ArticleBioinformatics advances2022
GRASP: a computational platform for building kinetic models of cellular metabolism.
Article in Bioinformatics advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Combining automated kinetic model selection with robust optimization and application to maximize cell-free production of UDP-GalNAc.Metabolic engineering communications · 2026Article
- Kinetic sampling shows the effect of medium composition on metabolic control in Saccharomyces cerevisiae.Microbial cell factories · 2026Article
- Parameterization of cell-free systems with time-series data using KETCHUP.PLoS computational biology · 2025Article
- Complex Kinetic Models Predict β-Carotene Production and Reveal Flux Limitations in RecombinantACS synthetic biology · 2025Article
- Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism.ACS synthetic biology · 2024Review
- Shu: visualization of high-dimensional biological pathways.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Summary: Kinetic models of metabolism are crucial to understand the inner workings of cell metabolism. By taking into account enzyme regulation, detailed kinetic models can provide accurate predictions of metabolic fluxes. Comprehensive consideration of kinetic regulation requires highly parameterized non-linear models, which are challenging to build and fit using available modelling tools. Here, we present a computational package implementing the GRASP framework for building detailed kinetic models of cellular metabolism. By defining the mechanisms of enzyme regulation and a reference state described by reaction fluxes and their corresponding Gibbs free energy ranges, GRASP can efficiently sample an arbitrarily large population of thermodynamically feasible kinetic models. If additional experimental data are available (fluxes, enzyme and metabolite concentrations), these can be integrated to generate models that closely reproduce these observations using an approximate Bayesian computation fitting framework. Within the same framework, model selection tasks can be readily performed. Availability and implementation: GRASP is implemented as an open-source package in the MATLAB environment. The software runs in Windows, macOS and Linux, is documented (graspk.readthedocs.io) and unit-tested. GRASP is freely available at github.com/biosustain/GRASP. Supplementary information: Supplementary data are available at
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Registered trials
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