Random walks on binary strings applied to the somatic hypermutation of B-cells

TL;DR

This paper models somatic hypermutation in B-cells using random walks on graphs to analyze mutation efficiency and antibody affinity maturation, providing explicit formulas and exploring biological implications.

Contribution

It introduces a mathematical model using graph-based random walks to understand the mutation process in B-cells, including explicit formulas for hitting times and extensions to more complex models.

Findings

Explicit formulas for expected hitting times on mutation graphs

Analysis of mutation process efficiency in affinity maturation

Numerical simulations of biologically more involved models

Abstract

Within the germinal center in follicles, B-cells proliferate, mutate and differentiate, while being submitted to a powerful selection~: a micro-evolutionary mechanism at the heart of adaptive immunity. A new foreign pathogen is confronted to our immune system, the mutation mechanism that allows B-cells to adapt to it is called {\em somatic hypermutation}~: a programmed process of mutation affecting B-cell receptors at extremely high rate. By considering random walks on graphs, we introduce and analyze a simplified mathematical model in order to understand this extremely efficient learning process. The structure of the graph reflects the choice of the mutation rule. We focus on the impact of this choice on typical time-scales of the graphs' exploration. We derive explicit formulas to evaluate the expected hitting time to cover a given Hamming distance on the graphs under consideration. This characterizes the efficiency of these processes in driving antibody affinity maturation. In a further step we present a biologically more involved model and discuss its numerical outputs within our mathematical framework. We provide as well limitations and possible extensions of our approach.