Generalized mathematical model of cancer heterogeneity
Naito, M.
Show abstract
The number of reports on mathematical modeling related to oncology is increasing with advances in oncology. Even though the field of oncology has developed significantly over the years, oncology-related experiments remain limited in their ability to examine cancer. To overcome this limitation, in this study, a stochastic process was incorporated into conventional cancer growth properties to obtain a generalized mathematical model of cancer growth. Further, an expression for the violation of symmetry by cancer clones that leads to cancer heterogeneity was derived by solving a stochastic differential equation. Monte Carlo simulations of the solution to the derived equation validate the theories formulated in this study. These findings are expected to provide a deeper understanding of the mechanisms of cancer growth, with Monte Carlo simulation having the potential of being a useful tool for oncologists.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Dynamics of cell type transition mediated by epigenetic modifications 97%
- Bifurcation and sensitivity analysis reveal key drivers of multistability in a model of macrophage polarization. 96%
- Global analysis of a cancer model with drug resistance due to Lamarckian induction and microvesicle transfer 96%
Similar papers in this journal
- Analytical approach of synchronous and asynchronous update schemes applied to solving biological Boolean networks 96%
- Theory on the rate equations of Michaelis-Menten type enzyme kinetics with competitive inhibition 96%
- Criticality and partial synchronization analysis in Wilson-Cowan and Jansen-Rit neural mass models 96%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- A new paradigm considering multicellular adhesion, repulsion and attraction represent diverse cellular tile patterns 97%
- Role of neutrophil extracellular traps in regulation of lung cancer invasion and metastasis: Structural Insights from a Computational Model 96%
- The Correlation Between Cell and Nucleus Size is Explained by an Eukaryotic Cell Growth Model 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.