TY - JOUR
T1 - From complex datasets to predictive models of embryonic development
AU - Dutta, Sayantan
AU - Patel, Aleena L.
AU - Keenan, Shannon E.
AU - Shvartsman, Stanislav Y.
N1 - Funding Information:
We thank M. Frenklach, E. Wieschaus, T. Schüpbach and all members of the Shvartsman laboratory for helpful discussions. We thank L. Reading-Ikkanda for graphic design of the figures. We thank G. Laevsky and the Molecular Biology Core Confocal Microscopy Facility for imaging support. We thank L. Yang for the enzyme-linked immunosorbent assay experiment. We thank N. J.-V. Djabrayan and G. Jimenez for the synthetic reporter CZC. We thank the Lewis Sigler Institute of Integrative Genomics for computational resources. The research was supported by the NIH (HD085870 grant). The funding agencies had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Funding Information:
We thank M. Frenklach, E. Wieschaus, T. Sch?pbach and all members of the Shvartsman laboratory for helpful discussions. We thank L. Reading-Ikkanda for graphic design of the figures. We thank G. Laevsky and the Molecular Biology Core Confocal Microscopy Facility for imaging support. We thank L. Yang for the enzyme-linked immunosorbent assay experiment. We thank N. J.-V. Djabrayan and G. Jimenez for the synthetic reporter CZC. We thank the Lewis Sigler Institute of Integrative Genomics for computational resources. The research was supported by the NIH (HD085870 grant). The funding agencies had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
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U2 - 10.1038/s43588-021-00110-2
DO - 10.1038/s43588-021-00110-2
M3 - Article
AN - SCOPUS:85125249567
SN - 2662-8457
VL - 1
SP - 516
EP - 520
JO - Nature Computational Science
JF - Nature Computational Science
IS - 8
ER -