The Implications of Small Stem Cell Niche Sizes and the Distribution of Fitness Effects of New Mutations in Aging and Tumorigenesis

Vincent L. Cannataro, Scott A. McKinley, Colette M. St. Mary

doi: http://dx.doi.org/10.1101/032813

Abstract:

Somatic tissue evolves over a vertebrate's lifetime due to the accumulation of mutations in stem cell populations. Mutations may alter cellular fitness and contribute to tumorigenesis or aging. The distribution of mutational effects within somatic cells is not known. Given the unique regulatory regime of somatic cell division we hypothesize that mutational effects in somatic tissue fall into a different framework than whole organisms; one in which there are more mutations of large effect. Through simulation analysis we investigate the fit of tumor incidence curves generated using exponential and power law Distributions of Fitness Effects (DFE) to known tumorigenesis incidence. Modeling considerations include the architecture of stem cell populations, i.e., a large number of very small populations, and mutations that do and do not fix neutrally in the stem cell niche. We find that the typically quantified DFE in whole organisms is sufficient to explain tumorigenesis incidence. Further, due to the effects of small stem cell population sizes, i.e., strong genetic drift, deleterious mutations are predicted to accumulate, resulting in reduced tissue maintenance. Thus, despite there being a large number of stem cells throughout the intestine, its compartmental architecture leads to significant aging, a prime example of Muller's Ratchet.

Link: http://biorxiv.org/content/early/2015/11/24/032813

Modeling Transitions between Responsive and Resistant States in Breast Cancer with Application to Therapy Optimization

Chun Chen, John J. Tyson, William Baumann

doi: http://dx.doi.org/10.1101/031245

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We present a mathematical model that captures the transitions among three experimentally observed estrogen sensitivity phenotypes in breast cancer cells. Based on this model, a population-level model is created and used to explore the optimization of a therapeutic protocol

http://biorxiv.org/content/early/2015/11/10/031245

Physics-based multiscale mass transport model in drug delivery and tumor microenvironment

Arturas Ziemys, Milos Kojic, Mauro Ferrari

doi: http://dx.doi.org/10.1101/031252

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We describe multiscale transport model, which was developed to simulate drug diffusion and convection in tissues and drug vectors. Models rely on material properties and physical laws of transport. Our methods show that drug transport analysis may provide deep insight into mechanisms of pharmacokinetics useful in nanotherapeutics and transport study within tumor microenvironment. Because the method relies on material properties and structures, the approach can help studying phenotypical differences as well.

http://biorxiv.org/content/early/2015/11/10/031252

Integrating experimental data to calibrate quantitative cancer models

Heiko Enderling

doi: http://dx.doi.org/10.1101/032102

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For quantitative cancer models to be meaningful and interpretable the number of unknown parameters must be kept minimal. Experimental data can be utilized to calibrate model dynamics rates or rate constants. Proper integration of experimental data, however, depends on the chosen theoretical framework. Using live imaging of cell proliferation as an example, we show how to derive cell cycle distributions in agent-based models and averaged proliferation rates in differential equation models. We focus on a tumor hierarchy of cancer stem and progenitor non-stem cancer cells.

http://biorxiv.org/content/early/2015/11/17/032102

Agent based models to investigate cooperation between cancer cells

Joao Xavier, William Chang

doi: http://dx.doi.org/10.1101/031070 

Abstract

We present a type of agent-based model that uses off-lattice spheres to represent individual cells in a solid tumor. The model calculates chemical gradients and determines the dynamics of the tumor as emergent properties of the interactions between the cells. As an example, we present an investigation of cooperation among cancer cells where cooperators secrete a growth factor that is costly to synthesize. Simulations reveal that cooperation is favored when cancer cells from the same lineage stay in close proximity. The result supports the hypothesis that kin selection, a theory that explains the evolution of cooperation in animals, also applies to cancers.