ArticleFrontiers in immunology2015
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.
Article in Frontiers in immunology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Exploring Chronic Rejection in Organ Transplantation Through Computational Modeling.Results and problems in cell differentiation · 2026Review
- Understanding Immune Dynamics in Liver Transplant Through Mathematical Modeling.Bulletin of mathematical biology · 2025Article
- Utilization of Computable Phenotypes in Electronic Health Record Research: A Review and Case Study in Atopic Dermatitis.The Journal of investigative dermatology · 2025Review
- Building digital twins of the human immune system: toward a roadmap.NPJ digital medicine · 2022Review
- Modeling the Potential of Treg-Based Therapies for Transplant Rejection: Effect of Dose, Timing, and Accumulation Site.Transplant international : official journal of the European Society for Organ Transplantation · 2022Article
- A novel agent-based, evolutionary model for expressing the dynamics of creative open-problem solving in small groups.Applied intelligence (Dordrecht, Netherlands) · 2021Article
- A case for the reuse and adaptation of mechanistic computational models to study transplant immunology.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2020Review
- Multiscale Modeling in the Clinic: Drug Design and Development.Annals of biomedical engineering · 2016Review
- Combining Theoretical and Experimental Techniques to Study Murine Heart Transplant Rejection.Frontiers in immunology · 2016Article
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Abstract
Agent-based modeling has been used to characterize the nested control loops and non-linear dynamics associated with inflammatory and immune responses, particularly as a means of visualizing putative mechanistic hypotheses. This process is termed dynamic knowledge representation and serves a critical role in facilitating the ability to test and potentially falsify hypotheses in the current data- and hypothesis-rich biomedical research environment. Importantly, dynamic computational modeling aids in identifying useful abstractions, a fundamental scientific principle that pervades the physical sciences. Recognizing the critical scientific role of abstraction provides an intellectual and methodological counterweight to the tendency in biology to emphasize comprehensive description as the primary manifestation of biological knowledge. Transplant immunology represents yet another example of the challenge of identifying sufficient understanding of the inflammatory/immune response in order to develop and refine clinically effective interventions. Advances in immunosuppressive therapies have greatly improved solid organ transplant (SOT) outcomes, most notably by reducing and treating acute rejection. The end goal of these transplant immune strategies is to facilitate effective control of the balance between regulatory T cells and the effector/cytotoxic T-cell populations in order to generate, and ideally maintain, a tolerant phenotype. Characterizing the dynamics of immune cell populations and the interactive feedback loops that lead to graft rejection or tolerance is extremely challenging, but is necessary if rational modulation to induce transplant tolerance is to be accomplished. Herein is presented the solid organ agent-based model (SOTABM) as an initial example of an agent-based model (ABM) that abstractly reproduces the cellular and molecular components of the immune response to SOT. Despite its abstract nature, the SOTABM is able to qualitatively reproduce acute rejection and the suppression of acute rejection by immunosuppression to generate transplant tolerance. The SOTABM is intended as an initial example of how ABMs can be used to dynamically represent mechanistic knowledge concerning transplant immunology in a scalable and expandable form and can thus potentially serve as useful adjuncts to the investigation and development of control strategies to induce transplant tolerance.
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