ArticleMolecular systems design & engineering2019
Learning-accelerated discovery of immune-tumour interactions.
Article in Molecular systems design & engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Tumor-immune trajectory context connects static tissue architecture to clinical outcomes.bioRxiv : the preprint server for biology · 2026Article
- Experimentally calibrated multiscale model predicts schedule dependent drug combination effects.NPJ systems biology and applications · 2026Article
- Article
- Review
- A Simple Framework for Agent-Based Modeling with Extracellular Matrix.Bulletin of mathematical biology · 2025Article
- A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins.NPJ systems biology and applications · 2024Article
- DISTRIBUTED MODEL EXPLORATION WITH EMEWS.Proceedings of the ... Winter Simulation Conference. Winter Simulation Conference · 2024Article
- A review of mechanistic learning in mathematical oncology.Frontiers in immunology · 2024Review
- Digitize your Biology! Modeling multicellular systems through interpretable cell behavior.bioRxiv : the preprint server for biology · 2023Article
- PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks.NPJ systems biology and applications · 2023Article
- A computational modeling approach for predicting multicell spheroid patterns based on signaling-induced differential adhesion.PLoS computational biology · 2022Article
- Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth.Frontiers in molecular biosciences · 2022Article
- SupportingPRIMUS : problems, resources, and issues in mathematics undergraduate studies · 2022Article
- A persistent invasive phenotype in post-hypoxic tumor cells is revealed by fate mapping and computational modeling.iScience · 2021Article
- Article
- The Use of Artificial Neural Networks to Forecast the Behavior of Agent-Based Models of Pathophysiology: An Example Utilizing an Agent-Based Model of Sepsis.Frontiers in physiology · 2021Article
- Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics.Frontiers in oncology · 2021Review
- Experiences in Developing a Distributed Agent-based Modeling Toolkit with Python.Proceedings of PYHPC 2020 : 9th Workshop on Python for High-Performance and Scientific Computing. Workshop on Python for High-Performance and Scientific Computing (9th : 2020 : Online) · 2020Article
- A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma.GigaScience · 2020Article
- Key challenges facing data-driven multicellular systems biology.GigaScience · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
We present an integrated framework for enabling dynamic exploration of design spaces for cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our framework combines PhysiCell, an open source agent-based simulation platform for cancer and other multicellular systems, and EMEWS, an open source platform for extreme-scale model exploration. We build an agent-based model of immunosurveillance against heterogeneous tumours, which includes spatial dynamics of stochastic tumour-immune contact interactions. We implement active learning and genetic algorithms using high-performance computing workflows to adaptively sample the model parameter space and iteratively discover optimal cancer regression regions within biological and clinical constraints.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.