Pluripotent embryonic stem cells (ESCs) can differentiate into all somatic cell types, making them a useful platform for studying a variety of cellular phenomenon. Furthermore, ESCs can be induced to form aggregates which recapitulate some aspects dynamics of development and morphogenesis. However, many different factors such as gradients of soluble morphogens, direct cell-to-cell signaling, and cell-matrix interactions have all been implicated in directing ESC differentiation. Though the effects of individual factors have been investigated independently, assaying combinatorial effects has proven inherently difficult due to the spatial and temporal dynamics associated with such cues. Dynamic computational models of ESC differentiation can provide powerful insight into how different cues function in combination, both spatially and temporally. The objective of this study was to examine how spatial patterns of differentiation in ESC aggregates arise as a function of the microenvironment. The central hypothesis was that heterogeneity associated with soluble morphogens and cell-cell signaling lead to complex spatial patterns in ESC aggregates. This work addressed these questions via a multiscale model to computationally assess pattern formation and a novel portable pattern recognition approach to compare biological and computational spatial patterns.
