## 1 Introduction
For all these reasons, it is critical to understand plant metabolism on a system-wide level to identify metabolic pathways involved in the production of key metabolites, characterize specific phenotypes influenced by environmental factors, and explore alternative uses of the leaf, such as nutraceuticalals. In the last two decades, Genome-Scale Metabolic (GEM) reconstructions have become a fundamental tool taking advantage of the development of high throughput data of omics technologies to study and understand the complex interactions of organisms [28]. Regarding the development of omics technologies in _C. sativa_, the first sequenced and assembled genome was produced in 2011 by [PERSON] et al. [29] and since then, publications based on whole-genome sequencing and population studies [30; 31; 32], transcriptomics [33], proteomics [34], and metabolomics have resolved compelling questions about the chemotype of the plant and its relationship with geography or characteristic markers [9]. Additionally, studies of _C. sativa_ on the metabolic response of the plant under different degrees of stress [35], its potential uses in different industries [14; 16], and particularly nutraceuticalals [36; 37], such as evaluation of anti-malarial activity [38], observation of in vivo antioxidant effects [39], and pathogen resistance [30], among others, stand out. Meanwhile, in plant systems biology, genome-scale modeling has advanced considerably thanks to the reconstruction of _Arabidopsis thaliana_, _Zea mays_, _Oryza sativa_, and _Saccharum officinarum_, among others [40], which have proven accurate predictions focused on specific aspects of central carbon metabolism. For _Arabidopsis thaliana_, GEM modeling has evolved from the production of biomass components observed in experimental data to the inclusion of compartments (cytosol, plastid, mitochondrion, peroxisome, and vacuole), calculation of cell maintenance energy costs, description of photosynthetic processes, integration of secondary metabolism pathways, gene expression, proteomic data, and multi-tissue models [41]; as an example, [PERSON] et al. used the Plant SEED scheme to obtain reconstructions and subsequently integrate transcriptomics data extracted from different plant tissues [42]. Here we present a Genome-Scale Metabolic (GEM) reconstruction of _C. sativa_ with an analysis of non-targeted LC-QTOF-MS (Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry)-based metabolomics data and evaluation of cytotoxicity and anticancer activity of leaf extracts, which could help to pave the way for the development of alternative uses of the leaf with potential applications in the food, cosmetic, textile, and agrochemical industries and also to enhance exploration of anticancer, analgesic, and anti-inflammatory compounds [11]. To our knowledge, this is the first attempt to comprehensively describe the metabolic capacities of _C. sativa_ leaf (including both primary and secondary metabolism) based on a Genome-Scale Metabolic reconstruction; the contextualization of the reconstruction was carried out via LC-QTOF-MS to favor the identification of metabolites with known (anticancer, due to cannabinoids) and alternative properties (nutraceuticalals, due to flavonoids and amino acids) [43].