Key challenges facing data-driven multicellular systems biology

Gigascience. 2019 Oct 1;8(10):giz127. doi: 10.1093/gigascience/giz127.

Abstract

Increasingly sophisticated experiments, coupled with large-scale computational models, have the potential to systematically test biological hypotheses to drive our understanding of multicellular systems. In this short review, we explore key challenges that must be overcome to achieve robust, repeatable data-driven multicellular systems biology. If these challenges can be solved, we can grow beyond the current state of isolated tools and datasets to a community-driven ecosystem of interoperable data, software utilities, and computational modeling platforms. Progress is within our grasp, but it will take community (and financial) commitment.

Keywords: big data; challenges; data standards; data-driven; machine learning; multicellular systems biology; multidisciplinary; open data; open source; simulations.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Review

MeSH terms

  • Big Data
  • Metadata
  • Systems Biology / methods*