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Bayesian inference of antibody evolutionary dynamics using multitype branching processes

Abstract Number:

2797 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Thanasi Bakis (1), William DeWitt (2), Yun Song (2), Frederick Matsen (3), Volodymyr Minin (1)

Institutions:

(1) University of California, Irvine, CA, (2) University of California, Berkeley, CA, (3) Fred Hutchinson Cancer Research Center, Seattle, WA

Co-Author(s):

William DeWitt  
University of California
Yun Song  
University of California
Frederick Matsen  
Fred Hutchinson Cancer Research Center
Volodymyr Minin  
University of California

First Author:

Thanasi Bakis  
University of California

Presenting Author:

Thanasi Bakis  
N/A

Abstract Text:

When our immune systems encounter foreign invaders, the B cells that produce our antibodies undergo a cyclic process of mutation and selection, competing to provide a refined immune response to the specific invader. To study how the immune system recognizes when the antibodies are sufficiently improved, we examine the state of the immune system in mice after an exposure to an artificial foreign agent by collecting genetic sequences of B cells. This experiment produces data only at one time point, so we lose all information about the preceding evolutionary process that mutates and selects B cells to optimize antibody efficiency. In this paper, we develop a multitype branching process model that integrates over unobserved antibody evolutionary histories and leverages parallel replications of immune responses we observed in experimentation. Our fully Bayesian approach, equipped with an efficient likelihood calculation algorithm and Markov chain Monte Carlo based approximation of the posterior, allows us to infer the currently-unknown functional relationship between the fitness of B cells that produce antibodies and the binding strength of these antibodies to pathogen-infected cells.

Keywords:

immunology|phylogenetics|phylodynamics|stochastic processes| |

Sponsors:

Section on Statistics in Genomics and Genetics

Tracks:

Miscellaneous

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