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Key challenges facing data-driven multicellular systems biology.

Paul Macklin1

  • 1Department of Intelligent Systems Engineering, Indiana University, 700 N Woodlawn Ave, Bloomington, IN 47408, USA.

Gigascience
|October 25, 2019
PubMed
Summary
This summary is machine-generated.

Sophisticated experiments and computational models can advance multicellular systems biology. Overcoming key challenges will create an integrated ecosystem for robust, data-driven biological research.

Keywords:
big datachallengesdata standardsdata-drivenmachine learningmulticellular systems biologymultidisciplinaryopen dataopen sourcesimulations

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Area of Science:

  • Multicellular systems biology
  • Computational biology
  • Biological data science

Background:

  • Sophisticated experimental techniques and large-scale computational models offer new avenues for hypothesis testing in multicellular systems.
  • Current research in multicellular systems biology is often limited by isolated tools and datasets.

Purpose of the Study:

  • To review the key challenges hindering the development of robust, repeatable, data-driven multicellular systems biology.
  • To outline a path toward a community-driven ecosystem of interoperable resources.

Main Methods:

  • Literature review of current challenges and opportunities in multicellular systems biology.
  • Analysis of the requirements for data integration and computational platform interoperability.

Main Results:

  • Identified critical challenges in achieving robust and repeatable data-driven multicellular systems biology.
  • Proposed a vision for an integrated ecosystem of data, software, and modeling platforms.

Conclusions:

  • Addressing these challenges requires community and financial commitment.
  • Solving these challenges will accelerate our understanding of complex multicellular systems.