Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus.

Levin, Michael.
Lobo, Daniel.
Lobikin, Maria.
2017.

Progress in regenerative medicine requires reverse-engineering cellular control networks to infer perturbations with desired systems-level outcomes. Such dynamic models allow phenotypic predictions for novel perturbations to be rapidly assessed in silico. Here, we analyzed a Xenopus model of conversion of melanocytes to a metastatic-like phenotype only previously observed in an all-or-none manner.... read more

Subjects
Regenerative medicine.
Developmental biology.
Computational biology.
Tufts University. Department of Biology.
Permanent URL
http://hdl.handle.net/10427/010692
Original publication
Lobo, D. et al. Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus. Sci. Rep. 7, 41339; doi: 10.1038/srep41339 (2017).
ID: tufts:20178
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