Evidence map›Paper›PMID 31001283›Full record

ArticleFrontiers in immunology2019

A Probabilistic Model of the Germinal Center Reaction.

Marcel Jan Thomas, Ulf Klein, John Lygeros, María Rodríguez Martínez

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 31 citations in OpenAlex.

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  9. Frontiers in immunology · 2024
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  11. Notch Signaling in Acute Inflammation and Sepsis.International journal of molecular sciences · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Marcel Jan ThomasIBM Research Zürich, Rüschlikon, Switzerland.
Ulf KleinExperimental Haematology, Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, United Kingdom.
John LygerosETH Zürich, Automatic Control Laboratory, Zurich, Switzerland.
María Rodríguez MartínezIBM Research Zürich, Rüschlikon, Switzerland.
IBM Research - Zurich · CHETH Zurich · CHSt James's University Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Germinal centers (GCs) are specialized compartments within the secondary lymphoid organs, where B cells proliferate, differentiate, and mutate their antibody genes. Upon exit from the GC, B cells terminally differentiate into plasma cells or memory B cells. While we have a good comprehension of plasma cell differentiation, memory B cell differentiation is still incompletely understood. In this paper, we extend previous models of the molecular events underlying B cell differentiation with new findings regarding memory B cell formation, and present a quantitative stochastic model of the intracellular and extracellular dynamics governing B cell maturation and exit from the GC. To simulate this model, we develop a novel extension to the Gillespie algorithm that enables the efficient stochastic simulation of the system, while keeping track of individual cell properties. Our model is able to explain the dynamical shift from memory B cell to plasma cell production over the lifetime of a GC. Moreover, our results suggest that B cell fate selection can be explained as a process that depends fundamentally on antigen affinity.

Indexed as

Immunologic MemoryLymphocyte ActivationModels, ImmunologicalAnimalsB-LymphocytesGerminal CenterHumansModels, Statisticalasymmetric centroblast divisionB cellgerminal centermemory B cell differentiationplasma cell differentiation

Identifiers

PMID31001283
PMCPMC6456718
OpenAlexW2927079200

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.