Identification of genome-scale metabolic network models using experimentally measured flux profiles

Abstract

Genome-scale metabolic network models can be reconstructed for well-characterized organisms using genomic annotation and literature information. However, there are many instances in which model predictions of metabolic fluxes are not entirely consistent with experimental data, indicating that the reactions in the model do not match the active reactions in the in vivo system. We introduce a method for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. This method, called optimal metabolic network identification (OMNI), allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. We applied the method to intracellular flux data for evolved Escherichia coli mutant strains with lower than predicted growth rates in order to identify reactions that act as flux bottlenecks in these strains. The expression of the genes corresponding to these bottleneck reactions was often found to be downregulated in the evolved strains relative to the wild-type strain. We also demonstrate the ability of the OMNI method to diagnose problems in E. coli strains engineered for metabolite overproduction that have not reached their predicted production potential. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data.

Figure 1. Bilevel Approach to OMNI

(A) Schematic illustration of the optimal metabolic network identification approach. Changes in the model reaction set lead to changes in the FBA-predicted optimal flux distribution (yellow) that can be compared to the experimental fluxes (red). (B) Bilevel optimization scheme for optimal metabolic network identification.

Figure 2. The Dependence of the Model Prediction Errors on the Number of Reactions Deleted from the Parental Strain Model

(A) The overall combined intracellular and growth rate prediction error as measured by the percentage of the OMNI objective value for the optimal modified model compared with the objective value for the parental strain model. (B) The growth rate prediction error (percentage of the experimental growth rate) for the optimal modified model identified by the OMNI method.

Figure 3. Correspondence between Bottleneck Reactions Identified by the OMNI Approach and Gene Expression Changes in the Evolved Knockout Strains Relative to the Wild-Type E. coli Strain

Expression changes, reported as log₂ ratios and statistically significant expression changes (see Materials and Methods for details) in each evolved strain, are shown by +/− signs depending on the direction of the change. The reaction names are listed in the first column and the corresponding genes are listed in the second column. The genes corresponding to the best combination of reaction bottlenecks of up to three reactions identified by the OMNI approach (Table 3) for each strain are shown by white boxes.