Structural conserved moiety splitting of a stoichiometric matrix

Ghaderi, Susan; Haraldsdóttir, Hulda S.; Ahookhosh, Masoud; Arreckx, Sylvain; Fleming, Ronan M. T.

doi:10.1016/j.jtbi.2020.110276

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…The PlantSEED semi-automatic reconstruction strategy was performed and curated with an exhaustive review of the literature and BLASTKOALA, to obtain the first C. sativa GEM reported in the literature ( Figure 1 ). Results were analyzed considering the challenges involved in modeling eukaryotic cells (large size, compartmentalization of metabolic processes, and variation in tissue-specific metabolic activity [ 61 ]) and also by considering topological characteristics of the network that can be analyzed from the stoichiometric matrix [ 62 ]. Features of the initially reconstructed network and topological analysis of the stoichiometric matrix through the sparsity pattern are shown in Table 1 and Table 2 and Figure 2 .…”

Section: Resultsmentioning

confidence: 99%

“…This value is particularly useful for comparing models based on the number of metabolites involved in each reaction. The double upper diagonal appearance observed in the stoichiometric matrix is primarily a result of the ordering of reactions, rather than an intrinsic feature [ 62 , 65 ]. GEM reconstruction of C. sativa incorporates various compartments, including the cytosol, stroma, Golgi, vacuole, cell wall, peroxisome, mitochondria, nucleus, and endoplasmic reticulum ( Supplementary Figure S1 ).…”

Section: Discussionmentioning

confidence: 99%

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Genome-Scale Metabolic Reconstruction, Non-Targeted LC-QTOF-MS Based Metabolomics Data, and Evaluation of Anticancer Activity of Cannabis sativa Leaf Extracts

Camargo,

Santamaria-Torres,

Cala

et al. 2023

Metabolites

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Over the past decades, Colombia has suffered complex social problems related to illicit crops, including forced displacement, violence, and environmental damage, among other consequences for vulnerable populations. Considerable effort has been made in the regulation of illicit crops, predominantly Cannabis sativa, leading to advances such as the legalization of medical cannabis and its derivatives, the improvement of crops, and leaving an open window to the development of scientific knowledge to explore alternative uses. It is estimated that C. sativa can produce approximately 750 specialized secondary metabolites. Some of the most relevant due to their anticancer properties, besides cannabinoids, are monoterpenes, sesquiterpenoids, triterpenoids, essential oils, flavonoids, and phenolic compounds. However, despite the increase in scientific research on the subject, it is necessary to study the primary and secondary metabolism of the plant and to identify key pathways that explore its great metabolic potential. For this purpose, a genome-scale metabolic reconstruction of C. sativa is described and contextualized using LC-QTOF-MS metabolic data obtained from the leaf extract from plants grown in the region of Pesca-Boyaca, Colombia under greenhouse conditions at the Clever Leaves facility. A compartmentalized model with 2101 reactions and 1314 metabolites highlights pathways associated with fatty acid biosynthesis, steroids, and amino acids, along with the metabolism of purine, pyrimidine, glucose, starch, and sucrose. Key metabolites were identified through metabolomic data, such as neurine, cannabisativine, cannflavin A, palmitoleic acid, cannabinoids, geranylhydroquinone, and steroids. They were analyzed and integrated into the reconstruction, and their potential applications are discussed. Cytotoxicity assays revealed high anticancer activity against gastric adenocarcinoma (AGS), melanoma cells (A375), and lung carcinoma cells (A549), combined with negligible impact against healthy human skin cells.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Genome-Scale Metabolic Reconstruction, Non-Targeted LC-QTOF-MS Based Metabolomics Data, and Evaluation of Anticancer Activity of Cannabis sativa Leaf Extracts

Camargo,

Santamaria-Torres,

Cala

et al. 2023

Metabolites

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“…For example, Elementary Flux Modes (Klamt et al, 2017) and Extreme Pathways (Wagner and Urbanczik, 2005) are two complementary techniques for decomposing the solution space into simpler units (Zanghellini et al, 2013;Muller and Bockmayr, 2014). Other methods for exploring the solution space include random flux sampling with Monte Carlo methods (Wiback et al, 2004), the use of dimensionality reduction techniques (Bhadra et al, 2018), and various structural decompositions of the stoichiometric matrix (Ghaderi et al, 2020).…”

Section: Flux Balance Analysismentioning

confidence: 99%

Opportunities at the Interface of Network Science and Metabolic Modeling

Dusad

Thiel

Barahona

et al. 2021

Front. Bioeng. Biotechnol.

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Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.

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“…The potential of hypergraphs to describe cellular networks has been hypothesized in a perspective in 2009 [30]. Mapping into a hypergraph was also noted in [31][32][33][34], bringing attention to this new representation. Recently, Mulas et al [35,36] applied hypergraphs to chemical networks, trying to capture the high-order nature of chemical reactions.…”

Section: Introductionmentioning

confidence: 99%

Robustness and Complexity of Directed and Weighted Metabolic Hypergraphs

Traversa,

Ferraz de Arruda,

Vazquez

et al. 2023

Entropy

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Metabolic networks are probably among the most challenging and important biological networks. Their study provides insight into how biological pathways work and how robust a specific organism is against an environment or therapy. Here, we propose a directed hypergraph with edge-dependent vertex weight as a novel framework to represent metabolic networks. This hypergraph-based representation captures higher-order interactions among metabolites and reactions, as well as the directionalities of reactions and stoichiometric weights, preserving all essential information. Within this framework, we propose the communicability and the search information as metrics to quantify the robustness and complexity of directed hypergraphs. We explore the implications of network directionality on these measures and illustrate a practical example by applying them to a small-scale E. coli core model. Additionally, we compare the robustness and the complexity of 30 different models of metabolism, connecting structural and biological properties. Our findings show that antibiotic resistance is associated with high structural robustness, while the complexity can distinguish between eukaryotic and prokaryotic organisms.

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Structural conserved moiety splitting of a stoichiometric matrix

Cited by 6 publications

References 20 publications

Genome-Scale Metabolic Reconstruction, Non-Targeted LC-QTOF-MS Based Metabolomics Data, and Evaluation of Anticancer Activity of Cannabis sativa Leaf Extracts

Genome-Scale Metabolic Reconstruction, Non-Targeted LC-QTOF-MS Based Metabolomics Data, and Evaluation of Anticancer Activity of Cannabis sativa Leaf Extracts

Opportunities at the Interface of Network Science and Metabolic Modeling

Robustness and Complexity of Directed and Weighted Metabolic Hypergraphs

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