A Review of Cell-Based Computational Modeling in Cancer Biology

Abstract

Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer’s ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control.

Cell fate forecasting: a data assimilation approach to predict epithelial-mesenchymal transition

“…As an initial test, we only consider signaling occurring in a single cell and do not 496 consider spatial interactions occurring within a multicellular tissue during EMT. Model 497 development of spatial interactions during the EMT process is complex, and this 498 challenge is indeed an area of ongoing work within our lab and others [39][40][41]. As 499 described by Hunt and colleagues [16], the EnKF can be further extended to account for 500 spatial localization and interacting spatial dynamics, and we plan to extend the 501 June 7, 2019 19/25 approach demonstrated here to multicellular tissues in the future as well.…”

Learning-accelerated Discovery of Immune-Tumour Interactions

“…To date, most mathematical modelling of cancer-immune interactions have used non-spatial models (i.e., systems of differential equations), molecular-scale models of signaling dynamics, or lattice-based agent-based models that could not readily investigate the impact of mechanical interactions between tumour and immune cells. See Norton et al 13 for a review of agent-based simulation models of tumour immune microenvironments, and Metzcar et al 12 for a broader overview of cell-based computational modelling in cancer biology. In the work below, we focus on previously unexplored mechanical, spatial, and stochastic aspects of tumour-immune contact interactions.…”

“…Dynamical mathematical models can pierce the complexity of tumour-immune interactions and inform our therapeutic strategies [9][10][11] . Due to the dynamical nature of individual immune cells, the nuances of individual immune-immune and immune-cancer cell interactions, and tumour cell heterogeneity, it is advantageous to use agent-based models (ABMs) to mathematically model individual cancer and immune cells (each with individual positions, states, and immune characteristics), rather than simulate populations of cells with blurred positions and properties 12 .…”

Impact of force function formulations on the numerical simulation of centre-based models

“…An extensive review of cell-based models for general tissue mechanics can be found in [7]. Additionally, there are several reviews dealing with prominent applications areas, such as tumour growth [16,17] and morphogenetic problems [18,19,20]. In [21] the authors compare five cell-based frameworks (cellular automata, cellular potts, CBM OS and Voronoi variants and vertex models) with respect to four common biological problems: cell sorting, monoclonal conversion, lateral inihibition and morphogen-dependent proliferation.…”