Speaker
Description
Immune responses are tightly regulated by a diverse set of interacting immune-cell populations, and immune-cell communication pathways are routinely targeted by immunotherapy in autoimmune diseases and cancer. However, a quantitative understanding of immune-cell regulation in vivo is only beginning to emerge [1]. We assessed cytokine reaction-diffusion dynamics using a 4D finite-element based modeling framework [2]. We found that spatial cytokine gradients robustly arise in physiological parameter regimes and are critical for effective paracrine signaling, and they do not arise by diffusion and uptake alone, but rather depend on properties of the cell population such as an all-or-none behavior of cytokine secreting cells. Next, we explored the effect of cytokine gradients in the context of motile cell populations in the lymph node and the tumor microenvironment, employing both cellular Potts models and a numerically efficient formulation of cytokine gradients based on a homogenization approach. Further, we developed a general mathematical framework for analysis of interactive immune-cell population dynamics, accounting for cell-cell interactions, cell-proliferation, and cell-differentiation by means of measurable response-time distributions [3]. We employed that framework to describe the onset of lupus nephritis, the renal manifestation of systemic lupus erythematosus (SLE) [4]. Starting from analysis of single-cell RNA-sequencing data on innate lymphoid cells, our mathematical model reconciles that experimental data with long-standing clinical observations such as elevated autoantibodies and interferons in individuals with genetic predisposition for SLE. Overall, our results from model simulations and data analysis highlight the complex dynamics imposed by cell-cell signaling networks in the immune system, with implications for therapeutic intervention.
Bibliography
@ARTICLE{Steinheuer2025-ta,
title = "Untangling cell--cell communication networks and on-treatment
response in immunotherapy",
author = "Steinheuer, Lisa Maria and Kl{\"u}mper, Niklas and Bald, Tobias
and Thurley, Kevin",
journal = "Curr. Opin. Syst. Biol.",
publisher = "Elsevier BV",
volume = 40,
number = 100534,
pages = "100534",
month = mar,
year = 2025,
copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
language = "en"
}
@ARTICLE{Brunner2024-pf,
title = "Diffusion-limited cytokine signaling in {T} cell populations",
author = "Brunner, Patrick and Kiwitz, Lukas and Li, Lisa and Thurley,
Kevin",
abstract = "Effective immune-cell responses depend on collective
decision-making mediated by diffusible intercellular signaling
proteins called cytokines. Here, we designed a three-dimensional
spatiotemporal modeling framework and a precise finite-element
simulation setup to systematically investigate the origin and
consequences of spatially inhomogeneous cytokine distributions
in lymph nodes. We found that such inhomogeneities are critical
for effective paracrine signaling, and they do not arise by
diffusion and uptake alone, but rather depend on properties of
the cell population such as an all-or-none behavior of cytokine
secreting cells. Furthermore, we assessed the regulatory
properties of negative and positive feedback in combination with
diffusion-limited signaling dynamics, and we derived statistical
quantities to characterize the spatiotemporal signaling
landscape in the context of specific tissue architectures.
Overall, our simulations highlight the complex spatiotemporal
dynamics imposed by cell-cell signaling with diffusible ligands,
which entails a large potential for fine-tuned biological
control especially if combined with feedback mechanisms.",
journal = "iScience",
publisher = "Elsevier BV",
volume = 27,
number = 6,
pages = "110134",
month = jun,
year = 2024,
keywords = "Cell biology; Immunology; Mathematical biosciences; Molecular
biology",
copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
language = "en"
}
@ARTICLE{Burt2023-ke,
title = "Distribution modeling quantifies collective {TH} cell decision
circuits in chronic inflammation",
author = "Burt, Philipp and Thurley, Kevin",
abstract = "Immune responses are tightly regulated by a diverse set of
interacting immune cell populations. Alongside decision-making
processes such as differentiation into specific effector cell
types, immune cells initiate proliferation at the beginning of
an inflammation, forming two layers of complexity. Here, we
developed a general mathematical framework for the data-driven
analysis of collective immune cell dynamics. We identified
qualitative and quantitative properties of generic network
motifs, and we specified differentiation dynamics by analysis of
kinetic transcriptome data. Furthermore, we derived a specific,
data-driven mathematical model for T helper 1 versus T
follicular helper cell-fate decision dynamics in acute and
chronic lymphocytic choriomeningitis virus infections in mice.
The model recapitulates important dynamical properties without
model fitting and solely by using measured response-time
distributions. Model simulations predict different windows of
opportunity for perturbation in acute and chronic infection
scenarios, with potential implications for optimization of
targeted immunotherapy.",
journal = "Sci. Adv.",
publisher = "American Association for the Advancement of Science (AAAS)",
volume = 9,
number = 37,
pages = "eadg7668",
month = sep,
year = 2023,
language = "en"
}
@ARTICLE{Kreider2026-rv,
title = "Amplification cycles through innate lymphoid cells at the onset
of lupus nephritis",
author = "Kreider, Rosa L and Biniaris-Georgallis, Stylianos-Iason and
Grothey, Bastian and Triantafyllopoulou, Antigoni and
Steinheuer, Lisa M and Thurley, Kevin",
abstract = "Disease progression in autoimmune conditions such as systemic
lupus erythematosus (SLE) is highly heterogeneous, and the
cellular and molecular mechanisms driving disease-onset dynamics
remain incompletely understood. Here, based on single-cell
transcriptomics data on lupus-prone NZB/W F1 mice, we derived a
mathematical cell-cell interaction model recapitulating early
dynamics of innate immune cells in lupus nephritis. We
identified a diverse landscape of tissue-associated ILC and
vessel-associated NK cell populations. We conceived a scalable
mathematical framework for analysis of immune-cell interaction
dynamics. A specific model formulation considers ILC as
amplifiers of inflammatory processes in the presence of
autoantibodies in lupus-prone individuals. Systematic model
analyses highlight the impact of positive feedback loops and
spontaneous inflammatory events or environmental stimuli, and
the timing-dependent effectiveness of depletion therapies.
Additionally, our model links the critical role of ILC
populations to hallmarks of SLE such as highly heterogeneous
disease dynamics. Overall, our findings lay the groundwork
towards a mathematical model of immune-tissue cellular
crosstalk, enabling quantification of disease severity and
prediction of responses to biologic treatments in autoimmune
diseases.",
journal = "Front. Immunol.",
publisher = "Frontiers Media SA",
volume = 17,
number = 1756560,
pages = "1756560",
month = mar,
year = 2026,
keywords = "NZB/W F1 mice; autoantibodies; autoimmune disease; cell-cell
communication; mathematical model; response-time modeling;
single-cell transcriptomics",
copyright = "https://creativecommons.org/licenses/by/4.0/",
language = "en"
}
