Multiscale model of the different modes of cancer cell invasion

Abstract

Motivation Mathematical models of biological processes altered in cancer are built using the knowledge of complex networks of signaling pathways, detailing the molecular regulations inside different cell types, such as tumor cells, immune and other stromal cells. If these models mainly focus on intracellular information, they often omit a description of the spatial organization among cells and their interactions, and with the tumoral microenvironment. Methods We present here a model of tumor cell invasion simulated with PhysiBoSS, a multiscale framework which combines agent-based modeling and continuous time Markov processes applied on Boolean network models. With this model, we aim to study the different modes of cell migration and to predict means to block it by considering not only spatial information obtained from the agent-based simulation but also intracellular regulation obtained from the Boolean model. Results Our multiscale model integrates the impact of gene mutations with the perturbation of the environmental conditions and allows the visualization of the results with 2D and 3D representations. The model successfully reproduces single and collective migration processes and is validated on published experiments on cell invasion. In silico experiments are suggested to search for possible targets that can block the more invasive tumoral phenotypes. Supplementary information https://github.com/sysbio-curie/Invasion_model_PhysiBoSS

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

“… Criterion Quantities Reference(s) Accuracy MSE, RMSE, F-score, ROC curve 28 , 95 Complexity # of model parameters, # of agents, connectivity density of ontology graph, Bayesian Information Criterion 119–122 Explanatory power black box vs . white box model, accordance with experimental data across scales 7 , 13 , 17 , 86 Model robustness/sensitivity coefficient of variation, sensitivity indices 28 , 66 , 124 Computational efficacy lines of code, run-time, memory usage, energy demand 125–128 …”

“…They probed for the effect of the spatial distribution of cancer cells on the treatment parameters optimizing the supply strategies in cell monolayers and three-dimensional tumor spheroids; similarly, they interrogated the robustness of the effective treatments with respect to the cell population heterogeneity of the cancer cells. Following the modeling work in, 85 Ruscone et al 86 proposed an enhanced multi-scale model to interrogate possible targets that can help block or suppress the invasive phenotypes of cancer cells. More specifically, the improvements are focused at the intracellular scale where they incorporated mechanisms of epithelialto-mesenchymal transition and cell metastasis.…”

“…The larger the range of spatial and temporal scales that an agent-based model allows to capture and reproduce, the higher its explanatory power. Along those lines, Wijeratne and Vavourakis 7 studied the impact of drug-borne nanoparticles on multiple levels including the fluid dynamics, biochemics, vasculature, and overall solid tumor, while Ruscone et al 86 also proposed an agent-based model and probe dynamics on the scales of genetics, metabolism, cell behavior and population.…”

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

“…PhysiBoSS allows bridging microenvironment signals, such as drugs, their effect on signalling pathways and the resulting population-level phenotypes 23 , 26 – 28 . The introduction of this hybrid simulation framework was an important step toward the mechanistic multi-scale description of complex biological systems such as healthy tissues and tumours 9 .…”

A simple framework for agent-based modeling with extracellular matrix

“…Goncalves and Garcia-Aznar represent the ECM as a density and use experimental migration data to explore tumor spheroids [35]. Lastly, Ruscone et al study cancer invasion using PhysiBoSS [36, 37], an implementation of PhyisCell [38] with the MaBoSS Boolean network simulator [39], with a model incorporating intracellular signaling activated by contact with a bulk ECM [40].…”