Key challenges facing data-driven multicellular systems biology

TL;DR

This paper discusses the main challenges in advancing data-driven multicellular systems biology and emphasizes the need for community collaboration to develop interoperable tools and datasets.

Contribution

It identifies key obstacles and advocates for a community-driven approach to foster progress in multicellular systems biology.

Findings

Highlighting the importance of interoperable data and tools

Identifying community and funding as critical factors

Emphasizing the potential of collaborative ecosystems

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.