Bayesian Analysis of Immune Response Dynamics with Sparse Time Series Data

Authors: 
Fernando V Bonassi, Cliburn Chan, Mike West
Google, Duke University Medical Center, Duke University

Apr 30 2014

In vaccine  development, the temporal  profiles of relative  abundance of  subtypes of immune cells (T-cells) is key to understanding vaccine efficacy.   Complex and expensive experimental studies generate very sparse time series data on this immune response. Fitting multi-parameter dynamic models of the immune response dynamics-- central to evaluating  mechanisms underlying vaccine efficacy-- is challenged by data sparsity.  The research reported here addresses this challenge.  For HIV/SIV vaccine studies in macaques, we:  (a) introduce novel dynamic models of progression of cellular populations over time with relevant, time-delayed components reflecting the vaccine response; (b)  define an effective Bayesian model fitting strategy that couples Markov chain Monte Carlo (MCMC) with Approximate Bayesian Computation (ABC)-- building on the complementary strengths of the two approaches, neither of which is effective alone; (c) explore questions of information content in the sparse time series for each of the model parameters, linking into experimental design and model simplification for future experiments;  and (d) develop, apply and compare the analysis with samples from a recent HIV/SIV  experiment, with novel insights and conclusions about the progressive response to the vaccine, and how this varies across subjects.

Keywords: 

Approximate Bayesian Computation (ABC), Bayesian Inference, Dynamic Models, Immunology, Learnability of Parameters, Markov chain Monte Carlo (MCMC), ODE Models, Sparse Data, Time Delays, Vaccine Design

Manuscript: 

PDF icon 2014-02.pdf

BibTeX Citation: 

@TECHREPORT{Bonassi2014,
  author = {F. V. Bonassi and C. Chan and M. West},
  title = {Bayesian analysis of immune response dynamics with sparse time series
	data},
  institution = {Department of Statistical Science, Duke University},
  year = {2014},
  type = {Discussion Paper},
  note = {Submitted for publication},
  url = {http://stat.duke.edu/research/papers/2014-02}
}