Implementing Immune Repertoire Models Using Weighted Finite State Machines

TL;DR

This paper introduces a novel approach using weighted finite state machines to model immune cell repertoires, enabling scalable and robust artificial immune systems that account for data multiplicity, with applications in anomaly detection.

Contribution

The paper demonstrates how weighted FSMs can effectively represent immune repertoires and model immunological processes, overcoming previous scalability limitations in string-based AISs.

Findings

Weighted FSMs enable representation of cell repertoires with multiplicity.

The method improves performance and robustness in anomaly detection tasks.

Scalability is significantly enhanced, allowing billions of receptors to be modeled quickly.

Abstract

The adaptive immune system's T and B cells can be viewed as large populations of simple, diverse classifiers. Artificial immune systems (AIS) $\unicode{x2013}$ algorithmic models of T or B cell repertoires $\unicode{x2013}$ are used in both computational biology and natural computing to investigate how the immune system adapts to its changing environments. However, researchers have struggled to build such systems at scale. For string-based AISs, finite state machines (FSMs) can store cell repertoires in compressed representations that are orders of magnitude smaller than explicitly stored receptor sets. This strategy allows AISs with billions of receptors to be generated in a matter of seconds. However, to date, these FSM-based AISs have been unable to deal with multiplicity in input data. Here, we show how weighted FSMs can be used to represent cell repertoires and model immunological processes like negative and positive selection, while also taking into account the multiplicity of input data. We use our method to build simple immune-inspired classifier systems that solve various toy problems in anomaly detection, showing how weights can be crucial for both performance and robustness to parameters. Our approach can potentially be extended to increase the scale of other population-based machine learning algorithms such as learning classifier systems.