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Center for Nonlinear Studies |
Understanding the correlation of gene specific mutations and tumor development has important implications in cancer therapy. Recent empirical data have elucidated the candidate cancer genes responsible for carcinogenesis through mutation and expression analysis. This work has revealed the heterogeneities in genotype that encode cancers of the same malignancy grade, providing evidence for the existence of multiple mutational paths that a population of cancer cells can take to manifest itself as a disease. The cell genotypes that are present in a tumor affect the malignancy grade through their effect on the phenotypes of individual cells that the tumor is comprised of. We use a graph theoretical approach to connect the gene expression and mutation data to cell phenotype. We have constructed a gene regulatory network from the KEGG pathway database. This network includes most accurately and completely the relevant pathways that contain the known cancer genes, which in turn encode distinct cell phenotypes. We are analyzing the network to predict the sensitivity of cell signaling pathways that control cell growth and death to alterations caused by gene mutations. The prevalence of gene mutations show no correlation to simple measures of their equivalent representations in the network. Because of the lack of necessary reaction rate data to model any of the interactions, we turn to a network boolean dynamics model in which the state of proteins, represented by nodes in the network, are on or off and are updated in time using functions depending on the network connections. When all the nodes are updated simultaneously at each time step we find that the phenotypic output resulting from the deterministic network dynamics are insensitive to the candidate gene mutations. With nonsimultaneous updating we find that the state space of the dynamics becomes too large to sample using random initial conditions. We employ a specific type of monte carlo algorithm to determine the proportions of inital conditions that have attractors whose protein profiles(on or off) are classified into distinct phenotypes. We consider 4 distinct cell phenotypes: Proliferation, Apoptosis, Survival, and None of the above. With this algorithm we estimate that the entire state space, whose size is on the order of 2800, can be sampled with less than the equivalent of 30,000 random initial conditions when we want to determine the proportions of the state space that lead to any of the predefined phenotypes. With this type of sampling we can determine whether changes in the network caused by mutations lead to altered proportions of states whose progression will end in the distinct phenotypes.
Host: Yi Jiang T-07