Modelling mitosis with multiple phenotypes: relation to Haeckel's recapitulation law
Abstract
The article presents a novel stochastic mathematical model of mitosis in heterogeneous (multiple-phenotype), age-dependent cell populations. The developed computational techniques involve flexible use of differentiation tree diagrams. The applicability of the model is discussed in the context of the Haeckelian (biogenetic) paradigm. In particular, the article puts forward the conjecture of generality of Haeckels recapitulation law. The conjecture is briefly collated against relevant scientific evidence and elaborated for the specific case of evolving/mutable cell phenotypes as considered by the model. The feasibility, basic regimes and the convenience of the model are tested on examples and experimental data, and the corresponding open source simulation software is described and demonstrated.
02 /Specification & Classification
Click any tag to browse matching papers- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- A general mathematical framework for understanding the behavior of heterogeneous stem cell regeneration
Jinzhi Lei·Journal of Theoretical BiologyPeer-reviewed·Feb 14, 2020
- A Multi-Stage Representation Of Cell Proliferation As A Markov Process
C. A. Yates, M. J. Ford, R. L. Mort·bioRxiv·May 26, 2017
- Stochastic Models for Population Dynamics
Sean X. Sun·bioRxiv·Dec 3, 2015
- A Physics-based Model Reveals Mechanisms of Epigenetic Memory
Erik J. Navarro, William Poole, … Simone Bianco·bioRxiv·Jan 22, 2026
- A multiscale model of epigenetic heterogeneity reveals the kinetic routes of pathological cell fate reprogramming
Núria Folguera-Blasco, Rubén Perez‐Carrasco, … Tomás Alarcón·PLoS Computational BiologyPeer-reviewed·Apr 30, 2019
- Cell Population Growth Kinetics in the Presence of Stochastic Heterogeneity of Cell Phenotype
Y. Wang, J. X. Zhou, … S. Huang·bioRxiv·Feb 8, 2023