ArticleBMC bioinformatics2015
Multiscale modeling of mucosal immune responses.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- The Future Exploring of Gut Microbiome-Immunity Interactions: From In Vivo/Vitro Models to In Silico Innovations.Microorganisms · 2024Review
- Mathematical modeling of regulatory networks of intracellular processes - Aims and selected methods.Computational and structural biotechnology journal · 2023Review
- A multiscale mechanistic model of human dendritic cells forFrontiers in immunology · 2023Article
- A roadmap for translational cancer glycoimmunology at single cell resolution.Journal of experimental & clinical cancer research : CR · 2022Review
- Computational models in inflammatory bowel disease.Clinical and translational science · 2022Review
- Parallelisation strategies for agent based simulation of immune systems.BMC bioinformatics · 2019Article
- Agent-based models of inflammation in translational systems biology: A decade later.Wiley interdisciplinary reviews. Systems biology and medicine · 2019Review
- High-resolution computational modeling of immune responses in the gut.GigaScience · 2019Article
- Review of the systems biology of the immune system using agent-based models.IET systems biology · 2018Review
- A systems pharmacology model for inflammatory bowel disease.PloS one · 2018Article
- Solving Immunology?Trends in immunology · 2017Review
- Model of Host-Pathogen Interaction Dynamics Links In Vivo Optical Imaging and Immune Responses.Infection and immunity · 2017Article
- IL-2 sensitivity and exogenous IL-2 concentration gradient tune the productive contact duration of CD8(+) T cell-APC: a multiscale modeling study.BMC systems biology · 2016Article
- Modeling-Enabled Systems Nutritional Immunology.Frontiers in nutrition · 2016Review
- Modeling the Role of Lanthionine Synthetase C-Like 2 (LANCL2) in the Modulation of Immune Responses to Helicobacter pylori Infection.PloS one · 2016Article
- Mucosal Interactions between Genetics, Diet, and Microbiome in Inflammatory Bowel Disease.Frontiers in immunology · 2016Review
- Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection.PloS one · 2015Article
- Goals in Nutrition Science 2015-2020.Frontiers in nutrition · 2015Article
- Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection.PloS one · 2015Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundComputational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation. IMPLEMENTATION: Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed.
conclusionWe used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation.Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM.
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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.