Mathematical model of growth and neuronal differentiation of human induced pluripotent stem cells seeded on melt electrospun biomaterial scaffolds
Human induced pluripotent stem cells (hiPSCs) have two main properties: pluripotency and self-renewal. Physical cues presented by biomaterial scaffolds can stimulate differentiation of hiPSCs to neurons. In this work, we develop and analyze a mathematical model of aggregate growth and neural diffe...
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| Language: | English en |
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2016
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| Online Access: | http://hdl.handle.net/1828/7459 |
| Summary: | Human induced pluripotent stem cells (hiPSCs) have two main properties: pluripotency
and self-renewal. Physical cues presented by biomaterial scaffolds can stimulate
differentiation of hiPSCs to neurons. In this work, we develop and analyze a mathematical model of aggregate growth and neural differentiation on melt electrospun
biomaterial scaffolds. An ordinary differential equation model of population size of
each cell state (stem, progenitor, differentiated) was developed based on experimental
results and previous literature. Analysis and numerical simulations of the model
successfully capture many of the dynamics observed experimentally. Analysis of the
model gives optimal parameter sets, that correspond to experimental procedures,
to maximize particular populations. The model indicates that a physiologic oxygen
level (~5%) increases population sizes compared to atmospheric oxygen levels (~21%).
Model analysis also indicates that the optimal scaffold porosity for maximizing aggregate
size is approximately 63%. This model allows for the use of mathematical
analysis and numerical simulations to determine the key factors controlling cell behavior
when seeded on melt electrospun scaffolds. === Graduate |
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