Abstract
With the advent of high-throughput technologies, the field of systems biology has amassed an abundance of “omics” data, quantifying thousands of cellular components across a variety of scales, ranging from mRNA transcript levels to metabolite quantities. Methods are needed to not only integrate this omics data but to also use this data to heighten the predictive capabilities of computational models. Several recent studies have successfully demonstrated how flux balance analysis (FBA), a constraint-based modeling approach, can be used to integrate transcriptomic data into genome-scale metabolic network reconstructions to generate predictive computational models. In this review, we summarize such FBA-based methods for integrating expression data into genome-scale metabolic network reconstructions, highlighting their advantages as well as their limitations.
Highlights
A central challenge in the development of systems biology is the integration of high-throughput data to generate predictive computational models
The user supplies a set of gene expression data, a genome-scale metabolic network reconstruction, and at least one specified metabolic functionality, or objective function, that the cell is assumed to achieve
In cases where the gene expression data is below a certain threshold, tight constraints are placed on the flux through the corresponding reactions in the reconstruction; in cases where the gene expression is above a certain threshold, loose constraints are placed on the flux through the corresponding reactions
Summary
A central challenge in the development of systems biology is the integration of high-throughput data to generate predictive computational models. GIMME One such method that guarantees to both produce a functioning metabolic model based on gene expression levels and quantify the agreement between the model and the data is called the Gene Inactivity Moderated by Metabolism and Expression (GIMME) algorithm (Becker and Palsson, 2008) To execute this algorithm, the user supplies a set of gene expression data, a genome-scale metabolic network reconstruction, and at least one specified metabolic functionality, or objective function, that the cell is assumed to achieve. In the event that the resulting model is unable to achieve the desired objective function, GIMME solves an LP problem that adds sets of the inactive reactions back into the system in such a way that minimizes deviation from the expression data To quantify this deviation, an inconsistency score is calculated for each reaction by multiplying the flux necessary to achieve a defined value of the objective function and the distance between the experimental mRNA transcript level and the threshold level. IMAT requires that reactions catalyzed by the products of highly expressed genes
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