From complex datasets to predictive models of embryonic development

Sayantan Dutta, Aleena L. Patel, Shannon E. Keenan, Stanislav Y. Shvartsman

Research output: Contribution to journalArticlepeer-review

Abstract

Modern studies of embryogenesis are increasingly quantitative, powered by rapid advances in imaging, sequencing and genome manipulation technologies. Deriving mechanistic insights from the complex datasets generated by these new tools requires systematic approaches for data-driven analysis of the underlying developmental processes. Here, we use data from our work on signal-dependent gene repression in the Drosophila embryo to illustrate how computational models can compactly summarize quantitative results of live imaging, chromatin immunoprecipitation and optogenetic perturbation experiments. The presented computational approach is ideally suited for integrating rapidly accumulating quantitative data and for guiding future studies of embryogenesis.

Original languageEnglish (US)
Pages (from-to)516-520
Number of pages5
JournalNature Computational Science
Volume1
Issue number8
DOIs
StatePublished - Aug 2021

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science Applications
  • Computer Networks and Communications

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