## 6 Genome-Scale Modeling: A Suitable Platform for Improving CA Production  
As previously described, the development of high-throughput techniques has allowed to measure and quantify a high number of cellular molecular constituents from the different biological levels (genome, RNAs, proteins, metabolites, etc.). The analysis of omics data is giving lights about the interrelated or individual relationship of biological levels (genes, proteins, regulators, and metabolites) and the production of specialized metabolites [137; 138; 139; 140]. However, the integration of multiple omics datasets for analyzing the interplay between biological levels is still a challenging task [141; 142; 143; 144; 145]. Most of the proposed methodologies for multi-omics data integration have been focused on the so-called element-based approaches (correlation, clustering, and multivariate analysis) [147]. These are useful to define correlations or groups in a particular biological level and a specific condition, but not to reveal the molecular mechanisms associated with a specific cell phenotype. However, the use of mathematical-based approaches considering system components, network interaction, and physicochemical properties of the cell, poses as a suitable alternative for multi-omics data integration. Such approaches can be used to infer the interplay between biological levels through the identification of mechanisms governing the cell phenotype. A mathematical-based approach that allows studying the metabolic fluxes and phenotypes of a cell under a plethora of nutritional/genetic/regulatory perturbations is the genome-scale metabolic modeling (GEMs) [148; 149]. This mathematical platform along with the integration of omics data (genomics, transcriptomics, proteomics, metabolomics, etc.) has demonstrated usefulness in guiding experimental efforts and elucidating mechanisms affecting cell phenotypes in many different cell types [150; 151; 152; 153; 154; 155; 156]. Usually, GEMs contain all the biochemical knowledge of a cell through the formulation of mass balances per metabolites and the existing association between genes, proteins/enzymes, and reactions [157]. The stationary mass balance is the simplest mathematical formulation allowing computing carbon fluxes under specific nutritional conditions. The mass balances are represented in a stoichiometric matrix multiplied by an unknown vector of reaction velocities (reaction fluxes), configuring a linear programming problem with an objective function based on some evolution criteria like the maximization of growth or energy production yield [158]. Under this consideration the in silico cells use most of the available nutrients to grow as expected to occur under the appropriated biological conditions [159]. This approach is vastly known as Flux Balance Analysis (FBA) and it is widely used for studying cell metabolism in many areas of research [160; 161]. Application and use of genome-scale metabolic modeling of _Streptomyces_ bacteria is currently an active field of study [138; 162; 163; 164; 165; 166; 167; 168; 169; 170; 171; 172; 173; 174; 175; 176; 177; 178; 179; 180; 181; 182; 183; 184; 185; 186; 187; 188; 189; 190; 191; 192; 1  
In 2010, [PERSON] and colleagues [122] used the annotation information of _S. clawuligerus_ ATCC 27064 genome in the reconstruction of the first genome-scale metabolic model of _S. clawuligerus_ (iMM864). This model encompasses 1492 reactions, 971 metabolites, and 864 genes; it was used to simulate growth on different nitrogen and carbon sources and evaluate essentiality of the 1.8 Mb megaplasmid identified in _S. clawuligerus_ ATCC 27064. Later, the same authors demonstrated that the FBA predicts the metabolic fluxes through the biosynthetic precursors of CA; in line with the gene expression data of the _S. clawuligerus_ overproducer strains (_claR/caR_ overexpression and _\(\Delta\)gap1_) [118]. In 2015, a stoichiometric model of _S.