Image-Enhanced Systems Biology: A Multiscale, Multidimensional Approach to Modeling and Controlling Stem Cell Function

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.