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System dynamics simulation for evaluating implementation strategies of genomic sequencing: tutorial and conceptual

Hadi A Khorshidi1,2,3, Deborah Marshall4, Ilias Goranitis5

  • 1Cancer Health Services Research, University of Melbourne Centre for Cancer Research, Parkville, Australia.

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|October 7, 2023
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Summary

System Dynamics (SD) offers powerful modeling for precision medicine (PM) in oncology, simplifying complex genomic testing and personalized treatment evaluation. SD provides advantages in dynamic analysis, even with limited data, aiding strategic decision-making.

Keywords:
GenomicsHealth services researchoncologysimulationsystems dynamicswhole genome sequencing

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Area of Science:

  • Health Policy
  • Computational Biology
  • Oncology

Background:

  • Precision medicine (PM), particularly in oncology, relies on genomic features for complex diagnostic and treatment pathways.
  • Evaluating and making decisions for PM necessitates advanced modeling techniques due to inherent complexity.
  • System Dynamics (SD) possesses strong modeling capabilities but is underutilized in PM and personalized treatment.

Purpose of the Study:

  • To explain System Dynamics (SD) tools within a cancer context.
  • To establish the rationale for using SD in genomic testing and personalized oncology.
  • To develop a conceptual model for strategic decision-making in Whole Genome Sequencing (WGS) implementation.

Main Methods:

  • Explanation of SD tools with cancer-specific examples.
  • Comparison of SD with other Dynamic Simulation Modelling (DSM) methods, highlighting SD's advantages.
  • Development of a conceptual model using Causal Loop Diagram (CLD) for WGS implementation strategy.

Main Results:

  • SD is suitable for health policy evaluation and modeling precision oncology and genomic testing.
  • SD's system-oriented approach captures complex dynamics using feedback loops.
  • SD models are resource-efficient, enabling exploratory and explanatory analyses over time.

Conclusions:

  • SD offers significant advantages for modeling and evaluating complex health systems, especially those with limited data and requiring interpretability.
  • SD's ability to model dynamic interactions and feedback loops makes it valuable for precision oncology and genomic testing.
  • The conceptual model demonstrates SD's utility for strategic decisions in implementing technologies like Whole Genome Sequencing.