Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN

Abstract

Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells.

Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry

“…Moreover, more abstract models sometimes permit identifying components (species or mechanisms) whose kinetics contribute little to the behavior of a model or that can be lumped with other species to simplify its computational representation (Rao et al, 2014). This can be particularly useful for very large systems with welldefined constraints, such as metabolic network models (Masid et al, 2020). With decreasing complexity of a model, it also becomes easier to perform robust parameter estimations and to determine how well the model is justified based on the available data (Raue et al, 2009).…”

Thermodynamic genome-scale metabolic modeling of metallodrug resistance in colorectal cancer

“…The pyTFA package [46], https://github.com/EPF L-LCSB/pytfa, formulates thermodynamic flux analysis (TFA) of GSMM as a mixed-integer linear programming problem that incorporates metabolite concentrations as thermodynamic constraints into a traditional flux balance analysis (FBA) model. Masid et al [48] have recently constructed an extensive thermodynamic database containing the thermodynamic information for compounds, reactions and compartments in human metabolism; this includes the Gibbs free energy formation of compounds and the associated error estimation, the pH, ionic strength and membrane potentials. Using Biopython (Version 1.78) we annotated the GSMM with SEED identifiers which allowed us to match the information in the GSMM to the thermodynamic database of Masid et al [48].…”

“…Thermodynamic flux analysis (TFA) imposes additional constraints on stoichiometric models to ensure thermodynamically valid fluxes and provides a framework for integrating metabolomics data into GSMMs [45,46]; extracellular metabolite data are used to constraint the directionality of exchange reactions of the model and intracellular metabolite data can be used to constraint reactions in the model. Both intra-and extracellular metabolite data have previously been integrated into system-specific metabolic models to draw physiological conclusions about cancerous and healthy cells [47,48,49,50]. In this work, we integrate experimentally determined absolute concentrations of intracellular metabolites and medium-based metabolites and growth rates of the colorectal cancer cell-line HCT116 into a cellline specific, thermodynamic, genome-scale metabolic model (GSMM).…”

Modular dynamic biomolecular modelling with bond graphs: the unification of stoichiometry, thermodynamics, kinetics and data

“…This can lead to mass flows that are not thermodynamically possible because they violate the second law of thermodynamics. Such non-physical flows can be detected and eliminated by adding additional thermodynamic constraints, as in thermodynamics-based metabolic flux analysis (TFA) [14,15], energy balance analysis (EBA) and expression, thermodynamics-enabled flux models (ETFL) [16–20] and loopless FBA [21]. Whereas constraint-based models provide metabolic fluxes, they generally do not explicitly account for metabolite concentrations, or how fluxes vary over time, both of which are required for dynamic whole-cell modelling.…”