ArticleBioinformatics (Oxford, England)2023
Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 18 citations in OpenAlex.
- Kinetic parameter prediction using neural networks identifies limitations to CThe New phytologist · 2026Article
- Generative approaches to kinetic parameter inference in metabolic networks via latent space exploration.Nature communications · 2026Article
- Parameterization of cell-free systems with time-series data using KETCHUP.PLoS computational biology · 2025Article
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- Jaxkineticmodel: Neural ordinary differential equations inspired parameterization of kinetic models.PLoS computational biology · 2025Article
- Mathematical complexities in radionuclide metabolic modelling: a review of ordinary differential equation kinetics solvers in biokinetic modelling.Journal of radiological protection : official journal of the Society for Radiological Protection · 2024Review
- Rational strain design with minimal phenotype perturbation.Nature communications · 2024Article
- Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering.ACS synthetic biology · 2023Article
- Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding.PLoS computational biology · 2023Article
- Structural Thermokinetic Modelling.Metabolites · 2022Article
- Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks.Nature machine intelligence · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
motivationLarge-scale kinetic models are an invaluable tool to understand the dynamic and adaptive responses of biological systems. The development and application of these models have been limited by the availability of computational tools to build and analyze large-scale models efficiently. The toolbox presented here provides the means to implement, parameterize and analyze large-scale kinetic models intuitively and efficiently.
resultsWe present a Python package (SKiMpy) bridging this gap by implementing an efficient kinetic modeling toolbox for the semiautomatic generation and analysis of large-scale kinetic models for various biological domains such as signaling, gene expression and metabolism. Furthermore, we demonstrate how this toolbox is used to parameterize kinetic models around a steady-state reference efficiently. Finally, we show how SKiMpy can implement multispecies bioreactor simulations to assess biotechnological processes. AVAILABILITY AND IMPLEMENTATION: The software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.