Overview
Biological development unfolds as a stochastic process characterized by continuous variation and discrete transitions, yet traditional analytical methods fail to capture this complexity, and we present EvoJump, a unified computational framework that models developmental trajectories as stochastic processes analyzed through cross-sectional laser plane views of phenotypic distributions. EvoJump integrates multiple stochastic process models including jump-diffusion, fractional Brownian motion, Cox-Ingersoll-Ross, and Lévy processes with advanced statistical methods including wavelet analysis, copula modeling, extreme value theory, and regime-switching detection, enabling analysis of developmental trajectories and evolutionary constraints, prediction of phenotypic outcomes with uncertainty quantification, and identification of developmental phase transitions and dependencies. Implemented in Python with comprehensive testing framework and extensive documentation, EvoJump bridges quantitative genetics and modern computational methods, enabling researchers to address fundamental questions about the mechanistic basis of phenotypic evolution across ontogeny, and the framework demonstrates robust performance with synthetic data validation and scales efficiently to large phenotyping datasets. All methods and the material for generating the paper are available in https://github.com/docxology/EvoJump
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Methods and contributions
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Findings and contributions
- Validation tests confirmed expected properties, e.g. fBM recovered standard Brownian motion at H = 0.5 and distinguished persistence regimes.
- The author reports that all tests in the testing framework pass.
- On synthetic data, copula analysis showed significant positive dependence between early and late developmental phenotypes (Kendall's τ = 0.45).
- The Drosophila simulation captured a near-fixation selective sweep, correlated eye-size evolution, hitchhiking, and selection-drift balance.
- Stated limitations include time-homogeneous parameters, separate analysis of traits, and treating observations as exact without measurement error.
Methods
- Cross-sectional 'laser plane' view of ontogeny as a stochastic process — Development is conceptualised as stochastic trajectories examined through phenotype distributions at successive timepoints.
- Python package unifying OU-jump, fBM, Cox-Ingersoll-Ross and Lévy process models — EvoJump implements several stochastic process models for developmental trajectories behind consistent interfaces.
- Wavelet, copula, extreme value and regime-switching analyses of trajectories — Statistical modules apply wavelets for multi-scale patterns, copulas for dependence, EVT for rare events and regime-switching for phase detection.
- Validation via synthetic data, analytical solutions and integration tests — Each process model and statistical method is checked with synthetic data of known parameters, analytical solutions where available, and end-to-end tests.
- Simulated 100-generation Drosophila selective sweep case study — A logistic-selection SDE with drift models a red-eye allele sweep and a correlated eye-size trait, based on a published classroom study.
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