A global method for fast simulations of molecular dynamics in multiscale agent-based models of biological tissues

Bergman, Daniel; Sweis, Randy F.; Pearson, Alexander T.; Nazari, Fereshteh; Jackson, Trachette L.

doi:10.1016/j.isci.2022.104387

Cited by 6 publications

(8 citation statements)

References 63 publications

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“…Targeting the specific task of parameter estimation, [ 7 ] laid out an efficient means for performing Bayesian inference on an ABM. In [ 8 ], we developed and analyzed what we refer to as the global method for handling molecular dynamics in an ABM, and this method resulted in orders of magnitude speedup. This method is best suited for ABMs that require simulating reactions at the cell surface and/or intracellular signaling pathways.…”

Section: Introductionmentioning

confidence: 99%

“…The global method reduces the computational time compared to the local method by averaging the molecular state variables across cells in a region of the microenvironment before solving the governing differential equations and applying the result to those cells. In [ 8 ], we explored two model systems in which these molecular dynamics resulted in continuous changes to cellular dynamics by modulating proliferation, apoptosis, and symmetric division rates. Among the many questions this left open regarding the applicability of the global method, a critical one was how it performed when the molecular level caused discrete changes at the cellular level.…”

Section: Introductionmentioning

confidence: 99%

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Phenotype switching in a global method for agent-based models of biological tissue

Bergman

Jackson

2023

PLoS ONE

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Agent-based models (ABMs) are an increasingly important tool for understanding the complexities presented by phenotypic and spatial heterogeneity in biological tissue. The resolution a modeler can achieve in these regards is unrivaled by other approaches. However, this comes at a steep computational cost limiting either the scale of such models or the ability to explore, parameterize, analyze, and apply them. When the models involve molecular-level dynamics, especially cell-specific dynamics, the limitations are compounded. We have developed a global method for solving these computationally expensive dynamics significantly decreases the computational time without altering the behavior of the system. Here, we extend this method to the case where cells can switch phenotypes in response to signals in the microenvironment. We find that the global method in this context preserves the temporal population dynamics and the spatial arrangements of the cells while requiring markedly less simulation time. We thus add a tool for efficiently simulating ABMs that captures key facets of the molecular and cellular dynamics in heterogeneous tissue.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Phenotype switching in a global method for agent-based models of biological tissue

Bergman

Jackson

2023

PLoS ONE

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“…An alternative approach is a discretized method that models cells as autonomous, decision making "agents," each with their own set of properties and behaviors. These agent-based models (ABMs) have become a valuable tool in translational systems oncology, which has goals of predicting the effects of novel drugs and drug combinations on difficultto-treat tumors (Altrock et al, 2015;Wang et al, 2015;Bergman et al, 2022). ABMs provide a logical structure for capturing the multiple time and spatial scales associated with cancer growth and progression because they allow for the characterization of tumor heterogeneity at an individual cell level that better reflects the complexity seen in vivo (Bergman et al, 2022).…”

Section: Open Access 1 Introductionmentioning

confidence: 99%

“…These agent-based models (ABMs) have become a valuable tool in translational systems oncology, which has goals of predicting the effects of novel drugs and drug combinations on difficultto-treat tumors (Altrock et al, 2015;Wang et al, 2015;Bergman et al, 2022). ABMs provide a logical structure for capturing the multiple time and spatial scales associated with cancer growth and progression because they allow for the characterization of tumor heterogeneity at an individual cell level that better reflects the complexity seen in vivo (Bergman et al, 2022). One major advantage of ABMs over traditional continuum ODE/PDE models is that they can generate realistic 3-dimensional virtual tumors that current state-ofart imaging technologies cannot infer from patient scans [for a discussion on limitations of imaging in cancer, see for instance (Bogdanovic et al, 2021;Ding et al, 2021;Martinez-Heras et al, 2021)].…”

Section: Open Access 1 Introductionmentioning

confidence: 99%

SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth

Jain

Norton

Prado

et al. 2022

Front. Mol. Biosci.

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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. Agent-based models 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 agent-based models 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 agent-based models and experimental data. In our approach, Surrogate Modeling for Reconstructing Parameter Surfaces (SMoRe ParS), we quantify the uncertainty in the relationship between agent-based model inputs and surrogate model parameters, and between surrogate model parameters and experimental data. In this way, surrogate model parameters serve as intermediaries between agent-based model input and data, making it possible to use them for calibration and uncertainty quantification of agent-based model parameters that map directly onto an experimental data set. We illustrate the functionality and novelty of Surrogate Modeling for Reconstructing Parameter Surfaces by applying it to an agent-based model 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 of interest, and where computational complexity and sparse, noisy calibration data hinder model parameterization.

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“…Targeting the specific task of parameters estimation, [7] laid out an efficient means for performing Bayesian inference on an ABM. In [8], we developed and analyzed what we refer to as the global method for handling molecular dynamics in an ABM, and this method resulted in orders of magnitude speedup. This method is best suited for ABMs that require simulating reactions at the cell surface and/or intracellular signaling pathways.…”

Section: Introductionmentioning

confidence: 99%

Phenotype switching in a global method for agent-based models of biological tissue

Bergman

Jackson

2022

Preprint

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A global method for fast simulations of molecular dynamics in multiscale agent-based models of biological tissues

Cited by 6 publications

References 63 publications

Phenotype switching in a global method for agent-based models of biological tissue

Phenotype switching in a global method for agent-based models of biological tissue

SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth

Phenotype switching in a global method for agent-based models of biological tissue

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