  A common metaphor for describing development is a rugged "epigenetic
landscape" where cell fates are represented as attracting valleys resulting
from a complex regulatory network. Here, we introduce a framework for
explicitly constructing epigenetic landscapes that combines genomic data with
techniques from spin-glass physics. Each cell fate is a dynamic attractor, yet
cells can change fate in response to external signals. Our model suggests that
partially reprogrammed cells are a natural consequence of high-dimensional
landscapes, and predicts that partially reprogrammed cells should be hybrids
that co-express genes from multiple cell fates. We verify this prediction by
reanalyzing existing datasets. Our model reproduces known reprogramming
protocols and identifies candidate transcription factors for reprogramming to
novel cell fates, suggesting epigenetic landscapes are a powerful paradigm for
understanding cellular identity.
