Agent-based methods facilitate integrative science in cancer

Abstract

In this opinion, we highlight agent-based modeling as a key tool for exploration of cell-cell and cell-environment interactions that drive cancer progression, therapeutic resistance, and metastasis. These biological phenomena are particularly suited to be captured at the cell-scale resolution possible only within agent-based or individual-based mathematical models. These modeling approaches complement experimental work (in vitro and in vivo systems) through parameterization and data extrapolation but also feed forward to drive new experiments that test model-generated predictions.

Keywords: agent-based mathematical models; cancer metabolism; immune–tumor interactions; integrative science; tissue homeostasis.

Figure 1.. Multi-scale agent-based model of melanoma:

Figure reproduced from ref. . (A) Cross section of human normal skin (hematoxylin and eosin stained), where the epidermis contains a basal layer, melanocytes, and keratinocytes, while the dermis contains fibroblasts and extracellular matrix. (B) The computational simulated cross section (bottom) closely mimics the overall qualitative features of normal skin homeostasis. (C) The process to design the multi-scale required iterative discussions within an integrative science team. Experimental biologists in collaboration with mathematicians drew a series of interaction diagrams, culminating in a final model (right) that can recapitulate both normal skin homeostasis and melanoma dynamics.

Figure 2:. Multi-scale model of normal human epidermis:

Figure panels are reproduced from ref. . A) Homeostatic epidermis model with high-resolution genomes. Rules flowchart shows how loss/replacement in the stem-cell niche at the basal layer are governed by a diffusible gradient of growth factor (GF). The model (bottom) investigates the effect of two fitness-enhancing mutations: NOTCH1 (middle) disrupts neutral dynamics through “blocking” neighboring cells from dividing into its local neighborhood while TP53 (right) are not subject to UV damage. B) Simulated dynamics matches patient biopsy clonal area frequency distributions. The inset shows log-10 transformed first incomplete moment for the same random sampling of patient comparable simulations. C) Difference between Komlogrov–Smirnov test statistic (Dm,n) and critical value (Dα) for all patient biopsies to patient-specific model simulation’s first incomplete moment distributions. Red arrow denotes comparisons where the null hypothesis can be rejected.

Figure 3:. Mapping immune-escape with multiscale modeling:

A) Spatial map of the hybrid discrete-continuum mathematical model of cancer metabolism from references and . Tumor cells colored by phenotype (normal, acid-resistant, Warburg) interact and compete for resources with normal cells (dark gray). Warburg emerges in areas of weak vascularization (bottom left). B) The tumor in panel A is color-coded to recent immune interactions, which correlates with local vascular density. C) The tumor in panel A color-coded to PD-L1 expression recently employed for immune escape.