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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 293980, 10 pages
Research Article

Modeling and Visualizing Cell Type Switching

1Computer Science Department, Utah State University, Logan, UT 84322, USA
2Biology Department, Utah State University, Logan, UT 84322, USA
3Center for Integrated BioSystems, Utah State University, Logan, UT 84322, USA
4Institute for Systems Biology, Seattle, WA 98109, USA
5Synthetic Biomanufacturing Institute, Logan, UT 84322, USA

Received 30 September 2013; Revised 20 December 2013; Accepted 10 January 2014; Published 14 April 2014

Academic Editor: Marco Villani

Copyright © 2014 Ahmadreza Ghaffarizadeh et al. This 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 differentiation is critical in explaining development and for taming diseases such as cancer. Differentiation 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 transdifferentiation or differentiation off standard paths. This work introduces a new analysis and visualization technique that is capable of representing all possible transitions between cell states compactly, quantitatively, and intuitively. This 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 profiles and the potential cell types that they represent. A visualization tool called CellDiff3D 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 influence of gene expression noise and mutational changes during myeloid cell differentiation are presented as a demonstration of the CellDiff3D technique, a new approach to quantify and envision all possible cell state transitions in any lineage network.