Regular ArticleConstraints-based models: Regulation of Gene Expression Reduces the Steady-state Solution Space
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Cited by (137)
Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline
2022, STAR ProtocolsCitation Excerpt :For more accurate and robust prediction of target metabolic behavior under different conditions or contexts (e.g., for mutant strains due to gene deletions in metabolic engineering), these metabolic network models can also be integrated with a set of genetic regulatory rules, which can be modeled as a transcriptional regulatory network involving transcription factors (TFs) that may regulate metabolic reactions. Transcriptional regulation is often integrated via “transcriptional regulatory constraints” with various heuristics in metabolic network models for flux predictions (Covert and Palsson, 2003; Shlomi et al., 2007; Covert et al., 2008). With the advent of high-throughput profiling technologies, genome-scale gene expression profiles can be easily obtained to help infer genetic regulatory rules.
TRIMER: Transcription Regulation Integrated with Metabolic Regulation
2021, iScienceCitation Excerpt :For more accurate and robust prediction of target metabolic behavior under different conditions or contexts, not only metabolic reactions but also the integration of genetic regulatory relationships involving transcription factors (TFs) that may regulate metabolic reactions, should be appropriately modeled. Due to the increasing computational complexity when considering multiple types of biomolecules in one computational system model, often transcription regulation has been integrated via “transcriptional regulatory constraints” with various heuristics for flux-balance analysis (FBA) of metabolic networks (Covert and Palsson, 2003; Shlomi et al., 2007; Covert et al., 2008; Shlomi et al., 2008; Fendt et al., 2010; Machado and Herrgård, 2014; Reiss et al., 2015; Reed, 2017; Motamedian et al., 2017; Yu and Blair, 2019). Many of these computational tools were often only validated for selected model organisms with curated data and network models.
Metabolomics and flux balance analysis
2021, Bioinformatics: Methods and ApplicationsRecent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms
2021, Metabolic EngineeringCitation Excerpt :SGL is then used to select a sparse set of genes that is the best predictor of a given phenotype. Phenotype prediction can be further enhanced by adding Boolean type constraints to GSM models to help incorporate regulation (Covert and Palsson, 2003) (e.g., under aerobic or anaerobic conditions), as indicated in Fig. 1(b). However, reconstructing a gene regulatory network (GRN) from high-throughput data remains challenging, as elucidated by the DREAM project for over 30 network inferences methods applied to E. coli, Saccharomyces cerevisiae, and Staphylococcus aureus (Marbach et al., 2012).
Computational tools in the assistance of personalized healthcare
2018, Computer Aided Chemical EngineeringCitation Excerpt :Such models can be transformed into stoichiometric representations, where the matrices represent the biological constraints associated with the studied system (e.g., genetic and physicochemical properties), while the solution space is defined by data resulting from gene expression and proteomic studies (Blazier and Papin, 2012). Constrained-based models are usually less computationally expensive compared to kinetic models and they are characterized by a decreased number of parameters (Covert and Palsson, 2003). In addition, there is a variety of software tools that facilitate the design of the metabolic network of interest (Lewis and Abdel-Haleem, 2013; Marinković and Orešič, 2016).
Advances in the integration of transcriptional regulatory information into genome-scale metabolic models
2016, BioSystemsCitation Excerpt :FBA predicts the fluxes of reactions and the biomass production rate without accounting for the regulatory constraints that are crucial in determining the presence and activity of enzymes in an environmental condition (Orth et al., 2010). This omission of regulatory constraints is an important limitation of FBA (Covert and Palsson, 2002, 2003; Åkesson et al., 2004) and can partly explain incorrect predictions by this method on gene essentiality (Covert et al., 2004) and gene interactions (Szappanos et al., 2011). The metabolic state of a cell in a given condition is governed by the expression of metabolic genes encoding enzymes.
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Corresponding author. Tel.: +1-858-534-5668; fax: +1-858-822-3120.
E-mail address: [email protected] (B.O. Palsson).