Research Article Modeling and Visualizing Cell Type Switching Ahmadreza Ghaffarizadeh, 1 Gregory J. Podgorski, 2,3 and Nicholas S. Flann 1,4,5 1 Computer Science Department, Utah State University, Logan, UT 84322, USA 2 Biology Department, Utah State University, Logan, UT 84322, USA 3 Center for Integrated BioSystems, Utah State University, Logan, UT 84322, USA 4 Institute for Systems Biology, Seattle, WA 98109, USA 5 Synthetic Biomanufacturing Institute, Logan, UT 84322, USA Correspondence should be addressed to Nicholas S. Flann; nick.lann@usu.edu Received 30 September 2013; Revised 20 December 2013; Accepted 10 January 2014; Published 14 April 2014 Academic Editor: Marco Villani Copyright © 2014 Ahmadreza Ghafarizadeh et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Understanding cellular diferentiation is critical in explaining development and for taming diseases such as cancer. Diferentiation is conventionally represented using bifurcating lineage trees. However, these lineage trees cannot readily capture or quantify all the types of transitions now known to occur between cell types, including transdiferentiation or diferentiation of standard paths. his work introduces a new analysis and visualization technique that is capable of representing all possible transitions between cell states compactly, quantitatively, and intuitively. his method considers the regulatory network of transcription factors that control cell type determination and then performs an analysis of network dynamics to identify stable expression proiles and the potential cell types that they represent. A visualization tool called CellDif3D creates an intuitive three-dimensional graph that shows the overall direction and probability of transitions between all pairs of cell types within a lineage. In this study, the inluence of gene expression noise and mutational changes during myeloid cell diferentiation are presented as a demonstration of the CellDif3D technique, a new approach to quantify and envision all possible cell state transitions in any lineage network. 1. Introduction During development, a complex system of tissues and organs emerges from a single cell by the coordination of cell division, morphogenesis, and diferentiation. Understanding the diferentiation of cell types is necessary to understand- ing development and its associated defects, for improved control of stem cell diferentiation in therapeutic use and for taming diseases such as cancer. Cellular diferentiation occurs when a less specialized cell or its progeny becomes increasingly specialized by acquiring properties that allow speciic functions. In animals, diferentiation typically results in a terminally diferentiated state in which a specialized cell can no longer acquire the properties of other specialized adult cells. Recent discoveries, however, have shown that terminally diferentiated cells can be reprogrammed to revert back to multipotent and pluripotent stem cells which have the potential to diferentiate into other cell types [1, 2] or to transdiferentiate into other specialized cell types [3]. Diferentiating cells normally follow well deined paths to mature cell types. Taken together, these paths are referred to as a lineage tree. Pluripotent stem cells give rise to progeny that specialize into more constrained multipotent cells. In turn, multipotent cells produce a variety of stable, terminally diferentiated cells. his process is usually depicted as a tree with a pluripotent cell at its root, multipotent cells as intermediate nodes, and the mature cell types as branch tips. As an example, a simpliied portion of the myeloid cell lineage tree is illustrated in Figure 1. his igure shows that common myeloid progenitor stem cells produce two pluripotent cell types, a megakaryocyte-erythrocyte progeni- tor and a granulocyte-monocyte progenitor, that in turn pro- duce terminally diferentiated erythrocytes, megakaryocytes, monocytes, and granulocytes. Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2014, Article ID 293980, 10 pages http://dx.doi.org/10.1155/2014/293980