ArticleBMC bioinformatics2018
High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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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.
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Who cites it
33 citing papers in PubMed, 65 citations in OpenAlex.
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- Drug-loaded nanoparticles for cancer therapy: A high-throughput multicellular agent-based modeling study.Journal of theoretical biology · 2026Article
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- A three-dimensional lattice-free agent-based model of intracellular ice formation and propagation and intercellular mechanics in liver tissues.Royal Society open science · 2024Article
- In silico study of heterogeneous tumour-derived organoid response to CAR T-cell therapy.Scientific reports · 2024Article
- Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist.Frontiers in cell and developmental biology · 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
- Examining the efficacy of localised gemcitabine therapy for the treatment of pancreatic cancer using a hybrid agent-based model.PLoS computational biology · 2023Article
- PhysiCOOL: A generalized framework for model Calibration and Optimization Of modeLing projects.GigaByte (Hong Kong, China) · 2023Article
- Mathematical modeling of cancer immunotherapy for personalized clinical translation.Nature computational science · 2022Article
- Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy.iScience · 2022Article
- Drug Development Digital Twins for Drug Discovery, Testing and Repurposing: A Schema for Requirements and Development.Frontiers in systems 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
- Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundCancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatment's success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologies-one to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimization-can provide a computational means for high-throughput hypothesis testing, and eventually, optimization.
resultsIn this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in applying PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a generalized PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where hundreds or thousands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization.
conclusionsWhile key notational and computational challenges remain, mechanistic agent-based models and high-throughput model exploration environments can be combined to systematically and rapidly explore key problems in cancer. These high-throughput computational experiments can improve our understanding of the underlying biology, drive future experiments, and ultimately inform clinical practice.
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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.