Francisco G. Vital-Lopez

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The identification of optimal intervention strategies is a key step in designing microbial strains with enhanced capabilities. In this paper, we propose a general computational procedure to determine which genes/enzymes should be eliminated, repressed or overexpressed to maximize the flux through a product of interest for general kinetic models. The(More)
Despite the close association between gene expression and metabolism, experimental evidence shows that gene expression levels alone cannot predict metabolic phenotypes, indicating a knowledge gap in our understanding of how these processes are connected. Here, we present a method that integrates transcriptome, fluxome, and metabolome data using kinetic(More)
Tumor progression depends on the intricate interplay between biological processes that span the molecular and macroscopic scales. A mathematical agent-based model is presented to describe the 3-D (three-dimensional) progression of a brain tumor type (i.e., glioblastoma multiforme) as the collective behavior of individual tumor cells whose fate is determined(More)
Modeling and analysis of the dynamics of signaling transduction networks can be powerful tools to understand and predict how cells will respond to native signals and artificial perturbations. This is of special interest for analyzing disease processes associated with signal transduction malfunctioning and to contribute to the development of efficient drug(More)
A hallmark of Pseudomonas aeruginosa is its ability to establish biofilm-based infections that are difficult to eradicate. Biofilms are less susceptible to host inflammatory and immune responses and have higher antibiotic tolerance than free-living planktonic cells. Developing treatments against biofilms requires an understanding of bacterial(More)
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