Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine [1]. One of their main benefits is that they can provide genotype-to-phenotype projections, such as growth rate and nutrient uptake predictions, and predictions of metabolic flux values. The latter can be used to assess metabolic reaction activities in different contexts ^[2][3][2,3]. A GEM describes a metabolic network with a stoichiometric matrix [4] and each reaction is constrained by its minimal and maximal flux bounds. Moreover, a GEM usually encodes the information on gene–protein reaction (GPR) associations, which can be applied in the adaptation of a GEM to a specific context described with high-throughput data, such as transcriptomics or proteomics data. Such integration can be performed with the application of context-specific model reconstruction algorithms, which are used to adapt the flux bounds of a reference model to a given context described with (at least one) high-throughput dataset. This allows one to at least partially automatise the reconstruction of tissue-specific, cell type-specific, disease-specific, or even personalised GEMs. Further investigation of context-specific GEMs includes comparative analyses between different conditions (e.g., analysis of metabolic reprogramming in cancer cells [5]), and identification of biomarkers and therapeutic targets in different diseases or disorders [6].

2. Genome-Scale Metabolic Modelling

Genome-scale metabolic models (GEMs) aim to systematically encode our knowledge of the metabolism of an organism. Reference GEMs describing generic models of a cell are constructed with a combination of automated approaches and manual curation. Such reconstructions are based on genome annotation data and a myriad of additional data sources, including biochemical databases, organism-specific databases, experimental data, and literature data [7]. GEM reconstruction, its refinement, adaptation, and analysis are commonly performed with the aid of model building tools [8] and reconstruction and analysis frameworks, such as COBRA [9], COBRApy [10], RAVEN [11] or PSAMM [12]. These frameworks provide implementation of a vast scope of methods with different goals, including the reconstruction of a draft metabolic model [13], visualisation of metabolic maps (e.g., see Paint4Net [14]), identification of blocked reactions and gap filling [15] and analysis of reconstructed GEMs, such as optimal steady-state flux assessment [16] or flux sampling [17]. GEMs have been reconstructed for more than 1,000 different organisms [18]. Moreover, advances in our knowledge guide iterative refinements of GEMs. For example, Recon presents a generic human GEM that has gone through several iterations from Recon 1 [19] to Recon 2.2 [20] and to Recond3D [21], and was later extended and integrated with the HMR2.0 database [22] to obtain the Human–GEM model [23]. In the context of biomedicine, GEM applications range from the identification of disease biomarkers to the prediction of drug targets [24], drug repurposing [25] and cancer research [26]. GEMs can also be applied in a vast array of bioengineering applications [18]. These range from predicting cellular phenotypes (e.g., in the context of predicting maximal growth in different conditions and identification of an optimal medium [27]) to guiding metabolic engineering (e.g., in the context of optimal strain design [28]) and identification of a minimal gene set [29]. Most computational approaches aimed at the analysis of GEMs rely on constraint-based modelling and are based on flux balance analysis (FBA) [16] or its derivations. FBA aims to find the metabolic flux values that are consistent with a set of given constraints (minimal and maximal flux bounds) and which bring the system to a steady state. Moreover, FBA requires a specification of required metabolic functionality (RMF) that is used to define an objective function for optimisation. The optimisation can then be formulated as a linear programming (LP) problem. However, since the constraints in this formulation are usually mathematically underdetermined [30], several nonunique optimal solutions exist. To assess metabolic flux ranges through reactions that bring the system to a near optimal, or optimal, steady state, flux variability (FVA) can be used [31]. However, the latter still requires the specification of a RMF, which is hard to identify in a general context and may yield biased results. Moreover, it has been shown that the definition of the RMF strongly affects the precision of model predictions [32]. An unbiased alternative to methods relying on RMF-based optimisation is to use flux sampling of the feasible solution space without a specific optimisation criterion [17]. Reconstructed GEMs, as described above, present the metabolism of a general cell in an arbitrary context and, thus, compose generic models. Since only specific metabolic reactions are, in fact, active in a specific cell [33], these models need to be further tailored to a specific context in which only a subset of enzymes is active [34]. This process can be carried out using different reconstruction algorithms, in combination with high-throughput datasets and available biological knowledge, to obtain context-specific models (see Figure 1 and Tables 1 and 2). The latter present a subset of a generic GEM and can be used to describe the metabolism of a specific cell in a specific context [35]. Finally, such a model can describe a cell-, a tissue-, a disease-, or even an individual-specific model.