期刊
INTEGRATIVE BIOLOGY
卷 7, 期 9, 页码 987-997出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/c5ib00024f
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资金
- AHA grant [13PRE16580001, 12BGIA12060154]
- Jefferson Trust Big Data Grant
- Cardiovascular Research Center Training Grant [NIH T32-HL007284]
- NSF grant [1235244]
- NIH [R01 HL43174, R00HL093219, F32HL95359, K99HL105779]
- Sloan Research Fellowship
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1235244] Funding Source: National Science Foundation
Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods.
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