Review of the systems biology of the immune system using agent‐based models

Shinde, Snehal B.; Kurhekar, Manish P.

doi:10.1049/iet-syb.2017.0073

Cited by 16 publications

(9 citation statements)

References 74 publications

(139 reference statements)

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“…Such multi-scale hybrid models increase the flexibility in model construction, improve computational performance, and enhance model credibility by allowing comparison between model output and a wide range of experimental and clinical observations. From another standpoint, the models that describe the immune system can be broadly categorized into top-down and bottom-up, and previous reviews have focused on computational modeling of the immune system [44,54]. The top-down approach models populations of cells, not single entities, and uses the mean behavior at the macroscopic level.…”

Section: Overview Of Computational Modeling Methodologies Including Amentioning

confidence: 99%

“…As is evident from the complex mechanisms of immune response and immune evasion described above, modeling the immune system is a challenging task [44]. For the specific case of cancer, immune cells can be found within the TME, the lymphatic system and the lymph nodes, resulting in spatial complexity.…”

Section: Immune System Biology and Cancermentioning

confidence: 99%

“…A series of studies on modeling of T-cell behavior in the lymph node have been conducted by Bogle and Dunbar [44,[109][110][111][112]. They modeled T-cell trafficking, activation, and proliferation in the lymph node paracortex using an agent-based approach.…”

Section: Models Focusing On Tumor-associated Lymphatics and Lymph Nodesmentioning

confidence: 99%

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Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

et al. 2019

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Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.

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Section: Overview Of Computational Modeling Methodologies Including Amentioning

confidence: 99%

Section: Immune System Biology and Cancermentioning

confidence: 99%

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Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

et al. 2019

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“…Each individual is assumed to be a social, intelligent agent that constantly modifies its own behavioral rules through feedbacks called micro-macro loops [57] , [58] . There have existed studies that employed ABM to describe the interaction of the immune system between tuberculosis and cancer [59] . In contrast, EBM is not subject to heterogeneity but relatively homogeneous, i.e.…”

Section: Related Workmentioning

confidence: 99%

Modeling viral dynamics in SARS-CoV-2 infection based on differential equations and numerical analysis

Odaka

Inoue

2021

Heliyon

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“…agent-based models, e.g., [7] which prove fruitful for non-linear interactions among entities of a biological system [8]. 2.…”

Section: Introductionmentioning

confidence: 99%

SBMLWebApp: Web-Based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models

Yamada

König

et al. 2021

Processes

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In systems biology, biological phenomena are often modeled by Ordinary Differential Equations (ODEs) and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulation, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the Systems Biology Simulation Core Library (SBSCL), and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code freely available. At the end of this article, the Data Availability Statement gives the internet links to the two websites to find the source code and run the program online.

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Review of the systems biology of the immune system using agent‐based models

Cited by 16 publications

References 74 publications

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

Modeling viral dynamics in SARS-CoV-2 infection based on differential equations and numerical analysis

SBMLWebApp: Web-Based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models

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