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Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data

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  • Xuming Tong
  • Jinghang Chen
  • Hongyu Miao
  • Tingting Li
  • Le Zhang

Abstract

Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.

Suggested Citation

  • Xuming Tong & Jinghang Chen & Hongyu Miao & Tingting Li & Le Zhang, 2015. "Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
    • Handle: RePEc:plo:pone00:0141295
    DOI: 10.1371/journal.pone.0141295
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    References listed on IDEAS

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    1. Heiss, Florian & Winschel, Viktor, 2008. "Likelihood approximation by numerical integration on sparse grids," Journal of Econometrics, Elsevier, vol. 144(1), pages 62-80, May.
    2. Hongyu Miao & Hulin Wu & Hongqi Xue, 2014. "Generalized Ordinary Differential Equation Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1672-1682, December.
    3. Hongyu Miao & Carrie Dykes & Lisa M. Demeter & Hulin Wu, 2009. "Differential Equation Modeling of HIV Viral Fitness Experiments: Model Identification, Model Selection, and Multimodel Inference," Biometrics, The International Biometric Society, vol. 65(1), pages 292-300, March.
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