MxlPy—Python package for mechanistic learning and hybrid modelling in life science

Bioinformatics Advances, vol. 5, pp. vbaf294

Abstract

Summary: Recent advances in artificial intelligence have accelerated the adoption of machine learning (ML) in biology, enabling powerful predictive models across diverse applications. However, in scientific research, the need for interpretability and mechanistic insight remains crucial. To address this, we introduce MxlPy, a Python package that combines mechanistic modelling with ML to deliver explainable, data-informed solutions. MxlPy facilitates mechanistic learning, an emerging approach that integrates the transparency of mathematical models with the flexibility of data-driven methods. By streamlining tasks such as data integration, model formulation, output analysis, and surrogate modelling, MxlPy enhances the modelling experience without sacrificing interpretability. Designed for both computational biologists and interdisciplinary researchers, it supports the development of accurate, efficient, and explainable models, making it a valuable tool for advancing bioinformatics, systems biology, and biomedical research. Availability and implementation: MxlPy source code is freely available at https://github.com/Computational-Biology-Aachen/MxlPy. The full documentation with features and examples can be found here https://computational-biology-aachen.github.io/MxlPy.

Authors 4

1. Marvin van Aalst Aachen Department of Biology, Computational Life Science Department of Biology

   RWTH Aachen University

   Affiliation as printed

   Department of Biology, Computational Life Science, RWTH Aachen University , Aachen 52074,

   RWTH Aachen University , Department of Biology, , 52074, Aachen,

2. Tim Nies Aachen Department of Biology, Computational Life Science Department of Biology

   RWTH Aachen University

   Affiliation as printed

   Department of Biology, Computational Life Science, RWTH Aachen University , Aachen 52074,

   RWTH Aachen University , Department of Biology, , 52074, Aachen,

3. Tobias Pfennig Aachen Department of Biology, Computational Life Science Computational Life Science Department of Biology

   Cluster of Excellence on Plant Sciences · RWTH Aachen University

   Affiliation as printed

   Cluster of Excellence on Plant Sciences (CEPLAS) , Düsseldorf 40225,

   Department of Biology, Computational Life Science, RWTH Aachen University , Aachen 52074,

   CEPLAS Cluster of Excellence on Plant Sciences, , Düsseldorf, 40225,

   Computational Life Science, Department of Biology, RWTH Aachen University, 52074, Aachen, Germany

4. Anna Matuszyńska corresponding Aachen Center for Computational Life Sciences Department of Biology, Computational Life Science Computational Life Science Department of Biology

   Cluster of Excellence on Plant Sciences · RWTH Aachen University

   Affiliation as printed

   Center for Computational Life Sciences , Aachen 52074,

   Cluster of Excellence on Plant Sciences (CEPLAS) , Düsseldorf 40225,

   Department of Biology, Computational Life Science, RWTH Aachen University , Aachen 52074,

   CEPLAS Cluster of Excellence on Plant Sciences, , Düsseldorf, 40225,

   Center for Computational Life Sciences , Aachen, 52074,

   Computational Life Science, Department of Biology, RWTH Aachen University, 52074, Aachen, Germany