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Synthesizing developmental trajectories

Author(s): Villoutreix, Paul; Anden, Joakim; Lim, Bomyi; Lu, Hang; Kevrekidis, Yannis G; et al

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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.
Publication Date: Sep-2017
Electronic Publication Date: 18-Sep-2017
Citation: Villoutreix, Paul, Anden, Joakim, Lim, Bomyi, Lu, Hang, Kevrekidis, Ioannis G, Singer, Amit, Shvartsman, Stanislav Y. (2017). Synthesizing developmental trajectories. PLOS COMPUTATIONAL BIOLOGY, 13 (10.1371/journal.pcbi.1005742
DOI: doi:10.1371/journal.pcbi.1005742
ISSN: 1553-734X
EISSN: 1553-7358
Type of Material: Journal Article
Version: Final published version. This is an open access article.

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