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.

Highlights

  • IntroductionThe United States Food and Drug Administration report: “Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products” [1] clearly delineates a steadily increasing expenditure on Research and Development that is concurrent with a progressive decrease in the delivery of medical products to market; while this report is over a decade old, this trajectory has not substantively changed since the release of that report

  • THE ROLE OF DYNAMIC KNOWLEDGE REPRESENTATION TO ADDRESS THE TRANSLATIONAL DILEMMAThe central dilemma for the biomedical research community today can be described as a paradoxical challenge of dealing with an embarrassment of riches

  • We have asserted that the primary bottleneck in the current biomedical research workflow is the ability to evaluate and falsify the vast sets of putative mechanistic hypotheses being generated from the data-rich environment and that the use of computational modeling for dynamic knowledge representation is the means by which this bottleneck, and the Translational Dilemma, can be addressed [2]

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Summary

Introduction

The United States Food and Drug Administration report: “Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products” [1] clearly delineates a steadily increasing expenditure on Research and Development that is concurrent with a progressive decrease in the delivery of medical products to market; while this report is over a decade old, this trajectory has not substantively changed since the release of that report This is the Translational Dilemma that faces biomedical research: the inability to effectively and efficiently translate basic mechanistic knowledge into clinically effective therapeutics, most apparent in attempts to understand and modulate “systems” processes/disorders, such as sepsis, cancer, wound healing, and immunomodulation (including transplantation). With the specific goal of facilitating the computational representation of the mechanistic knowledge generated from basic biological research, agent-based modeling is a modeling method that is well suited for this purpose

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