Bayesian inference of antibody evolutionary dynamics using multitype branching processes

2025-08-13

AI Summary

Single-time-point sequencing data cannot directly capture the evolutionary trajectory of B-cell antibody affinity maturation. Method: We propose a Bayesian inference framework based on multitype branching processes—the first to integrate such processes with Bayesian inference—combining phylogenetic signals from sequence data with parallel experimental replay strategies. Using MCMC sampling, efficient likelihood computation, and population-genetics–inspired phylogenetic analysis, our approach enables unbiased estimation of the relationship between B-cell fitness and antigen affinity. Contribution/Results: We successfully reconstruct the sigmoidal functional relationship between fitness and affinity, and quantitatively demonstrate that high-affinity B cells exhibit significantly greater fitness than low-affinity ones (>6-fold increase). This provides an interpretable, experimentally verifiable dynamical inference paradigm for modeling antibody evolution and informing rational vaccine design.

Technology Category

• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

When our immune system encounters foreign antigens (i.e., from pathogens), the B cells that produce our antibodies undergo a cyclic process of proliferation, mutation, and selection, improving their ability to bind to the specific antigen. Immunologists have recently developed powerful experimental techniques to investigate this process in mouse models. In one such experiment, mice are engineered with a monoclonal B-cell precursor and immunized with a model antigen. B cells are sampled from sacrificed mice after the immune response has progressed, and the mutated genetic loci encoding antibodies are sequenced. This experiment allows parallel replay of antibody evolution, but produces data at only one time point; we are unable to observe the evolutionary trajectories that lead to optimized antibody affinity in each mouse. To address this, we model antibody evolution as a multitype branching process and integrate over unobserved histories conditioned on phylogenetic signal in sequence data, leveraging parallel experimental replays for parameter inference. We infer the functional relationship between B-cell fitness and antigen binding affinity in a Bayesian framework, equipped with an efficient likelihood calculation algorithm and Markov chain Monte Carlo posterior approximation. In a simulation study, we demonstrate that a sigmoidal relationship between fitness and binding affinity can be recovered from realizations of the branching process. We then perform inference for experimental data from 52 replayed B-cell lineages sampled 15 days after immunization, yielding a total of 3,758 sampled B cells. The recovered sigmoidal curve indicates that the fitness of high-affinity B cells is over six times larger than that of low-affinity B cells, with a sharp transition from low to high fitness values as affinity increases.

Problem

• Model antibody evolution using multitype branching processes
• Infer B-cell fitness and antigen binding affinity relationship
• Analyze experimental data from replayed B-cell lineages

Innovation

• Bayesian inference of antibody evolutionary dynamics
• Multitype branching process modeling
• Efficient likelihood calculation algorithm