Towards personalized genome-scale modeling of inborn errors of metabolism for systems medicine applications

Inborn errors of metabolism (IEMs) are a group of more than 1000 inherited diseases that are individually rare but have a cumulative global prevalence of 50 per 100,000 births. Recently, it has been recognized that like common diseases, patients with rare diseases can greatly vary in the manifestation and severity of symptoms. Here, we review omics-driven approaches that enable an integrated, holistic view of metabolic phenotypes in IEM patients. We focus on applications of Constraint-based Reconstruction and Analysis (COBRA), a widely used mechanistic systems biology approach, to model the effects of inherited diseases. Moreover, we review evidence that the gut microbiome is also altered in rare diseases. Finally, we outline an approach using personalized metabolic models of IEM patients for the prediction of biomarkers and tailored therapeutic or dietary interventions. Such applications could pave the way towards personalized medicine not just for common, but also for rare diseases.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Construction and Validation of a Genome-Scale Metabolic Network of Thermotoga sp. Strain RQ7

Thermotoga are anaerobic hyperthermophiles that have a deep lineage to the last universal ancestor and produce biological hydrogen gas accompanying cell growth. In recent years, systems-level approaches have been used to elucidate their metabolic capacities, by integrating mathematical modeling and experimental results. To assist biochemical engineering studies of T. sp. strain RQ7, this work aims at building a metabolic model of the bacterium that quantitatively simulates its metabolism at the genome scale. The constructed model, RQ7_iJG408, consists of 408 genes, 692 reactions, and 538 metabolites. Constraint-based flux balance analyses were used to simulate cell growth in both the complex and defined media. Quantitative comparison of the predicted and measured growth rates resulted in good agreements. This model serves as a foundation for an integrated biochemical description of T. sp. strain RQ7. It is a useful tool in designing growth media, identifying metabolic engineering strategies, and exploiting the physiological potentials of this biotechnologically significant organism.

Software and Methods for Computational Flux Balance Analysis

As genetic engineering of organisms has grown easier and more precise, computational modeling of metabolic systems has played an increasingly important role in both guiding experimental interventions and in understanding the results of metabolic perturbations.