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A hypercubic Mk model framework for capturing reversibility in disease, cancer, and evolutionary accumulation
Iain G Johnston1,2, Ramon Diaz-Uriarte3,4
1Department of Mathematics, University of Bergen, Realfagbygget, Bergen 5007, Norway.
Bioinformatics (Oxford, England)
|December 12, 2024
Summary
This study introduces a new model for tracking how biological systems gain and lose features over time, applicable to cancer and evolution. The model supports complex feature interactions and reversible changes, offering a more realistic approach to accumulation dynamics.
Area of Science:
- Evolutionary biology
- Cancer progression
- Systems biology
Background:
- Accumulation models are widely used to study biological systems that acquire binary features over time, such as mutations in cancer or evolutionary changes.
- Existing models often fail to account for feature reversibility, i.e., the loss of a feature after acquisition.
- This limitation hinders accurate modeling of complex biological dynamics where features can be gained and lost.
Purpose of the Study:
- To develop a novel framework for inferring feature accumulation dynamics that explicitly supports reversibility.
- To extend the established Mk model by embedding it within a hypercubic transition graph to handle reversible transitions.
- To accommodate complex interactions between features and various data types (cross-sectional, longitudinal, phylogenetic).
Main Methods:
- Utilized the Mk model from evolutionary biology, adapted to a hypercubic transition graph framework.
- Developed methods to infer accumulation dynamics from potentially uncertain data.
- Supported arbitrary sets of features, including positive and negative interactions, moving beyond pairwise limitations.
Main Results:
- Demonstrated the efficacy of the hypercubic Mk model for inferring reversible accumulation processes.
- Successfully applied the model to synthetic datasets and real-world data from bacterial drug resistance and cancer progression.
- Showcased the model's ability to handle complex feature interactions and diverse data types.
Conclusions:
- The hypercubic Mk model provides a robust framework for studying feature accumulation dynamics with reversibility.
- This approach offers a more realistic and comprehensive understanding of biological system evolution, particularly in cancer and evolutionary biology.
- While current implementation has feature number limitations, strategies for scaling to larger systems are discussed.
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