Simulating Heterogeneous Tumor Cell Populations

Abstract

Certain tumor phenomena, like metabolic heterogeneity and local stable regions of chronic hypoxia, signify a tumor’s resistance to therapy. Although recent research has shed light on the intracellular mechanisms of cancer metabolic reprogramming, little is known about how tumors become metabolically heterogeneous or chronically hypoxic, namely the initial conditions and spatiotemporal dynamics that drive these cell population conditions. To study these aspects, we developed a minimal, spatially-resolved simulation framework for modeling tissue-scale mixed populations of cells based on diffusible particles the cells consume and release, the concentrations of which determine their behavior in arbitrarily complex ways, and on stochastic reproduction. We simulate cell populations that self-sort to facilitate metabolic symbiosis, that grow according to tumor-stroma signaling patterns, and that give rise to stable local regions of chronic hypoxia near blood vessels. We raise two novel questions in the context of these results: (1) How will two metabolically symbiotic cell subpopulations self-sort in the presence of glucose, oxygen, and lactate gradients? We observe a robust pattern of alternating striations. (2) What is the proper time scale to observe stable local regions of chronic hypoxia? We observe the stability is a function of the balance of three factors related to O2—diffusion rate, local vessel release rate, and viable and hypoxic tumor cell consumption rate. We anticipate our simulation framework will help researchers design better experiments and generate novel hypotheses to better understand dynamic, emergent whole-tumor behavior.

An Hybrid Model for Rectal Tumour Response Prediction during Radiotherapy

“…Most of the latest studies focus either on space-time aspects via reaction-diffusion equations [34] or on purely biological aspects at the cellular level [35]. These algorithms are generally compared with each other [36] or challenged against theoretical models [37] or empirical information [38], but they are rarely confronted with real data. In the present work, we proposed a method based on fully-described reaction-diffusion equations driven by a cellular-based stochastic approach [16].…”

Continuum descriptions of spatial spreading for heterogeneous cell populations: Theory and experiment

“…Therefore, it could be possible that there are multiple types of heterogeneity acting in even this very simple experiment. Previously, heterogeneity in cell populations has been introduced in both discrete and continuum models of cell motility (Simpson et al, 2014;Jin et al, 2016b;Sundstrom et al, 2016;Matsiaka et al, 2017). Previous work has also attempted to estimate parameters in heterogeneous models that describe glioblastoma progression (Rutter et al, 2018).…”

Modelling mitosis with multiple phenotypes: relation to Haeckel’s recapitulation law

“…Among stochastic techniques it is worth mentioning the version of the two-phenotype stochastic model of concurrent haematopoiesis [5] extended for the case of several phenotypes [6] and the multi-phenotype model of bone marrow featured by cellular automaton-style considerations [7], the latter being the closest to the presented approach. Another stochastic cellular automaton model by Sundstrom et al [8] studied tumorigenesis with multiple phenotypes by taking into account 2D and 3D spatial effects and limitations, but not cell differentiation. The work by Lu et al [9] analysed the mitosis in two-phenotype systems on the basis of the algorithm by Gillespie [10], also without cell differentiation.…”