Genome-scale reconstructions allow simulation of phenotypes through constraint-based analysis and flux balance analysis [6
]. In addition, GPR associations provide a mechanistic basis connecting the genotype with the phenotype (). In this section, we focus on the application of simulation to predict and form a mechanistic basis for pathological and drug-treated states (.iv).
The systemic effect of inherited inborn errors of metabolism has been studied using Recon 1. The Online Mendelian Inheritance of Man (OMIM) database [44
] catalogues all known hereditary morbid single nucleotide polymorphisms (SNPs). Shlomi et al. simulated SNPs by reducing the flux of the affected reactions in Recon 1 [66
]. They found that there was a systemic effect on the network, particularly in changes to the variability of the substrate uptake and secretions by Recon 1. Thus the network-based predictions can be used to determine changes in biofluid metabolite levels revealing potential biomarkers. In another study, Veeramani and Bader calculated flux correlations between reactions in Recon 1, showing that flux linked reactions had similar morbid SNP phenotypes [67
]. This work is very similar to that done previously on the human mitochondria network [43
Similar to the SNP studies and the earlier enzymopathies of erythrocytes, Gille et al. used HepatoNet 1 to study hepatic enzyme deficiencies [52
]. Genes, and associated reactions, were knocked out systematically to determine the effect on liver metabolic functions. The authors found that for 80 enzymes and transporters that were computationally predicted to be essential for at least one metabolic function, clinical symptoms have been reported in literature.
In addition to SNP and enzyme deficiencies, a recent study looked at the systemic effect of imprinted genes on metabolism [68
]. In particular, Sigurdsson et al. focused on maternal deletion of ATP10A and showed that there was an anabolic effect, which corroborated with the clinical phenotype.
Recon 1 has also been used to study cancer metabolism and potential pharmaceuticals. Folger et al. calculated drug targets and their synergies using the generic cancer metabolic network and Recon 1 [49
]. They were able to predict selective targets that preferentially affected the cancer model, taking advantage of metabolic auxotrophies. The method was further leveraged to predict selective drug targets in renal-cell cancer, which are deficient in fumarate hydratase. A pathway for heme biosynthesis and degradation was implicated as essential in renal-cell cancer, but not normal cells. The computational prediction was followed up with experimental validation in both cell lines and mouse models [69
In addition, the Warburg effect has been computationally studied as it relates to cancerous tumors [70
]. Shlomi et al. use an enzyme solvent capacity consideration [71
] alongside constraint-based modeling. Increasing in silico
growth rate with the new solvent capacity constraints forces an increase in glycolytic flux, providing a possible mechanism for the Warburg effect. In addition, Recon 1 recapitulated known characteristics of cancer cells including oncogenic progression and a preference for glutamine uptake.
Genome-scale metabolic reconstructions have also been used to study neural disorders. The multi-cellular models of brain metabolism were used to study the differences between neuron types in Alzheimer’s disease [52
]. Key enzyme deficiencies were simulated and Lewis et al. were able to metabolically differentiate one of the cell types (GABAergic) from the other three. Their findings corroborate with the clinical phenotype as GABAergic neurons are known to be less metabolically affected by Alzheimer’s disease.
As systems biology represents a holistic approach to studying human pathology, Recon 1 has been used to study comorbidity and epistasis. Comorbidity refers to an unrelated concomitant pathology to a primary disease. Using Recon 1 and the KEGG database as knowledge-bases, Lee et al. built a metabolic disease network that accounts for flux correlations between reactions, topology, and associated diseases [72
]. Simulations showed that diseases associated within correlated reaction sets showed higher comorbidity, were more prevalent in the population, and resulted in a higher chance of death. In an another work that focuses on the topology of the metabolic network, Imielinski et al. studied epistasis by calculating reaction sets that would synergistically deactivate cellular functions [73
]. In particular, knockout reaction sets were calculated for deactivating fumarase, which is known to play a role in cancer. The authors found that there are many knockout reaction sets and the reactions involved within a set can be topologically distant.
Genome-scale metabolic reconstructions have also been used to simulate drug effects. In particular, the renal metabolic model reconstructed by Chang et al. was used to study off-target drug binding and the systemic consequences [46
]. The renal metabolic model was integrated with structural bioinformatics techniques that allow prediction of ligand binding to proteins in the metabolic network. Thus, a drug’s systemic effect on the kidney’s physiological function can be simulated on a genome-scale level. This allows for easy off-target drug screening before potential testing in drug trials. In another drug related study, Costa et al. built a computational approach utilizing Recon 1, tissue expression profiles, and subcellular localization information to predict genes that if controlled by drugs would elicit a phenotypic response [74
]. The approach can also be used to determine morbid genes.
Constraint-based modeling and Recon 1 allows simulating normal and diseased human phenotypes. GPR associations allow for linking the genotype and phenotype in simulations to form a mechanistic basis to physiology, pathology, and pharmacology. Recon 1 has been used in many studies to determine the underlying mechanisms of a particular disease phenotype as well as in predicting the phenotype of genetic perturbations.