ArticleComputational and structural biotechnology journal2023
Integration of transcriptomics data into agent-based models of solid tumor metastasis.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Informing agent-based models with spatial data using convolutional autoencoders.Bioinformatics (Oxford, England) · 2026Article
- Integrating AI, mechanistic modelling and network approaches in systems biology for translational research.NPJ systems biology and applications · 2026Article
- Agent-based modeling of cellular dynamics in adoptive cell therapy.Communications biology · 2026Article
- Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation.Cancer biology & therapy · 2024Review
- Gene network-based and ensemble modeling-based selection of tumor-associated antigens with a predicted low risk of tissue damage for targeted immunotherapy.Journal for immunotherapy of cancer · 2024Article
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4 authors.
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Abstract
Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational modeling is a promising approach that would benefit from protocols to integrate the data generated by the high-throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale cancer models. In the method, we conduct transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We then determine global and local sensitivity through systematic model simulations that assess the relevance of parameter variations in triggering therapy resistance. We illustrate the methodology with a
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