TY - JOUR T1 - Decomposition of Gene Expression State Space Trajectories A1 - Mar, Jessica C. A1 - Quackenbush, John Y1 - 2009/12/24 N2 - Author Summary Understanding how cells differentiate from one state to another is a fundamental problem in biology with implications for better understanding evolution, the development of complex organisms from a single fertilized egg, and the etiology of human disease. One way to view these processes is to examine cells as “complex adaptive systems” where the state of all genes in a cell (more than 20,000 genes) determines that cell's “state” at a given point in time. In this view, differentiating cells move along a path in “state space” from one stable “attractor” to another. In a 2005 paper, Sui Huang and colleagues presented an experimental model in which they claimed to have evidence for such attractors and for the transitions between them. The problem with this approach is that although it is intuitively appealing, it lacks predictive power. Reanalyzing Huang's data, we demonstrate that there is an alternative interpretation that still allows for a state space description but which has greater ability to make testable predictions. Specifically, we show that these abstract state space trajectories can be mapped onto more well-known pathways and represented as a “core” differentiation pathway and “transient” processes that capture the effects of the treatments that initiate differentiation. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 5 IS - 12 UR - https://doi.org/10.1371/journal.pcbi.1000626 SP - e1000626 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1000626 ER -