Genome‐scale metabolic models applied to human health and disease

Abstract

Advances in genome sequencing, high throughput measurement of gene and protein expression levels, data accessibility, and computational power have allowed genome-scale metabolic models (GEMs) to become a useful tool for understanding metabolic alterations associated with many different diseases. Despite the proven utility of GEMs, researchers confront multiple challenges in the use of GEMs, their application to human health and disease, and their construction and simulation in an organ-specific and disease-specific manner. Several approaches that researchers are taking to address these challenges include using proteomic and transcriptomic-informed methods to build GEMs for individual organs, diseases, and patients and using constraints on model behavior during simulation to match observed metabolic fluxes. We review the challenges facing researchers in the use of GEMs, review the approaches used to address these challenges, and describe advances that are on the horizon and could lead to a better understanding of human metabolism. WIREs Syst Biol Med 2017, 9:e1393. doi: 10.1002/wsbm.1393 For further resources related to this article, please visit the WIREs website.

Computational Modeling of the Human Microbiome

“…The human microbiome is an ecological setting where different microbial species are interacting with each other through metabolite and protein exchange and is dynamic and complex in nature [3]. The diversity of microorganisms currently identified is high (Figure 2) and the relationships between organisms can be either cooperative or competitive where microbes exchange the essential elements for their own benefit or the dominant microbe may try to exploit the maximum resources using its own metabolic network and secrete inhibitory agents against other microbes [154]. These interactions form the basis of the working of the entire human microbiome.…”

“…One approach that can facilitate studying the human microbiome as an interacting, dynamic community is community genome scale modeling [86]. However, challenges exist to utilizing genome scale modeling (GEM) including the low number of models for human microbiome species and the need for further improvements in computational frameworks for large-scale microbial communities (including appropriate objective functions and compartmentalization issues) [28,35,154]. Ongoing research is working on identifying and annotating novel human microbiome species which is needed as a basis for metabolic modeling.…”

Improving the accuracy of flux balance analysis through the implementation of carbon availability constraints for intracellular reactions

“…Because the stoichiometry of biochemical reactions is well‐established and widely available in curated public databases, GEMs can be used as an accurate representation of the metabolic capabilities of a particular organism (Kelk, Olivier, Stougie, & Bruggeman, ). Constraint‐based modeling has been successfully used to guide metabolic engineering strategies (Mishra et al, ), identify novel genes as antimicrobial drug targets (Mienda, Salihu, Adamu, & Idris, ), predict cellular phenotypes (Ramirez et al, ), analyze biological networks (Selvarasu et al, ), and study evolutionary processes (McCloskey, Palsson, & Feist, ; Pál et al, ) across more than 30 different organisms (Cook & Nielsen, ; Duarte & Herrg, ; Duarte et al, ; Feist et al, ; Förster, Famili, Fu, Palsson, & Nielsen, ; Hefzi et al, ; Reed, Vo, Schilling, & Palsson, ; Selvarasu et al, ).…”

The poly-omics of ageing through individual-based metabolic modelling

“…By modelling most of the known biochemistry of a cell, they allow achieving a mechanistic understanding of the genotype-phenotype relationship. Coupled with tools for integration of omics data, metabolic models have been successfully exploited in a wide range of applications in health and disease, including personalised, condition- and tissue-specific cancer modelling [ 34 – 37 ].…”