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From complex datasets to predictive models of embryonic development

A preprint version of the article is available at bioRxiv.


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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Fig. 1: Integration of heterogeneous data and computational techniques.
Fig. 2: Estimation of the parameters of model and quantitative predictions.

Data availability

Source data are provided with this paper. Raw images used to generate data in Fig. 1c are also available in ref. 28.

Code availability

The code for the MCMC simulation, the processed experimental data used to estimate the parameters and code for generating ensemble predictions are available in ref. 28. The code is also available as Supplementary Software with this paper.


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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.

Author information




S.E.K. and A.L.P. carried out all the experiments. S.E.K., S.D. and A.L.P. analyzed the data. S.Y.S., S.D. and S.E.K. designed the model. S.D. implemented the model and performed the simulations. S.D., A.L.P., S.E.K. and S.Y.S. wrote the manuscript

Corresponding author

Correspondence to Stanislav Y. Shvartsman.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review informationNature Computational Science thanks the anonymous reviewers for their contribution to the peer review of this work. Handling editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8 and Table 1.

Reporting Summary

Supplementary Software

A set of codes that demonstrate how we learn parameters from the existing dataset from multi-scale experiments in a gene regulatory network in the terminal patterning system of the Drosophila (fruit fly) embryo and make quantitative predictions as described in the paper.

Source data

Source Data Fig. 1

For the simulation results, data from all 1,000 experiments are given. Each row represents the time point mentioned in the left-most column, and all other columns represent the simulation output from each parameter set. For the experiment, the fluorescence intensity of nuclear Cic for all the nuclei is given. These are background-subtracted, averaged and normalized as described in the paper to obtain the data in Fig. 1c. In each row, the first entry is the time point followed by the fluorescence intensity of all the nuclei, followed by zeroes when the number of nuclei is less than the number of columns.

Source Data Fig. 2

For Fig. 2a–d, all the parameters obtained from MCMC simulation are given. The columns represent individual parameters, and each row represents an individual parameter. In Fig. 2e–g, ‘full simulation’ refers to the same data as for Fig. 1e (top). Data for all the perturbed simulations are given. Each row represents the time point mentioned in the left-most column, and all other columns represent the simulation output from each parameter set.

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Dutta, S., Patel, A.L., Keenan, S.E. et al. From complex datasets to predictive models of embryonic development. Nat Comput Sci 1, 516–520 (2021).

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