Next Article in Journal
Aquaporins during Pregnancy: Their Function and Significance
Next Article in Special Issue
LncRNA Structural Characteristics in Epigenetic Regulation
Previous Article in Journal
Impaired Osteogenesis of Disease-Specific Induced Pluripotent Stem Cells Derived from a CFC Syndrome Patient
Previous Article in Special Issue
Identifying the Epitope Regions of Therapeutic Antibodies Based on Structure Descriptors
Open AccessArticle

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

by Tingting Li 1,*,†, Zhengguo Cheng 2,† and Le Zhang 2,3,*
1
College of Mathematics and Statistics, Southwest University, Chongqing 400715, China
2
College of Computer and Information Science, Southwest University, Chongqing 400715, China
3
College of Computer Science, Sichuan University, Chengdu 610065, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Mol. Sci. 2017, 18(12), 2592; https://doi.org/10.3390/ijms18122592
Received: 19 October 2017 / Revised: 23 November 2017 / Accepted: 26 November 2017 / Published: 1 December 2017
Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency. View Full-Text
Keywords: agent-based models; generalized additive model; history matching; particle swarm optimization algorithm agent-based models; generalized additive model; history matching; particle swarm optimization algorithm
Show Figures

Figure 1

MDPI and ACS Style

Li, T.; Cheng, Z.; Zhang, L. 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. Int. J. Mol. Sci. 2017, 18, 2592.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop