Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

Abstract

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) with a reduced parameter space that allows for an accurate and efficient calibration using a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model, we perform a time-dependent global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties due to limited temporal observational data. The cgABM reduces the computational time of ABM simulations by 93% to 97% while staying within a 3% difference in prediction compared to ABM. Additionally, the cgABM can reliably predict the temporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61±2.01 and 5.78±1.13, respectively.

Tumour growth: Bayesian parameter calibration of a multiphase porous media model based on in vitro observations of Neuroblastoma spheroid growth in a hydrogel microenvironment

“…This multidisciplinary study integrates various technologies, which combine ideally in advancing our understanding of how in vitro growth of tumour spheroids is regulated by the surrounding microenvironment. The methodology we present goes one step beyond those presented in the literature [29, 30].…”

“…Hawkins-Daarud et al [28] laid out a Bayesian framework for calibration, validation and uncertainty quantification of tumour-growth models considering synthetic data, and Collis et al [29] presented the Bayesian calibration of a simple Gompertzian tumour-growth model as a tutorial. More specifically, Lima et al [30] performed a Bayesian calibration of a stochastic, multiscale agent-based model based on 2D cell cultures of human breast carcinoma cells.…”

Adaptive time integration of mechanical forces in center-based models for biological cell populations

“…Since |g(2δr)| increases with the force function in (4) and the sums in (22) decreases when δr is reduced, the forces in the system are dominated by the force between the two cells involved in the proliferation. This dominance increases the smaller the initial distance 2δr is between the proliferating cells.…”

“…In general, the eigenvalues of largest modulus immediately after proliferation of A p are much larger than the modulus of the eigenvalues of A 0 , see (22). It follows from [12,Ch.…”

“…Off-lattice cell-based models are used in simulations to study e.g. tumours [21,22,25], morphogenesis [11], and colonies of bacteria [15]. The governing equations for the motion of the aggregation of cells constitute a system of ordinary differential equations (ODEs) derived from Newton's second law [1,4,21,27].…”