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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


Bhanwar Lal Puniya

Alyssa La Fleur

Ab Rauf Shah

Matteo Barberis

Tomáš Helikar


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.


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.

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
Public URL


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Copyright Statement
Copyright: © 2021 Wertheim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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