PuSH - Publication Server of Helmholtz Zentrum München

Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data.

Bioinformatics 31, i89-i96 (2015)
Publ. Version/Full Text DOI
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
MOTIVATION: High-dimensional single-cell snapshot data are becoming widespread in the systems biology community, as a mean to understand biological processes at the cellular level. However, as temporal information is lost with such data, mathematical models have been limited to capture only static features of the underlying cellular mechanisms. RESULTS: Here, we present a modular framework which allows to recover the temporal behaviour from single-cell snapshot data and reverse engineer the dynamics of gene expression. The framework combines a dimensionality reduction method with a cell time-ordering algorithm to generate pseudo time-series observations. These are in turn used to learn transcriptional ODE models and do model selection on structural network features. We apply it on synthetic data and then on real hematopoietic stem cells data, to reconstruct gene expression dynamics during differentiation pathways and infer the structure of a key gene regulatory network. AVAILABILITY AND IMPLEMENTATION: C++ and Matlab code available at https://www.helmholtz-muenchen.de/fileadmin/ICB/software/inferenceSnapshot.zip. CONTACT: fabian.theis@helmholtz-muenchen.deSupplementary information: Supplementary data are available at Bioinformatics online.
Additional Metrics?
Icb_Latent Causes Icb_rene
Edit extra informations Login
Publication type Article: Journal article
Document type Scientific Article
Keywords Escherichia-coli; Expression; Network; Stem; Leukemia; Biology; Systems; Models
ISSN (print) / ISBN 1367-4803
Journal Bioinformatics
Quellenangaben Volume: 31, Issue: 12, Pages: i89-i96 Article Number: , Supplement: ,
Publisher Oxford University Press
Publishing Place Oxford
Reviewing status