The promise of stem cell-based therapy is predicated on harnessing the
plasticity of stem cell phenotypes to repair or replace damaged tissues. As technologies
for detecting, isolating, modifying, tracking, and even inducing stem cells improve, the
very definition of what constitutes a stem cell is now an open question. Addressing this
fundamental problem has triggered an explosion of activity that spans the entire breadth
of biological fields, from molecular biology to population biology. While this has
clearly increased the gross amount of information concerning stem cells, its net impact
is limited by a lack of integrative multiscale models that are readily accessible to
researchers from many disciplines. The field of embryonic stem (ES) cell biology is a
good example of the strengths and limitations of the segregative reductionist approach.
The goal of this brief review is to highlight some of the most promising recent advances
in embryonic stem cell research, with an emphasis on how data gathered from one level
can benefit research across multiple scales.
Keywords: Multiscale modeling, embryonic stem cells, concurrent methods,
hierarchical methods, systems biology, design optimization, feature selection, cellgraphs,
induced pluripotent stem cells, supervised learning, machine learning, stem
cell niche, imaging, graph theory, tissue modeling Tissue structure/function, cell-cell
communication.
