Multicellular simulation predicts microvascular patterning and in silico tissue assembly

Remodeling of microvascular networks in mammals is critical for physiological adaptations and therapeutic revascularization. Cellular behaviors such as proliferation, differentiation, and migration are coordinated in these remodeling events via combinations of biochemical and biomechanical signals. We developed a cellular automata (CA) computational simulation that integrates epigenetic stimuli, molecular signals, and cellular behaviors to predict microvascular network patterning events. Over 50 rules obtained from published experimental data govern independent behaviors (including proliferation, differentiation, and migration) of thousands of interacting cells and diffusible growth factors in their tissue environment. From initial network patterns of in vivo blood vessel networks, the model predicts emergent patterning responses to two stimuli: 1) network-wide changes in hemodynamic mechanical stresses, and 2) exogenous focal delivery of an angiogenic growth factor. The CA model predicts comparable increases in vascular density (370+/-29 mm/mm3) 14 days after treatment with exogenous growth factor to that in vivo (480+/-41 mm/mm3) and approximately a twofold increase in contractile vessel lengths 5-10 days after 10% increase in circumferential wall strain, consistent with in vivo results. The CA simulation was thus able to identify a functional patterning module capable of quantitatively predicting vessel network remodeling in response to two important epigenetic stimuli.