branching morphogenesis, it is not clear how the shape changes that are inherent to establishing organ structure are produced. Using morphological features extracted from sequential images of SMG organ cultures we were able to develop a dynamic graph-based predictive model that is able to mimic the process of cleft formation and predict the final state. In addition, we compare our model to a state-of-the-art Glazier-Graner-Hogeweg (GGH) simulative tool, and demonstrate that the dynamic graph-based predictive model has comparable accuracy in modeling growth of clefts across SMG developmental stages, as well as faster convergence to the target SMG morphology.
机译:在本文中,我们介绍了一种基于生物学动机的基于动态图的生长模型,用于描述和预测下颌下腺(SMG)从胚胎形成第12天后3小时到胚胎形成后8小时的分支形态发生过程中的the裂形成阶段。 E12天,可以视为E12.5。分支形态发生是许多哺乳动物外分泌腺和内分泌腺经历从原始芽到成年器官的重要形态转变的过程。尽管许多研究已经研究了驱动分支形态发生的细胞和分子机制,但尚不清楚如何产生建立器官结构所固有的形状变化。使用从SMG器官培养的连续图像中提取的形态学特征,我们能够开发基于动态图的预测模型,该模型能够模拟mi裂的形成过程并预测最终状态。此外,我们将模型与最先进的Glazier-Graner-Hogeweg(GGH)模拟工具进行了比较,并证明了基于动态图的预测模型在模拟SMG发育阶段的裂隙生长方面具有可比的准确性,以及更快地收敛到目标SMG形态。

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