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

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

Integrated framework to model cellular phenotype as a component of biochemical networks

Identification of important molecules in a biological pathway is a critical step in rationally developing targets for manipulating cellular behavior. Computational models that interlink the molecular expression in cellular networks to the phenotypes offer a handle on the important regulatory aspects of the cellular behavior. In the present study, we developed an approach based on fuzzy logic to express the cellular phenotype as a function of the gene expression ratios. Using micro-array data from the Yeast cell cycle studies, we developed an integrated model that treats the phenotypic data in the same manner as the gene expression profiles. Iterative simulations using the network model, mimicking experimental approaches, provided insights to help identify molecules that are most important for the control of the phenotype. These high-impact molecules are likely to make good targets for knockout experiments that are aimed at altering the phenotype or cell behavior.M.S., Biomedical Engineering -- Drexel University, 200

An integrated framework to model cellular phenotype as a component of biochemical networks.

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