Endothelial cells (ECs) create a vascular network with a tubular structure in response to growth factors diffused into the gel and interactions with the surrounding environment. Individual cells migrate in response to all of these cues, leading to network pattern formation. This paper presents a dynamic model predicting EC sprout growth that is tuned to time-lapse experimental cell migration data obtained from microfluidic 3D culture. Simple cell migration equations with just a few parameters are formulated and a Maximum Likelihood estimator is used for estimating model parameters from experimental data. The tuned model is used to evaluate the influence of different sprout elongation rates on cell density in the sprout stalk. This quantitative modeling approach will lead to input shaping and feedback control to optimize sprouting metrics such as stalk cell density.

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