title: Prediction of Growth Factor-Dependent Cleft Formation During Branching Morphogenesis Using A Dynamic Graph-Based Growth Model.
abstract: 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 in silico, while reducing the requirement of in vivo experiments.
authors.forename: Nimit
authors.initials: N
authors.lastname: Dhulekar
authors.forename: Shayoni
authors.initials: S
authors.lastname: Ray
authors.forename: Daniel
authors.initials: D
authors.lastname: Yuan
authors.forename: Abhirami
authors.initials: A
authors.lastname: Baskaran
authors.forename: Basak
authors.initials: B
authors.lastname: Oztan
authors.forename: Melinda
authors.initials: M
authors.lastname: Larsen
authors.forename: Bulent
authors.initials: B
authors.lastname: Yener
mesh_terms: D000818:Animals; D005260:Female; D051379:Mice; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D009024:Morphogenesis / Q000502:physiology*; D012469:Salivary Glands / Q000254:growth & development; D049490:Systems Biology / Q000379:methods*; D000069558:Unsupervised Machine Learning