A framework for predictively linking cell-level signaling with larger scale patterning in regeneration and growth has yet to be created within the field of regenerative biology. If this could be achieved, regeneration (controlled cell growth), cancer (uncontrolled cell growth), and birth defects (mispatterning of cell growth) could be more easily understood and manipulated. This paper looks to create a key part of this preliminary framework by using level set methods and a cellular control scheme to predict macroscopic regenerative morphology. This simulation specifically looks at Xenopus laevis tail regeneration, and uses three control regimes to collectively mimic biological regeneration. The algorithm shows promise in creating an abstracted model to predict cell patterning on a macroscopic level.

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© 2016 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
2016
MIT Press
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