How to Simulate a Germinal Center

Robert, Philippe A.; Rastogi, Ananya; Binder, Sebastian; Meyer‐Hermann, Michael

doi:10.1007/978-1-4939-7095-7_22

Cited by 23 publications

(19 citation statements)

References 25 publications

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“…Many models are phenotypic in that they lack precise molecular and cellular details, so they cannot readily be informed by-and their outputs compared with-experimental and patient data [24]. Some modeling studies used detailed agents but each focused on a given compartment such as germinal centers [25]. A few agent-based models were built by including different immune cell types in multi-compartment setups not dissimilar to the one presented in this paper [26,27].…”

Section: Discussionmentioning

confidence: 99%

Multi-Approach and Multi-Scale Model of CD4+ T Cells Predicts Switch-Like and Oscillatory Emergent Behaviors in Inflammatory Response to Infection

Wertheim

Puniya

Fleur

et al. 2020

Preprint

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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 taking place 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. Our technology helps capture emergent behaviors that arise from nonlinear dynamics interwoven across three scales. Multi-scale insights added to single-scale studies allowed us to identify switch-like and oscillatory behaviors of CD4+ T cells at the population level, which are both novel and immunologically important. We envision our model and the generic framework encompassing it to become the foundation of a more comprehensive model of the human immune system. Immune responses mediated by CD4+ T cells involve complex interactions among immune cells and molecules.Resting CD4+ T cells are activated by antigen-presenting cells and cytokines, further differentiate, and secrete cytokines to act against pathogens and abnormal cells. They also recruit other immune cells to the sites of infection. Depending on the cytokine milieu, activated CD4+ T cells may differentiate into various phenotypes including T helper type 1 (Th1), T helper type 2 (Th2), T helper type 17 (Th17), and induced T regulatory cells (Tregs) [1]. To produce the energy and molecular precursors required to achieve a specific mixture of phenotypes, activated CD4+ T cells utilize certain signaling and metabolic pathways such as aerobic glycolysis [1,2]. To fully understand these complex interactions underlying the dynamics of CD4+ T cell immune response, we must integrate events taking place at various spatial, temporal, and organizational scales, such as immune cell proliferation, development, and migration; cell-cell and cell-molecule interactions; intracellular signaling; and intracellular metabolism.Multi-scale modeling aims to integrate spatial, temporal, and organizational scales of biological systems. This strategy has been used extensively in immunology. For instance, multi-scale models have been developed to study infections and inflammatory processes [3], and the immune response to the Helicobacter pylori infection [4]. Such integration could be achieved by combining different modeling approaches, such as ordinary differential equations (ODEs) and partial differential equations (PDEs) for the chemical kinetics and transport of molecular species (in terms of concentrations) in and across different cells, organs or tissues; agent-based modeling (ABM) for cell...

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Section: Discussionmentioning

confidence: 99%

Multi-Approach and Multi-Scale Model of CD4+ T Cells Predicts Switch-Like and Oscillatory Emergent Behaviors in Inflammatory Response to Infection

Wertheim

Puniya

Fleur

et al. 2020

Preprint

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“…seconds) compared to [37]. It means, for a maximal affinity, corresponding to a capture probability of 1, an antigen can be captured every 3.6 seconds.…”

Section: D-lattice Representation Of Proteins Proteins Structures Amentioning

confidence: 99%

A 3D structural affinity model for multi-epitope in silico germinal center simulations

Meyer‐Hermann

2019

Preprint

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Antibody-based immunotherapies require the tedious identification and development of antibodies with specific properties. In particular, vaccine development for mutating pathogens is challenged by their fast evolution, the complexity of immunodominance, and the heterogeneous immune history of individuals. Mathematical models are critical for predicting successful vaccine conditions or designing potent antibodies. Existing models are limited by their abstract and poorly structural representations of antigen epitopes. Here, we propose a structural lattice-based model for antibody-antigen affinity. An efficient algorithm is given that predicts the best binding structure of an antibody's amino acid sequence around an antigen with shortened computational time. It is suitable for large simulations of affinity maturation. This structural representation contains key physiological properties, such as affinity jumps and cross-reactivity, and successfully reflects the topology of antigen epitopes, such as pockets and shielded residues. We perform in silico immunizations via germinal center simulations and show that our model can explain complex phenomena like recognition of the same epitope from unrelated clones. We show that the use of cocktails of similar epitopes promotes the development of cross-reactive antibodies. This model opens a new avenue for optimizing multivalent vaccines with combined cocktails or sequential immunizations, and to reveal reasons for vaccine success or failure on a structural basis.

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“…Such models can be more complex and need not have an efficiently-computable likelihood to be useful. Agent-based models, such as models of a germinal center (26) fall into this category. Models can make predictions, such as the groundbreaking 1993 prediction of cyclic re-entry (30) that was dramatically validated over a decade later (31, 32).…”

Section: Modelsmentioning

confidence: 99%

“…For example, it’s not possible to calculate likelihoods for complex models based on agent-based simulation (26), although one could sample them using ABC. In fact, an informal version of ABC is currently used in B cell receptor sequence analysis, in which one adjusts simulation parameters until they generate data that looks close to experimental data according to a battery of summary statistics (27–29).…”

Section: Introductionmentioning

confidence: 99%

The Bayesian optimist's guide to adaptive immune receptor repertoire analysis

Olson

Matsen

2018

Immunological Reviews

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Probabilistic modeling is fundamental to the statistical analysis of complex data. In addition to forming a coherent description of the data-generating process, probabilistic models enable parameter inference about given datasets. This procedure is well developed in the Bayesian perspective, in which one infers probability distributions describing to what extent various possible parameters agree with the data. In this paper, we motivate and review probabilistic modeling for adaptive immune receptor repertoire data then describe progress and prospects for future work, from germline haplotyping to adaptive immune system deployment across tissues. The relevant quantities in immune sequence analysis include not only continuous parameters such as gene use frequency but also discrete objects such as B-cell clusters and lineages. Throughout this review, we unravel the many opportunities for probabilistic modeling in adaptive immune receptor analysis, including settings for which the Bayesian approach holds substantial promise (especially if one is optimistic about new computational methods). From our perspective, the greatest prospects for progress in probabilistic modeling for repertoires concern ancestral sequence estimation for B-cell receptor lineages, including uncertainty from germline genotype, rearrangement, and lineage development.

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How to Simulate a Germinal Center

Cited by 23 publications

References 25 publications

Multi-Approach and Multi-Scale Model of CD4+ T Cells Predicts Switch-Like and Oscillatory Emergent Behaviors in Inflammatory Response to Infection

Multi-Approach and Multi-Scale Model of CD4+ T Cells Predicts Switch-Like and Oscillatory Emergent Behaviors in Inflammatory Response to Infection

A 3D structural affinity model for multi-epitope in silico germinal center simulations

The Bayesian optimist's guide to adaptive immune receptor repertoire analysis

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