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

Harsh Vardhan Jain, Kerri Ann Norton, Bernardo Bianco Prado, Trachette L. Jackson

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Article number1056461
JournalFrontiers in Molecular Biosciences
StatePublished - Dec 23 2022

Bibliographical note

Funding Information:
This work was supported by NIH/NCI U01CA243075 (TJ). Part of the research for this paper was carried out while the authors were in residence at the Mathematisches Forschungsinstitut Oberwolfach, supported by their “Research in Pairs” program.

Publisher Copyright:
Copyright © 2022 Jain, Norton, Prado and Jackson.


  • agent-based model
  • parameter identifiability
  • surrogate model
  • uncertainty quantification
  • vascular tumor growth

PubMed: MeSH publication types

  • Journal Article


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