Synthesizing developmental trajectories.

TitleSynthesizing developmental trajectories.
Publication TypeJournal Article
Year of Publication2017
AuthorsVilloutreix, P, Andén, J, Lim, B, Lu, H, Kevrekidis, IG, Singer, A, Shvartsman, SY
JournalPLoS Comput Biol
Date Published2017 Sep
KeywordsAnimals, Body Patterning, Computational Biology, Drosophila, Image Processing, Computer-Assisted, Microscopy, Confocal, Models, Biological, Supervised Machine Learning

<p>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.</p>

Alternate JournalPLoS Comput Biol
PubMed ID28922353
PubMed Central IDPMC5619836
Grant ListR01 GM088333 / GM / NIGMS NIH HHS / United States
R01 GM107103 / GM / NIGMS NIH HHS / United States