Inferring Pathological Metabolic Patterns in Breast Cancer Tissue from Genome-Scale Models.

We will consider genome-scale metabolic models that attempt to describe the metabolism of human cells focusing on breast cells. The model has two versions related to the presence or absence of a specific breast tumor. The aim will be to mine these genome-scale models as a multi-objective optimization problem in order to maximize biomass production and minimize the reactions whose enzymes that catalyze them may have undergone mutations (oncometabolite), causing cancer cells to proliferate. This study discovered characteristic pathological patterns for the breast cancer genome-scale model used. This work presents an in silico BioCAD methodology to investigate and compare the metabolic pathways of breast tissue in the presence of a tumor in contrast to those of healthy tissue. A large number of genome-scale metabolic model simulations have been carried out to explore the solution spaces of genetic configurations and metabolic reactions. An evolutionary algorithm is employed to guide the search for possible solutions, and a multi-objective optimization principle is used to identify the best candidate solutions.