Mini-batch optimization enables training of ODE models on large-scale datasets

Paul Stapor, Leonard Schmiester, Christoph Wierling, Simon Merkt, Dilan Pathirana, Bodo M. H. Lange, Daniel Weindl and Jan Hasenauer ()
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Paul Stapor: Institute of Computational Biology
Leonard Schmiester: Institute of Computational Biology
Christoph Wierling: Alacris Theranostics GmbH
Simon Merkt: Universität Bonn, Faculty of Mathematics and Natural Sciences
Dilan Pathirana: Universität Bonn, Faculty of Mathematics and Natural Sciences
Bodo M. H. Lange: Alacris Theranostics GmbH
Daniel Weindl: Institute of Computational Biology
Jan Hasenauer: Institute of Computational Biology

Nature Communications, 2022, vol. 13, issue 1, 1-17

Abstract: Abstract Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.

Date: 2022
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DOI: 10.1038/s41467-021-27374-6

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