A multi-approach and multi-scale platform to model CD4+ T cells responding to infections

Abstract

Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node’s ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology.

Recent applications of quantitative systems pharmacology and machine learning models across diseases

“…Knowing the complexity of T cell biology, a recent study integrated four different modeling approaches to build three different scales (e.g. signaling, metabolism and cellular) capturing the essential biological phenomena of T cell biology [ 143 ]. Understanding the importance of different T cell populations in disease, QSP models can address the dynamic of cell development to predict biomarkers and conditions where the cellular balance is disrupted.…”

Mathematical Modeling of Proliferative Immune Response Initiated by Interactions Between Classical Antigen-Presenting Cells Under Joint Antagonistic IL-2 and IL-4 Signaling

“…The parameter κ defines the downregulation of IL-2R by IL-4. multi-approach and multi-scale modeling framework that can be used to model diverse immune responses at molecular, cellular, and systemic scales (Wertheim et al, 2021). In this study, cell population dynamics and systemic cytokine concentrations were described by ODEs (Wertheim et al, 2021).…”

“…multi-approach and multi-scale modeling framework that can be used to model diverse immune responses at molecular, cellular, and systemic scales (Wertheim et al, 2021). In this study, cell population dynamics and systemic cytokine concentrations were described by ODEs (Wertheim et al, 2021). Excitingly, their model demonstrates the dynamics of CD4+ T cells in response to influenza infections in different cytokine milieus, considering heterogeneous populations of T helper cells and 11 cytokines in three spatial compartments (an infection site or target organ, lymphoid tissues, and a circulatory system).…”

A multiscale mechanistic model of human dendritic cells for in-silico investigation of immune responses and novel therapeutics discovery

“…Multiscale models have the potential to uncover the underlying mechanisms behind emergent behaviors at various scales such as intracellular, cellular, and systemic levels. Such models can consider various temporal and organizational scales including signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport ( 25 ). Different multiscale models have been developed to study the dynamic response of DC under different extracellular environments.…”