Investigation of cellular movement in the prostate epithelium using an agent-based model

Lao, Bert J.; Kamei, Daniel T.

doi:10.1016/j.jtbi.2007.11.001

Cited by 7 publications

(5 citation statements)

References 29 publications

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“…For directed movement of cell, Deisboeck and coworkers36,54 chose the best location for migration among the neighboring sites based on the amount of nutrients, levels of toxicity, and mechanical confinement in their tumor model . In their simulation of cell movement in the prostate epithelium, Lao and Kamei34 tested different movement behaviors of transit amplifying/intermediate cells and luminal cells in the prostate duct, and compared with experimental data.…”

Section: Cell Behaviormentioning

confidence: 99%

Rule-Based Simulation of Multi-Cellular Biological Systems—A Review of Modeling Techniques

et al. 2009

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Emergent behaviors of multi-cellular biological systems (MCBS) result from the behaviors of each individual cells and their interactions with other cells and with the environment. Modeling MCBS requires incorporating these complex interactions among the individual cells and the environment. Modeling approaches for MCBS can be grouped into two categories: continuum models and cell-based models. Continuum models usually take the form of partial differential equations, and the model equations provide insight into the relationship among the components in the system. Cell-based models simulate each individual cell behavior and interactions among them enabling the observation of the emergent system behavior. This review focuses on the cell-based models of MCBS, and especially, the technical aspect of the rule-based simulation method for MCBS is reviewed. How to implement the cell behaviors and the interactions with other cells and with the environment into the computational domain is discussed. The cell behaviors reviewed in this paper are division, migration, apoptosis/necrosis, and differentiation. The environmental factors such as extracellular matrix, chemicals, microvasculature, and forces are also discussed. Application examples of these cell behaviors and interactions are presented.

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Section: Cell Behaviormentioning

confidence: 99%

Rule-Based Simulation of Multi-Cellular Biological Systems—A Review of Modeling Techniques

et al. 2009

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“…Previous in silico models of prostate cancer have been focused on specific mechanisms such as the formation of bone metastases ( 62 ) or the role of disrupted stem cell movement in causing excessive growth in healthy prostatic ducts ( 82 ). To our knowledge, this is the first ABM to simulate the onset and development of prostate cancer in healthy prostatic acini considering the effects of the microenvironment including fibroblasts and macrophages.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model

Passier,

van Genderen,

Zaalberg

et al. 2023

Cancer Research Communications

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Over ten percent of men will be diagnosed with prostate cancer (PCa) during their lifetime. Arising from luminal cells of the prostatic acinus, PCa is influenced by multiple cells in its microenvironment. To expand our knowledge and explore means to prevent and treat the disease, it is important to understand what drives the onset and early stages of PCa. In this study, we developed an agent-based model of a prostatic acinus including its microenvironment, to allow for in silico studying of PCa development. The model was based on prior reports and in-house data of tumor cells co-cultured with Cancer Associated Fibroblasts (CAFs) and pro-tumor and/or anti-tumor macrophages. Growth patterns depicted by the model were pathologically validated on H&E slide images of human PCa specimens. We identified that stochasticity of interactions between macrophages and tumor cells at early stages strongly affect tumor development. Additionally, we discovered that more systematic deviations in tumor development result from a combinatorial effect of the probability of acquiring mutations and the tumor-promoting abilities of CAFs and macrophages. In silico modeled tumors were then compared with 494 cancer patients with matching characteristics, showing strong association between predicted tumor load and patients’ clinical outcome. Our findings suggest that the likelihood of tumor formation depends on a combination of stochastic events and systematic characteristics. While stochasticity cannot be controlled, information on systematic effects may aid the development of prevention strategies tailored to the molecular characteristics of an individual patient.

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“…Previous in silico models of PCa have been focused on specific mechanisms such as the formation of bone metastases (76) or the role of disrupted stem cell movement in causing excessive growth in healthy prostatic ducts (94). To our knowledge, this is the first ABM to simulate the onset and development of prostate cancer in healthy prostatic acini considering the effects of the microenvironment including fibroblasts and macrophages.…”

Section: Discussionmentioning

confidence: 99%

Exploring the onset and progression of prostate cancer through a multicellular agent-based model

Passier

Genderen

Zaalberg

et al. 2023

Preprint

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Over ten percent of men will be diagnosed with prostate cancer (PCa) during their lifetime. Arising from luminal cells of the prostatic acinus, PCa is influenced by multiple cells in its microenvironment. To expand our knowledge and explore means to prevent and treat the disease, it is important to understand what drives the onset and early stages of PCa. In this study, we developed an agent-based model of a prostatic acinus including its microenvironment, to allow for in silico studying of PCa development. The model was based on prior reports and in-house data of tumor cells co-cultured with Cancer Associated Fibroblasts (CAFs) and pro-tumor and/or anti-tumor macrophages. Growth patterns depicted by the model were pathologically validated on H&E slide images of human PCa specimens. We identified that stochasticity of interactions between macrophages and tumor cells at early stages strongly affect tumor development. Additionally, we discovered that more systematic deviations in tumor development result from a combinatorial effect of the probability of acquiring mutations and the tumor-promoting abilities of CAFs and macrophages. In silico modeled tumors were then compared with 494 cancer patients with matching characteristics, showing strong association between predicted tumor load and patients' clinical outcome. Our findings suggest that the likelihood of tumor formation depends on a combination of stochastic events and systematic characteristics. While stochasticity cannot be controlled, information on systematic effects may aid the development of prevention strategies tailored to the molecular characteristics of an individual patient.

show abstract

Investigation of cellular movement in the prostate epithelium using an agent-based model

Cited by 7 publications

References 29 publications

Rule-Based Simulation of Multi-Cellular Biological Systems—A Review of Modeling Techniques

Rule-Based Simulation of Multi-Cellular Biological Systems—A Review of Modeling Techniques

Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model

Exploring the onset and progression of prostate cancer through a multicellular agent-based model

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