Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks

Pasquale Bove, Alessio Micheli, Paolo Milazzo, Marco Podda

Abstract

Biochemical pathways are often represented as graphs, in which nodes and edges give a qualitative description of the modeled reactions, while node and edge labels provide quantitative details such as kinetic and stoichiometric parameters. Dynamical properties of biochemical pathways are usually assessed by performing numerical (ODE-based) or stochastic simulations in which quantitative parameters are essential. These simulation methods are often computationally very expensive, in particular when property assessment requires varying parameters such as initial concentrations of molecules. In this paper we propose the use of a Deep Neural Network (DNN) to predict such dynamical properties relying only on the graph structure. In particular, our model is based on Graph Neural Networks. We focus on the dynamical property of concentration robustness, which is the ability of the pathway to maintain the concentration of some molecules within certain intervals despite of perturbation in the initial concentration of other molecules. The use of DNNs can allow robustness to be predicted by avoiding the burden of performing a huge number of numerical or stochastic simulations. Moreover, once trained, the model could be applied to predicting robustness properties for pathways in which quantitative parameters are not available.

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Paper Citation


in Harvard Style

Bove P., Micheli A., Milazzo P. and Podda M. (2020). Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, ISBN 978-989-758-398-8, pages 32-43. DOI: 10.5220/0008964700320043


in Bibtex Style

@conference{bioinformatics20,
author={Pasquale Bove and Alessio Micheli and Paolo Milazzo and Marco Podda},
title={Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,},
year={2020},
pages={32-43},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008964700320043},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,
TI - Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks
SN - 978-989-758-398-8
AU - Bove P.
AU - Micheli A.
AU - Milazzo P.
AU - Podda M.
PY - 2020
SP - 32
EP - 43
DO - 10.5220/0008964700320043