ArticlePloS one2015
Towards anatomic scale agent-based modeling with a massively parallel spatially explicit general-purpose model of enteric tissue (SEGMEnT_HPC).
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- The Wound Environment Agent-based Model (WEABM): a digital twin platform for characterization and complex therapeutic discovery for volumetric muscle loss.bioRxiv : the preprint server for biology · 2024Article
- Combination Chemotherapy of Multidrug-resistant Early-stage Colon Cancer: Determining Optimal Dose Schedules by High-performance Computer Simulation.Cancer research communications · 2023Article
- Multiscale modeling in the framework of biological systems and its potential for spaceflight biology studies.iScience · 2022Review
- Utilizing the Heterogeneity of Clinical Data for Model Refinement and Rule Discovery Through the Application of Genetic Algorithms to Calibrate a High-Dimensional Agent-Based Model of Systemic Inflammation.Frontiers in physiology · 2021Article
- Agent-based models of inflammation in translational systems biology: A decade later.Wiley interdisciplinary reviews. Systems biology and medicine · 2019Review
- Agent-based modeling of morphogenetic systems: Advantages and challenges.PLoS computational biology · 2019Review
- Optimization of Dose Schedules for Chemotherapy of Early Colon Cancer Determined by High-Performance Computer Simulations.Cancer informatics · 2019Article
- Engineering a Tumor Microenvironment-Mimetic Niche for Tissue Regeneration with Xenogeneic Cancer Cells.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2018Article
- Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation.PLoS computational biology · 2018Article
- Mechanobiological model of arterial growth and remodeling.Biomechanics and modeling in mechanobiology · 2018Article
- Introduction of a Framework for Dynamic Knowledge Representation of the Control Structure of Transplant Immunology: Employing the Power of Abstraction with a Solid Organ Transplant Agent-Based Model.Frontiers in immunology · 2015Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis.
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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.