Synthesizing developmental trajectories

Paul Villoutreix, Joakim Andén, Bomyi Lim, Hang Lu, Ioannis G. Kevrekidis, Amit Singer, Stanislav Y. Shvartsman

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Dynamical processes in biology are studied using an ever-increasing number of techniques, each of which brings out unique features of the system. One of the current challenges is to develop systematic approaches for fusing heterogeneous datasets into an integrated view of multivariable dynamics. We demonstrate that heterogeneous data fusion can be successfully implemented within a semi-supervised learning framework that exploits the intrinsic geometry of high-dimensional datasets. We illustrate our approach using a dataset from studies of pattern formation in Drosophila. The result is a continuous trajectory that reveals the joint dynamics of gene expression, subcellular protein localization, protein phosphorylation, and tissue morphogenesis. Our approach can be readily adapted to other imaging modalities and forms a starting point for further steps of data analytics and modeling of biological dynamics.

Original languageEnglish (US)
Article numbere1005742
JournalPLoS computational biology
Volume13
Issue number9
DOIs
StatePublished - Sep 2017

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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    Villoutreix, P., Andén, J., Lim, B., Lu, H., Kevrekidis, I. G., Singer, A., & Shvartsman, S. Y. (2017). Synthesizing developmental trajectories. PLoS computational biology, 13(9), [e1005742]. https://doi.org/10.1371/journal.pcbi.1005742