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

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.

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

“…At last, the model’s key parameters were optimized using PSO in the non-implausible domain by fitting the real experiment data. In this paper, the real Influeza A Virus (IAV) data set [ 2 , 20 ] is employed to demonstrate the performance of our proposed procedure ( Figure 1 ) and the statements of the detailed method are deferred in Section 3 .…”

“…In view of this, Tong et al [ 20 ] developed an innovative approach, IABMR, by integrating the advantages of both DE and ABM. Firstly, they denoted each cell as an agent with three phenotypes (i.e., quiescence, proliferation, and apoptosis) and employed ABM to describe the dynamic interactions among the components (i.e., epithelial cells, infected epithelial cells, and virus) and simulate the immune system.…”

“…A novel parameter estimation method by incorporating the experimental data for the simulator ABM during the procedure is proposed. IAV infection data [ 2 , 20 ] is employed to evaluate the efficiency and accuracy of the proposed method. First, we employed ABM as simulator [ 31 ] to simulate the data.…”

Mathematical and Computational Modeling in Complex Biological Systems

“…In systems biology, ABM is usually used to model tumor growth (drug response) and angiogenesis in the cancer microenvironment. Each cell type in this system is considered as an agent, and the complicated cell-cell communications are achieved through some secreted ligands [88–94]. The implement of ABM is usually based on Markov Chain Monte Carlo.…”

Investigation of mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model and experimental optimization/validation

“…15 is to compute the average relative error (ARE)[17,29,30] for testing model's predictive precision. Eq.…”