SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth - Jain, Harsh Vardhan; Norton, Kerri-Ann; Prado, Bernardo Bianco; Jackson, Trachette L. (Frontiers in Molecular Biosciences, 2022)
Multiscale systems biology is having an increasingly powerful impact on our understanding of the interconnected molecular, cellular, and microenvironmental drivers of tumor growth and the effects of novel drugs and drug combinations for cancer therapy. Agent-based models (ABMs) that treat cells as autonomous decision-makers, each with their own intrinsic characteristics, are a natural platform for capturing intratumoral heterogeneity. ABMs are also useful for integrating the multiple time and spatial scales associated with vascular tumor growth and response to treatment. Despite all their benefits, the computational costs of solving ABMs escalate and become prohibitive when simulating millions of cells, making parameter exploration and model parameterization from experimental data very challenging. Moreover, such data are typically limited, coarse-grained and may lack any spatial resolution, compounding these challenges. We address these issues by developing a first-of-its-kind method that leverages explicitly formulated surrogate models (SMs) to bridge the current computational divide between ABMs and experimental data. In our approach, Surrogate Modeling for Reconstructing Parameter Surfaces (SMoRe ParS), we quantify the uncertainty in the relationship between ABM inputs and SM parameters, and between SM parameters and experimental data. In this way, SM parameters serve as intermediaries between ABM input and data, making it possible to use them for calibration and uncertainty quantification of ABM parameters that map directly onto an experimental data set. We illustrate the functionality and novelty of SMoRe ParS by applying it to an ABM of 3D vascular tumor growth, and experimental data in the form of tumor volume time-courses. Our method is broadly applicable to situations where preserving underlying mechanistic information is o interest, and where computational complexity and sparse, noisy calibration data hinder model parameterization.