Abstract
Constraint-based models of metabolism have been used in a variety of studies on drug discovery, metabolic engineering, evolution, and multi-species interactions. These genome-scale models can be generated for any sequenced organism since their main parameters (i.e., reaction stoichiometry) are highly conserved. Their relatively low parameter requirement makes these models easy to develop; however, these models often result in a solution space with multiple possible flux distributions, making it difficult to determine the precise flux state in the cell. Recent research efforts in this modeling field have investigated how additional experimental data, including gene expression, protein expression, metabolite concentrations, and kinetic parameters, can be used to reduce the solution space. This mini-review provides a summary of the data-driven computational approaches that are available for reducing the solution space and thereby improve predictions of intracellular fluxes by constraint-based models.
Highlights
Genome-scale constraint-based metabolic models can be used to predict or describe cellular behaviors, such as growth rates, uptake/secretion rates, and intracellular fluxes. These models have been used for a variety of applications, involving studies on drug discovery [1], metabolic engineering [2], evolution [3], genome annotation [4,5,6], and multi-species interactions [7,8,9,10]
A common misconception is that these metabolic models rely on detailed kinetic parameters; such kinetic parameters are not required and are generally absent from most constraint-based models
Given the non-uniqueness of constraint-based model solutions, a growing number of methods have focused recently on incorporating additional constraints to reduce the solution space and thereby improve the precision and accuracy of model predictions. This editorial reviews recent methods that utilize additional biological information to further restrict metabolic fluxes, many of which are available in a variety of software packages [17,18,19,20]
Summary
Genome-scale constraint-based metabolic models can be used to predict or describe cellular behaviors, such as growth rates, uptake/secretion rates, and intracellular fluxes. Constraint-based metabolic models are developed by integrating genomic, biochemical, and physiological information for an organism, in a process that has been recently reviewed [11]. This editorial reviews recent methods that utilize additional biological information (e.g., gene or protein expression, metabolite concentrations, and kinetic parameters) to further restrict metabolic fluxes, many of which are available in a variety of software packages [17,18,19,20].
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