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Advances in Bioinformatics
Volume 2011, Article ID 608295, 14 pages
Research Article

An Integrated Framework to Model Cellular Phenotype as a Component of Biochemical Networks

1Department of Cancer Biology, Kimmel Cancer Center, Thomas Jefferson University, Bluemle Life Sciences Building, 233 S. 10th Street, Philadelphia, PA 19107, USA
2School of Biomedical Engineering, Science and Health Systems, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA
3American Association for Cancer Research, Cancer Discovery, 615 Chestnut Street, Philadelphia, PA 19106, USA

Received 23 June 2011; Accepted 26 August 2011

Academic Editor: Y. Van de Peer

Copyright © 2011 Michael Gormley 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.


Identification of regulatory molecules in signaling pathways is critical for understanding cellular behavior. Given the complexity of the transcriptional gene network, the relationship between molecular expression and phenotype is difficult to determine using reductionist experimental methods. Computational models provide the means to characterize regulatory mechanisms and predict phenotype in the context of gene networks. Integrating gene expression data with phenotypic data in transcriptional network models enables systematic identification of critical molecules in a biological network. We developed an approach based on fuzzy logic to model cell budding in Saccharomyces cerevisiae using time series expression microarray data of the cell cycle. Cell budding is a phenotype of viable cells undergoing division. Predicted interactions between gene expression and phenotype reflected known biological relationships. Dynamic simulation analysis reproduced the behavior of the yeast cell cycle and accurately identified genes and interactions which are essential for cell viability.