Big-Data-Driven Stem Cell Science and Tissue Engineering: Vision and Unique Opportunities

Antonio Del Sol; Hans J Thiesen; Jaime Imitola; Rafael E Carazo Salas

doi:10.1016/j.stem.2017.01.006

Big-Data-Driven Stem Cell Science and Tissue Engineering: Vision and Unique Opportunities

Cell Stem Cell. 2017 Feb 2;20(2):157-160. doi: 10.1016/j.stem.2017.01.006.

Authors

Antonio Del Sol¹, Hans J Thiesen², Jaime Imitola³, Rafael E Carazo Salas⁴

Affiliations

¹ Computational Biology Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, Belvaux 4367, Luxembourg. Electronic address: antonio.delsol@uni.lu.
² Institute of Immunology, Universitätsmedizin Rostock, University of Rostock, 18051 Rostock, Germany.
³ Laboratory for Neural Stem Cells and Functional Neurogenetics, Departments of Neurology and Neuroscience, and Molecular Biology and Cancer Genetic Program, The James Comprehensive Cancer Hospital, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA.
⁴ Pharmacology Department and Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, UK. Electronic address: rafael.carazosalas@bristol.ac.uk.

PMID: 28157497
DOI: 10.1016/j.stem.2017.01.006

Abstract

Achieving the promises of stem cell science to generate precise disease models and designer cell samples for personalized therapeutics will require harnessing pheno-genotypic cell-level data quantitatively and predictively in the lab and clinic. Those requirements could be met by developing a Big-Data-driven stem cell science strategy and community.

MeSH terms

Animals
Computational Biology
Humans
Phenotype
Science*
Statistics as Topic
Stem Cells / cytology*
Tissue Engineering*