Abstract
The integration of high-throughput data to build predictive computational models of cellular metabolism is a major challenge of systems biology. These models are needed to predict cellular responses to genetic and environmental perturbations. Typically, this response involves both metabolic regulations related to the kinetic properties of enzymes and a genetic regulation affecting their concentrations. Thus, the integration of the transcriptional regulatory information is required to improve the accuracy and predictive ability of metabolic models. Integrative modeling is of primary importance to guide the search for various applications such as discovering novel potential drug targets to develop efficient therapeutic strategies for various diseases. In this paper, we propose an integrative predictive model based on techniques combining semantic web, probabilistic modeling, and constraint-based modeling methods. We applied our approach to human cancer metabolism to predict in silico the growth response of specific cancer cells under approved drug effects. Our method has proven successful in predicting the biomass rates of human liver cancer cells under drug-induced transcriptional perturbations.
Highlights
Understanding and predicting how genetic and environmental perturbations alter the behavior and subsequently the phenotype of an organism is a major goal of systems biology
We focus on the Probabilistic Regulation of Metabolism (PROM) [13], which is the first approach that accounts for genetic variations on the metabolic network in an automated fashion
The availability of annotated sequenced genomes which provides knowledge about metabolic enzymes has made possible the reconstruction of genome-scale metabolic networks. Such knowledge allows to link a gene to metabolic reactions via Gene–Protein Reaction (GPR) relationships, through the enzymes catalyzing each of the metabolic reactions
Summary
Understanding and predicting how genetic and environmental perturbations alter the behavior and subsequently the phenotype of an organism is a major goal of systems biology. Given the flood of biological data (genomic, proteomic, and transcriptomic) and interaction knowledge presented in pathway databases, significant efforts have been made in reconstructing different genome-scale networks for a wide variety of organisms ranging from bacteria to humans [1,2,3,4]. Due to their central role in the functioning of an organism, metabolic and gene regulatory networks have been extensively studied recently. Modeling and simulating the genome-scale metabolic network remains a significant challenge due to its size and complexity. Cellular metabolism is regulated through the control of enzyme activity by either activation or inhibition of the transcription
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