Juxtaposition of System Dynamics and Agent-based Simulation for a Case Study in Immunosenescence

Abstract: Advances in healthcare and in the quality of life significantly increase human life expectancy. With the ageing of populations, new un-faced challenges are brought to science. The human body is naturally selected to be well-functioning until the age of reproduction to keep the species alive. However, as the lifespan extends, unseen problems due to the body deterioration emerge. There are several age-related diseases with no appropriate treatment; therefore, the complex ageing phenomena needs further understanding. Immunosenescence, the ageing of the immune system, is highly correlated to the negative effects of ageing, such as the increase of auto-inflammatory diseases and decrease in responsiveness to new diseases. Besides clinical and mathematical tools, we believe there is opportunity to further exploit simulation tools to understand immunosenescence. Compared to real-world experimentation, benefits include time and cost effectiveness due to the laborious, resource-intensiveness of the biological environment and the possibility of conducting experiments without ethic restrictions. Contrasted with mathematical models, simulation modelling is more suitable for representing complex systems and emergence. In addition, there is the belief that simulation models are easier to communicate in interdisciplinary contexts. Our work investigates the usefulness of simulations to understand immunosenescence by employing two different simulation methods, agent-based and system dynamics simulation, to a case study of immune cells depletion with age.

• The paper demonstrates that both SDMS and ABMS effectively model naive T cell dynamics in immunosenescence, with SDMS offering computational simplicity.
• SDMS uses deterministic ODE-based equations to capture feedback mechanisms, while ABMS simulates individual T cell interactions with stochastic variability.
• Implications include potential hybrid models that combine system-level insights and granular behavior details for improved clinical simulation applications.

Juxtaposition of System Dynamics and Agent-based Simulation for Immunosenescence

Introduction

The paper "Juxtaposition of System Dynamics and Agent-based Simulation for a Case Study in Immunosenescence" (1607.05888) presents an analysis of two computational simulation approaches—System Dynamics Modelling and Simulation (SDMS) and Agent-Based Modelling and Simulation (ABMS)—in understanding the dynamics of immunosenescence, specifically focusing on naive T cell depletion with age. As human life expectancy continues to rise, the phenomenon of immunosenescence presents significant challenges such as an increased prevalence of auto-inflammatory diseases and reduced immune response to new pathogens. The study aims to determine the suitability and interchangeability of SDMS and ABMS in modeling these complex biological processes.

Simulation Approaches

System Dynamics Modelling and Simulation (SDMS)

SDMS leverages continuous simulation with deterministic outcomes, operating on a top-down approach by employing a series of ordinary differential equations (ODEs) to represent the system dynamics. This method is particularly relevant for capturing causal loops and feedback mechanisms inherent in complex systemic processes. In this study, SDMS is used to model the naive T cell population dynamics based on equations that describe thymic output decay and cell population interactions over time, aligning it with real-world biological data.

Agent-Based Modelling and Simulation (ABMS)

ABMS, on the other hand, uses a bottom-up modeling perspective with stochastic characteristics. It is apt for systems where individual entity behavior and interactions drive the overall system dynamics. In this case, naive T cells are modeled as agents, each interacting with its environment based on predefined rules that dictate behaviors such as proliferation and death. Agents can assume different states, representing diverse phases of naive T cell development, and adapt over time, offering insights into emergent system behaviors from micro-level interactions.

Methodological Comparison and Results

The juxtaposition of SDMS and ABMS in this study sought to ascertain which approach better suits the problem of naive T cell depletion and whether they can substitute for one another effectively. Five scenarios with varying parameterizations were simulated for both SDMS and ABMS, comparing outputs to the original ODE-based model and empirical data.

Both methodologies successfully parallel the original mathematical model's results, with SDMS providing simplicity and computational efficiency, while ABMS offered potential insights into probabilistic variability and emergent behaviors. The variation observed in ABMS did not significantly deviate from expected population dynamics, reaffirming its interchangeability with SDMS for this scenario. The deterministic nature of SDMS renders it computationally less intensive, lending it an edge in straightforward implementation for large-scale simulations.

Implications and Future Research Directions

The findings suggest that while both SDMS and ABMS are capable of modeling naive T cell dynamics in the context of immunosenescence, SDMS appears more pragmatic for this specific case due to its computational simplicity and ease of use. This study highlights the potential of simulation models in providing insights into biological aging phenomena, reinforcing their applicability in healthcare advancements and policy formulation.

Future research could benefit from developing hybrid models that leverage the strengths of both SDMS's systemic perspective and ABMS's granular detail. Further, calibrating these simulations independently of the original ODE constraints could yield unique insights and improve the model's ability to replicate biological nuances more authentically. Enhanced model communication and interdisciplinary collaboration are vital for progressing simulation tools into practical clinical and research applications.

Conclusion

The study concludes that both system dynamics and agent-based simulations serve as effective tools in understanding the dynamics of immunosenescence. While similar outcomes were achieved between the two methodologies, SDMS offers a more resource-efficient approach suitable for scenarios requiring less complexity. However, the rich detail available in ABMS could be pivotal in contexts where interactions and emergent behaviors are of primary concern. Future research directions include refining these models independently of existing mathematical frameworks and enhancing their utility in real-world application contexts.