ArticlePLoS computational biology2023
Calibrating agent-based models to tumor images using representation learning.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Informing agent-based models with spatial data using convolutional autoencoders.Bioinformatics (Oxford, England) · 2026Article
- Cancer-associated fibroblasts drive metabolic heterogeneity in colorectal cancer cells: predictions from metabolic modeling.NPJ systems biology and applications · 2026Article
- Migrastatic therapy as a potential game-changer in adaptive cancer treatment.Scientific reports · 2026Article
- Hybrid modelling using simulation and machine learning in healthcare.Computers & operations research · 2026Review
- Quantitative Calibration of a Spatial QSP Model Identifies Fibroblast Impact on HCC Immunotherapy.bioRxiv : the preprint server for biology · 2025Article
- Cancer model with moving extinction threshold reproduces real cancer data.Journal of the Royal Society, Interface · 2025Article
- Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer.NPJ systems biology and applications · 2025Article
- Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation.Cancer biology & therapy · 2024Review
- Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer.bioRxiv : the preprint server for biology · 2024Article
- Spatial organization and stochastic fluctuations of immune cells impact clinical responsiveness to immunotherapy in melanoma patients.PNAS nexus · 2024Article
- Spatially fractionated GRID radiation potentiates immune-mediated tumor control.Radiation oncology (London, England) · 2024Article
- Integrating mechanism-based T cell phenotypes into a model of tumor-immune cell interactions.APL bioengineering · 2024Article
- Integration of Clinical Trial Spatial Multiomics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery.Cancer research · 2024Article
- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
- A seven-step guide to spatial, agent-based modelling of tumour evolution.Evolutionary applications · 2024Review
- Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine.Frontiers in physiology · 2024Review
- Dysregulated FGFR3 signaling alters the immune landscape in bladder cancer and presents therapeutic possibilities in an agent-based model.Frontiers in immunology · 2024Article
- Connecting Agent-Based Models with High-Dimensional Parameter Spaces to Multidimensional Data Using SMoRe ParS: A Surrogate Modeling Approach.Bulletin of mathematical biology · 2023Article
- Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial.bioRxiv : the preprint server for biology · 2023Article
- Calibration methods to fit parameters within complex biological models.Frontiers in applied mathematics and statistics · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Agent-based models (ABMs) have enabled great advances in the study of tumor development and therapeutic response, allowing researchers to explore the spatiotemporal evolution of the tumor and its microenvironment. However, these models face serious drawbacks in the realm of parameterization - ABM parameters are typically set individually based on various data and literature sources, rather than through a rigorous parameter estimation approach. While ABMs can be fit to simple time-course data (such as tumor volume), that type of data loses the spatial information that is a defining feature of ABMs. While tumor images provide spatial information, it is exceedingly difficult to compare tumor images to ABM simulations beyond a qualitative visual comparison. Without a quantitative method of comparing the similarity of tumor images to ABM simulations, a rigorous parameter fitting is not possible. Here, we present a novel approach that applies neural networks to represent both tumor images and ABM simulations as low dimensional points, with the distance between points acting as a quantitative measure of difference between the two. This enables a quantitative comparison of tumor images and ABM simulations, where the distance between simulated and experimental images can be minimized using standard parameter-fitting algorithms. Here, we describe this method and present two examples to demonstrate the application of the approach to estimate parameters for two distinct ABMs. Overall, we provide a novel method to robustly estimate ABM parameters.
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