Multiscale computational models of cancer

Warner, Helen V.; Sivakumar, Nikita; Peirce, Shayn M.; Lazzara, Matthew J.

doi:10.1016/j.cobme.2019.11.002

Cited by 15 publications

(11 citation statements)

References 48 publications

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“…Yet this approach has investigated only the last steps of the process because the cancer cells were directly injected intraportally to target the mouse liver. Multiscale computational models were later employed to capture the whole invasion-metastasis cascade and provide insights into the mechanisms underpinning the metastatic process at the cellular scale [7,15]. However, these models rapidly grew in complexity, requiring significant amounts of computational power, with 10's to 100's of numerical parameters that can be difficult to interpret individually.…”

Section: Introductionmentioning

confidence: 99%

Is There One Key Step in the Metastatic Cascade?

Dujon

Capp

Brown

et al. 2021

Cancers

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The majority of cancer-related deaths are the result of metastases (i.e., dissemination and establishment of tumor cells at distant sites from the origin), which develop through a multi-step process classically termed the metastatic cascade. The respective contributions of each step to the metastatic process are well described but are also currently not completely understood. Is there, for example, a critical phase that disproportionately affects the probability of the development of metastases in individual patients? Here, we address this question using a modified Drake equation, initially formulated by the astrophysicist Frank Drake to estimate the probability of the emergence of intelligent civilizations in the Milky Way. Using simulations based on realistic parameter values obtained from the literature for breast cancer, we examine, under the linear progression hypothesis, the contribution of each component of the metastatic cascade. Simulations demonstrate that the most critical parameter governing the formation of clinical metastases is the survival duration of circulating tumor cells (CTCs).

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Section: Introductionmentioning

confidence: 99%

Is There One Key Step in the Metastatic Cascade?

Dujon

Capp

Brown

et al. 2021

Cancers

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“…Thus, ABMs capable of incorporating the signaling dynamics that lead to EMT and the resultant effects on differential cell-to-cell adhesion could be used to better understand the morphogenesis of primary tumors and to potentially understand the processes that lead tumors of different compositions to shed metastases (28,54). Our modeling framework would extend previous relevant computational models of how differential adhesion impacts tumor metastasis and cell migration (58).…”

Section: Discussionmentioning

confidence: 97%

“…The need to solve differential equations describing reaction kinetics in each cell within the ABM will greatly increase the computational power needed to simulate the system, as well as the number of model parameters. These are widespread issues encountered in multiscale, mechanistic biological models (41,65), and the tradeoffs must be carefully weighed in designing the structure of such models. Encouragingly, recent studies have presented novel methods for reducing the computational burden of complex mechanistic models by using neural networks and hybrid continuum-based modeling approaches (58,59,66,67).…”

Section: Discussionmentioning

confidence: 99%

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A computational modeling approach for predicting multicell patterns based on signaling-induced differential adhesion

Sivakumar

Warner

Peirce

et al. 2021

Preprint

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Dynamically activated differential adhesion within cell populations enables the emergence of unique patterns in heterogeneous multicellular systems. This process has previously been explored using synthetically engineered heterogenous multicell spheroid systems in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models may be leveraged to explore the key parameters that drive the emergence of different patterns more systematically. Here, we developed and validated two-and three-dimensional agent-based models (ABMs) of spheroid patterning for cells engineered with a bidirectional signaling circuit regulating N-and P-cadherin expression. The model was used to predict how varying initial cell seeding, cadherin induction probabilities, or homotypic adhesion strengths between cells leads to different spheroid patterns, and unsupervised machine learning techniques were used to map system parameters to unique spheroid patterns. The model was also used as a tool to design new synthetic cell signaling circuits based on a desired final multicell pattern.

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“…Besides, comprehensive reviews of hybrid models and multiscale models have been published in [ 32 , 33 ], respectively. Metzcar and coworkers [ 34 ], Warner et al [ 35 ], and Magi et al [ 36 ] briefly reviewed recent mathematical modeling of cancer biology, i.e., computational (cell-based and multiscale) models and modeling some hallmarks of cancer such as abnormalities in cell division and proliferation, resistance to cell death, angiogenesis, invasion and metastasis, evading immune destruction, and metabolic changes.…”

Section: Introductionmentioning

confidence: 99%

A New Mathematical Model for Controlling Tumor Growth Based on Microenvironment Acidity and Oxygen Concentration

Pourhasanzade

Sabzpoushan

2021

BioMed Research International

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Hypoxia and the pH level of the tumor microenvironment have a great impact on the treatment of tumors. Here, the tumor growth is controlled by regulating the oxygen concentration and the acidity of the tumor microenvironment by introducing a two-dimensional multiscale cellular automata model of avascular tumor growth. The spatiotemporal evolution of tumor growth and metabolic variations is modeled based on biological assumptions, physical structure, states of cells, and transition rules. Each cell is allocated to one of the following states: proliferating cancer, nonproliferating cancer, necrotic, and normal cells. According to the response of the microenvironmental conditions, each cell consumes/produces metabolic factors and updates its state based on some stochastic rules. The input parameters are compatible with cancer biology using experimental data. The effect of neighborhoods during mitosis and simulating spatial heterogeneity is studied by considering multicellular layer structure of tumor. A simple Darwinist mutation is considered by introducing a critical parameter ( Nmm ) that affects division probability of the proliferative tumor cells based on the microenvironmental conditions and cancer hallmarks. The results show that Nmm regulation has a significant influence on the dynamics of tumor growth, the growth fraction, necrotic fraction, and the concentration levels of the metabolic factors. The model not only is able to simulate the in vivo tumor growth quantitatively and qualitatively but also can simulate the concentration of metabolic factors, oxygen, and acidity graphically. The results show the spatial heterogeneity effects on the proliferation of cancer cells and the rest of the system. By increasing Nmm , tumor shrinkage and significant increasing in the oxygen concentration and the pH value of the tumor microenvironment are observed. The results demonstrate the model’s ability, providing an essential tool for simulating different tumor evolution scenarios of a patient and reliable prediction of spatiotemporal progression of tumors for utilizing in personalized therapy.

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Multiscale computational models of cancer

Cited by 15 publications

References 48 publications

Is There One Key Step in the Metastatic Cascade?

Is There One Key Step in the Metastatic Cascade?

A computational modeling approach for predicting multicell patterns based on signaling-induced differential adhesion

A New Mathematical Model for Controlling Tumor Growth Based on Microenvironment Acidity and Oxygen Concentration

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