(582am) Constraints-Based Flux Balance Analysis In Cancer Cell Growth and Metabolism

Several types of cancer cells/tissues adapt and evolve their metabolism as a strategy for increased cell growth and proliferation leading to malignancy. The genome-scale human metabolic model is thus an excellent platform to study a complex multi-hit, multi phenotype disease like cancer. Constraints-based flux balance analysis allows for integration of heterogeneous data-types as well as analysis to compute phenotype. The focus of this talk is the development and analysis of tissue specific models for cancer. Global microarray gene expression data is used to develop cell-line and patient models for lung cancer using from legacy data using the recent genome scale model of human metabolism, Recon2 as the basis. The algorithm for integration of the gene expression data defines a scaling function to convert expression levels to fix the maximum capacity of every reaction in the network. The scaling function allows for introducing parameters for not only post-transcriptional and translation regulatory effects that modulate protein concentration but also the microenvironment of the cells, that dictate activity of the proteins. Linear optimization results in a computed value for the optimal growth phenotype of the cell lines comparable to experimental values. The cell line models are further validated using in-house growth and substrate uptake rate data and phenotypic micro-arrays Biolog^TM. The predictions of clinical models using tissue data and blood data will be compared to the classical cell line models. The clinical models are interrogated for growth rates of patient always higher than the normal cohort. Integration of results of routine blood work, specialized clinical tests like PET (Positron Emission Tomography) scans allow for better prognosis but also fundamental understanding of the disease. The challenge for predictive differentiation of progressive disease states and their ability to predict outcomes of therapy will also be discussed.