Neural Ordinary Differential Equations Inspired Parameterization of Kinetic Models

Abstract

MotivationMetabolic kinetic models are widely used to model biological systems. Despite their widespread use, it remains challenging to parameterize these Ordinary Differential Equations (ODE) for large scale kinetic models. Recent work on neural ODEs has shown the potential for modeling time-series data using neural networks, and many methodological developments in this field can similarly be applied to kinetic models.ResultsWe have implemented a simulation and training framework for Systems Biology Markup Language (SBML) models using JAX/Diffrax, which we namedjaxkineticmodel. JAX allows for automatic differentiation and just-in-time compilation capabilities to speed up the parameterization of kinetic models. We show the robust capabilities of training kinetic models using this framework on a large collection of SBML models with different degrees of prior information on parameter initialization. Finally, we showcase the training framework implementation on a complex model of glycolysis. These results show that our framework can be used to fit large metabolic kinetic models efficiently and provides a strong platform for modeling biological systems.ImplementationImplementation ofjaxkineticmodelis available as a Python package athttps://github.com/AbeelLab/jaxkineticmodel.Author summaryUnderstanding how metabolism works from a systems perspective is important for many biotechnological applications. Metabolic kinetic models help in achieving understanding, but there construction and parametrization has proven to be complex, especially for larger metabolic networks. Recent success in the field of neural ordinary differential equations in combination with other mathematical/computational techniques may help in tackling this issue for training kinetic models. We have implemented a Python package namedjaxkineticmodelthat can be used to build, simulate and train kinetic models, as well as compatibility with the Systems Biology Markup Language. This framework allows for efficient training of kinetic models on time-series concentration data using a neural ordinary differential equation inspired approach. We show the convergence properties on a large collection of SBML models, as well as experimental data. This shows a robust training process for models with hundreds of parameters, indicating that it can be used for large-scale kinetic model training.