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

Nimit Dhulekar, Lauren M. Bange, Abiurami Baskaran, D. Yuan, Basak Oztan, B. Yener, Shayoni Ray, M. Larsen
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引用次数: 5

Abstract

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.
一种预测唾液腺分支形态发生的动态图计算模型
本文介绍了一种基于生物动机的动态图生长模型,用于描述和预测小鼠下颌骨腺(SMG)分支形态发生过程中从胚胎期E12后3小时到胚胎期E12后8小时(可视为E12.5)的裂缝形成阶段。分支形态发生是许多哺乳动物的外分泌腺和内分泌腺经历重要形态转变的过程,从初生芽到成年器官。尽管许多研究已经探讨了驱动分支形态发生的细胞和分子机制,但尚不清楚建立器官结构所固有的形状变化是如何产生的。利用从SMG器官培养的连续图像中提取的形态学特征,我们能够开发一个动态的基于图形的预测模型,该模型能够模拟裂缝形成的过程并预测最终状态。此外,我们将我们的模型与最先进的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发育阶段的裂缝增长方面具有相当的准确性,并且更快地收敛到目标SMG形态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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