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

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 a...

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Autores principales: Jain, Harsh Vardhan, Norton, Kerri-Ann, Prado, Bernardo Bianco, Jackson, Trachette L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816661/
https://www.ncbi.nlm.nih.gov/pubmed/36619168
http://dx.doi.org/10.3389/fmolb.2022.1056461
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author Jain, Harsh Vardhan
Norton, Kerri-Ann
Prado, Bernardo Bianco
Jackson, Trachette L.
author_facet Jain, Harsh Vardhan
Norton, Kerri-Ann
Prado, Bernardo Bianco
Jackson, Trachette L.
author_sort Jain, Harsh Vardhan
collection PubMed
description 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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spelling pubmed-98166612023-01-07 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. Front Mol Biosci Molecular Biosciences 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. Frontiers Media S.A. 2022-12-23 /pmc/articles/PMC9816661/ /pubmed/36619168 http://dx.doi.org/10.3389/fmolb.2022.1056461 Text en Copyright © 2022 Jain, Norton, Prado and Jackson. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Jain, Harsh Vardhan
Norton, Kerri-Ann
Prado, Bernardo Bianco
Jackson, Trachette L.
SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth
title SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth
title_full SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth
title_fullStr SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth
title_full_unstemmed SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth
title_short SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth
title_sort smore pars: a novel methodology for bridging modeling modalities and experimental data applied to 3d vascular tumor growth
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816661/
https://www.ncbi.nlm.nih.gov/pubmed/36619168
http://dx.doi.org/10.3389/fmolb.2022.1056461
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