  CRISPR technology has enabled large-scale cell lineage tracing for complex
multicellular organisms by mutating synthetic genomic barcodes during
organismal development. However, these sophisticated biological tools currently
use ad-hoc and outmoded computational methods to reconstruct the cell lineage
tree from the mutated barcodes. Because these methods are agnostic to the
biological mechanism, they are unable to take full advantage of the data's
structure. We propose a statistical model for the mutation process and develop
a procedure to estimate the tree topology, branch lengths, and mutation
parameters by iteratively applying penalized maximum likelihood estimation. In
contrast to existing techniques, our method estimates time along each branch,
rather than number of mutation events, thus providing a detailed account of
tissue-type differentiation. Via simulations, we demonstrate that our method is
substantially more accurate than existing approaches. Our reconstructed trees
also better recapitulate known aspects of zebrafish development and reproduce
similar results across fish replicates.
