Parameter estimation analysis of the glioblastoma immune model

In exploring optimal strategies for immunotherapy in glioblastoma (GBM), one of the main challenges is enhancing treatment response. To better understand the dynamics of tumor-immune interactions, one applied Bayesian methods to estimate the parameters of glioblastoma immune model by using experimental data. One compared the effects of using uniform prior distributions versus improved prior distributions, which were adjusted based on posterior information, during parameter estimation. In addition, a comparative analysis of the results obtained by using four Markov Chain Monte Carlo (MCMC) sampling algorithms which respectively are Metropolis, DEMetropolis, DEMetropolisZ and NUTS, were performed. The results showed that the improved prior distribution significantly enhanced the accuracy of the model parameter estimates, and reduced the variance of the posterior distribution, but increased computational time and resource demands. Furthermore, DEMetropolisZ provided such efficient sampling and narrower confidence intervals within a shorter time frame, which outperformed the others. In contrast, the efficiency and stability of the Metropolis method were relatively poor. Therefore, the importance of selecting appropriate prior distributions and sampling algorithms to improve both the accuracy and efficiency of model inference were studied. The study provides valuable insights for optimizing GBM immunotherapy strategies and serves as a reference for modeling and parameter estimation of complex biological systems.