Understanding the workings of stem cells is now seen as essential to the progress of regenerative medicine, tissue engineering and cancer therapeutics. Recently, there has been a growing awareness that the behaviors of stem cells emerge out of the highly complex interactions of dynamic networks of gene regulation and proliferative control. The need to describe, predict, and ultimately understand such interactions is drawing ideas and investigators from the field of systems biology into stem cell biology.
The last few years have seen significant advances in our understanding of the molecular mechanisms of stem-cell-fate specification. New and emerging high-throughput techniques, as well as increasingly accurate loss-of-function perturbation techniques, are allowing us to dissect the interplay among genetic, epigenetic, proteomic, and signaling mechanisms in stem-cell-fate determination with ever-increasing fidelity.
Taken together, recent reports using these new techniques demonstrate that stem-cell-fate specification is an extremely complex process, regulated by multiple mutually interacting molecular mechanisms involving multiple regulatory feedback loops. Given this complexity and the sensitive dependence of stem cell differentiation on signaling cues from the extracellular environment, how are we best to develop a coherent quantitative understanding of stem cell fate at the systems level? One approach that we and other researchers have begun to investigate is the application of techniques derived in the computational disciplines (mathematics, physics, computer science, etc.) to problems in stem cell biology.
Systems biology emerged around 2000, along with microarrays and other high-throughput techniques that can collect hard drives full of quantitative biological data. By using data to build simulations of biological phenomena, the goal is to create hypotheses and ‘intuition’ beyond that of a single human brain. Not only does the analysis of all this data require a mathematical framework and computational tools, but it presents interesting questions for theoreticians with applied mathematics, computer science or engineering backgrounds.
How does a stem cell decide what specialized identity to adopt – or simply to remain a stem cell? A new study suggests that the conventional view, which assumes that cells are “instructed” to progress along prescribed signaling pathways, is too simplistic. Instead, it supports the idea that cells differentiate through the collective behavior of multiple genes in a network that ultimately leads to just a few endpoints – just as a marble on a hilltop can travel a nearly infinite number of downward paths, only to arrive in the same valley.
The findings, published in the May 22 issue of Nature, give a glimpse into how that collective behavior works, and show that cell populations maintain a built-in variability that nature can harness for change under the right conditions. The findings also help explain why the process of differentiating stem cells into specific lineages in the laboratory has been highly inefficient.
