  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.
