Control of human immune response function by T-cell population fluctuation and relaxation dynamics

TL;DR

This paper models human immune response fluctuations using a kinetic T-cell regulation model, revealing phase transition-like behavior and bistability that could aid in disease diagnosis.

Contribution

It introduces a fluctuation-based immune response function derived from a stochastic T-cell model, highlighting dynamical phase transitions and bistability in immune regulation.

Findings

Fluctuations differentiate healthy and diseased immune responses.

A divergence-like growth indicates a phase transition in T-cell regulation.

Bistability arises from intermittent T-cell fluctuations, affecting immune status.

Abstract

Clinical studies have indicated that in malignant surveillances fluctuations in the population of certain effector T-cell repertoire become suppressed. Motivated by such observations and in an attempt to quantify adaptive human response to pathogens, we define an immune response function (IMRF) in terms of mean square fluctuations of T-cell concentrations. We employ a recently developed kinetic model of T-cell regulation that contains the essential immunosuppressive effects of vitamin-D. We employ Gillespie algorithm to make the first study of fluctuations along the stochastic trajectories. This fluctuation-based IMRF can differentiate responses of different individuals after pathogenic incursion both under healthy and disease conditions. We find that relative fluctuations in T-cells (and hence IMRF) are different in strongly regulated (malignant prone) and weakly regulated (autoimmune prone) regions. The cross-over from one steady state (weakly regulated) to the other (strongly regulated) is accompanied by a divergence-like growth in the fluctuation of both the effector and regulatory T-cell concentration over a wide range of pathogenic stimulation, displaying a dynamical phase transition like behavior. The growth in fluctuation in this desired immune response regime is found to arise from an intermittent fluctuation between regulatory and effector T-cells that results in a bimodal distribution of population of each, indicating bistability. The signature of intermittent behavior is further confirmed by calculating the power spectrum of the corresponding fluctuation of time correlation function. The calculated time correlation functions of fluctuations show that the slow fluctuation causes the bistabilty in healthy state. Thus, in diseases diagnosis process, such steady state response parameters can provide immense information which might become helpful to define an immune status.