Abstract
Constraint-based metabolic modeling methods such as Flux Balance Analysis (FBA) are routinely used to predict the effects of genetic changes and to design strains with desired metabolic properties. The major bottleneck in modeling genome-scale metabolic systems is the establishment and manual curation of reliable stoichiometric models. Initial reconstructions are typically refined through comparisons to experimental growth data from gene knockouts or nutrient environments. Existing methods iteratively correct one erroneous model prediction at a time, resulting in accumulating network changes that are often not globally optimal. We present GlobalFit, a bi-level optimization method that finds a globally optimal network, by identifying the minimal set of network changes needed to correctly predict all experimentally observed growth and non-growth cases simultaneously. When applied to the genome-scale metabolic model of Mycoplasma genitalium, GlobalFit decreases unexplained gene knockout phenotypes by 79%, increasing accuracy from 87.3% (according to the current state-of-the-art) to 97.3%. While currently available computers do not allow a global optimization of the much larger metabolic network of E. coli, the main strengths of GlobalFit are already played out when considering only one growth and one non-growth case simultaneously. Application of a corresponding strategy halves the number of unexplained cases for the already highly curated E. coli model, increasing accuracy from 90.8% to 95.4%.
Highlights
Metabolism is the best understood large cellular system
Mathematical models that aim to describe the complete metabolism of a cell help us understand cellular metabolic capabilities and evolution, and aid the biotechnological design of microbial strains with desired properties
Draft models are frequently improved through adjustments that increase the agreement of growth/non-growth predictions with observations from gene knockout experiments
Summary
Genome-scale metabolic models that largely rely on constraints for mass balance (i.e., all internal metabolites that are produced must be consumed) are routinely applied to predict a wide range of metabolic phenomena [1]. The most widely-used of these constraint-based methods, Flux Balance Analysis (FBA), has been successfully applied to predict a range of biological phenomena such as gene knockout effects [1] and the evolutionary adaptation of microbial strains [2,3,4], and has been employed to predict drug targets [5] and to design microbial strains for bioengineering [6]. The resulting draft reconstructions often contain gaps: the modeled organism or its gene knockout strain can grow in vivo, while the model is unable to produce biomass in silico in the same metabolic environment (false-negative predictions, FNp). Gap filling methods have been introduced to resolve individual FNp through a minimal number of network changes, making irreversible reactions reversible or adding reactions from a database [8,9,10,11]
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