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PubMed · 42635223

Informing agent-based models with spatial data using convolutional autoencoders.

Bi-Rong Wang·Chen-Yi Liao·Erik H J Danen·Elsa Neubert·Federica Eduati
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Abstract

MOTIVATION: Spatial computational models such as agent-based models (ABMs) offer powerful in silico tools to study tumor dynamics, yet imaging data are still rarely used to inform these models directly. RESULTS: We present an ABM optimization framework that leverages convolutional encoders to compare spatial patterns between experimental imaging data and ABM-generated outputs within a shared latent space. This quantitative comparison was used to estimate ABM parameters across three datasets, ranging from synthetic data to 3D tumoroid-T cell co-culture microscopy and histopathology images from The Cancer Genome Atlas skin cutaneous melanoma samples. Estimated parameters were evaluated using data-derived features and experimental knowledge, including experimental conditions and gene expressions. Simulations using optimized parameters reproduced key spatial features of the training images, such as tumor boundary complexity and tumor-tumor neighborhood structure. Together, these results demonstrate a flexible framework for ABM parameter optimization using spatial data across modalities, enabling systematic investigation of how spatial architecture influences tumor progression and immune interactions. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/SysBioOncology/ AutoencoderABM under the GPL-3.0 license, with corresponding data sets at https://zenodo.org/records/19022344.

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AutoencoderHumansConvolutional Neural NetworksMelanomaComputer SimulationModels, BiologicalComputational BiologyImage Processing, Computer-AssistedJournal ArticleResearch Support, Non-U.S. Gov't

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Bi-Rong Wang, Chen-Yi Liao, Erik H J Danen, Elsa Neubert, Federica Eduati. 2026-08-01. Informing agent-based models with spatial data using convolutional autoencoders.. https://doi.org/10.1093/bioinformatics%2Fbtag444

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SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.

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