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

Wertheim, Kenneth Y.; Puniya, Bhanwar Lal; La Fleur, Alyssa; Shah, Ab Rauf; Barberis, Matteo; Helikar, Tomáš

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.

Citation

Wertheim, K. Y., Puniya, B. L., La Fleur, A., Shah, A. R., Barberis, M., & Helikar, T. (2021). A multi-approach and multi-scale platform to model CD4+ T cells responding to infections. PLoS Computational Biology, 17(8), Article e1009209. https://doi.org/10.1371/journal.pcbi.1009209

• Journal Article Type: Article
• Acceptance Date: Jun 23, 2021
• Online Publication Date: Aug 3, 2021
• Publication Date: Aug 1, 2021
• Deposit Date: Jan 23, 2023
• Publicly Available Date: Jan 23, 2023
• Journal: PLoS Computational Biology
• Print ISSN: 1553-734X
• Electronic ISSN: 1553-7358
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 8
• Article Number: e1009209
• DOI: https://doi.org/10.1371/journal.pcbi.1009209
• Public URL: https://hull-repository.worktribe.com/output/4186873