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PyCycleBio: modelling non-sinusoidal-oscillator systems in temporal biology
Alexander R Bennett1, George Birchenough1,2, Daniel Bojar2,3
1Department of Medical Biochemistry, Institute of Biomedicine, University of Gothenburg, 41390 Gothenburg, Sweden.
Bioinformatics Advances
|February 16, 2026
Summary
PyCycleBio integrates harmonic oscillator and Cosinor models for robust biological rhythm analysis. This new platform enhances sensitivity for transcriptomics, proteomics, and metabolomics data, advancing chronobiology.
Area of Science:
- Chronobiology
- Bioinformatics
- Systems Biology
Background:
- Biological molecules like proteins, mRNA, and metabolites exhibit rhythmic variations, particularly influenced by the day-night cycle.
- Current bioinformatics tools for rhythm analysis use either single-component harmonic oscillator models for temporal dynamics or multi-component Cosinor models for complex patterns.
Purpose of the Study:
- To develop a novel bioinformatics platform, PyCycleBio, that integrates the strengths of harmonic oscillator and Cosinor models.
- To enhance the analysis of biological rhythms by modeling diverse rhythmic behaviors and temporal dynamics.
Main Methods:
- PyCycleBio employs bounded-multi-component models and modulus operators combined with the harmonic oscillator equation.
- The platform models rhythmic behaviors, including temporal dynamics regulation through amplitude coefficients.
Main Results:
- PyCycleBio demonstrates superior sensitivity and functionality compared to existing analytical frameworks.
- The platform reveals novel associations between data types (transcriptomics, proteomics, metabolomics), sampling conditions, and rhythmic characteristics.
- New insights into the temporal regulation of biomolecules were uncovered.
Conclusions:
- PyCycleBio offers an advanced approach for analyzing complex temporal regulation of biomolecules.
- This platform is expected to significantly advance the field of chronobiology and physiological understanding.
- The tool is accessible via GitHub, PyPI, and Google Colab for broad usability.
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