Dritschel2018 - A mathematical model of cytotoxic and helper T cell interactions in a tumour microenvironment
Ontology highlight
ABSTRACT:
SUBMITTER: 
Johannes Meyer
PROVIDER: BIOMD0000000763 | BioModels | 2019-07-26
REPOSITORIES: BioModels
Similar Datasets
Project description:We compared gene expression profiling between CD4+ helper T cells and CD8+ cytotoxic T cells CD4+ helper T cells vs CD8+ cytotoxic T cells
2015-06-30 | E-GEOD-67204 | ArrayExpress
Project description:This model gives a mathematical description of the interactions between tumor cells, cytotoxic T lymphocytes and helper T cells (HTCs) within the tumor microenvironment, with emphasis on the role played by HTCs. The effects and dynamics of adoptive cell immunotherapy and HTC recruitment are also specifically discussed.
2019-08-08 | BIOMD0000000783 | BioModels
Project description:This is a coupled ordinary differential equation model of tumour-immune dynamics, accounting for biological and clinical factors which regulate the interaction rates of cytotoxic T lymphocytes on the surface of the tumour mass.
2019-08-12 | BIOMD0000000787 | BioModels
Project description:Owen1998 - tumour treatment model
This model is described in the article:
Modelling the macrophage
invasion of tumours: effects on growth and composition.
Owen MR, Sherratt JA.
IMA J Math Appl Med Biol 1998 Jun;
15(2): 165-185
Abstract:
Even in the early stages of their development, tumours are
not simply a homogeneous grouping of mutant cells; rather, they
develop in tandem with normal tissue cells, and also recruit
other cell types including lymphatic cells and the endothelial
cells required for the development of a blood supply. It has
been repeatedly seen that macrophages form a significant
proportion of the tumour mass, and that they can have a variety
of effects upon the tumour, leading to a delicate balance
between growth promotion and inhibition. This paper develops a
model for the early, avascular growth of a tumour,
concentrating on the inhibitory effect of macrophages due to
their cytolytic activity. It is shown that such an immune
response is not sufficient to prevent growth, due to it being a
second-order process with respect to the density of the tumour
cells present. However, the presence of macrophages does have
important effects on the tumour composition, and the authors
perform a detailed bifurcation analysis of their model to
clarify this. An extended model is also considered which
incorporates addition of exogenous chemical regulators. In this
case, the model admits the possibility of tumour regression,
and the therapeutic implications of this are discussed.
This model is hosted on
BioModels Database
and identified by:
MODEL1708250002.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-06-13 | BIOMD0000000650 | BioModels
Project description:Owen1998 - tumour growth model
Deterministic model for the early,
avascular growth of a tumour, concentrating on the inhibitory
effect of macrophages.
This model is described in the article:
Modelling the macrophage
invasion of tumours: effects on growth and composition.
Owen MR, Sherratt JA.
IMA J Math Appl Med Biol 1998 Jun;
15(2): 165-185
Abstract:
Even in the early stages of their development, tumours are
not simply a homogeneous grouping of mutant cells; rather, they
develop in tandem with normal tissue cells, and also recruit
other cell types including lymphatic cells and the endothelial
cells required for the development of a blood supply. It has
been repeatedly seen that macrophages form a significant
proportion of the tumour mass, and that they can have a variety
of effects upon the tumour, leading to a delicate balance
between growth promotion and inhibition. This paper develops a
model for the early, avascular growth of a tumour,
concentrating on the inhibitory effect of macrophages due to
their cytolytic activity. It is shown that such an immune
response is not sufficient to prevent growth, due to it being a
second-order process with respect to the density of the tumour
cells present. However, the presence of macrophages does have
important effects on the tumour composition, and the authors
perform a detailed bifurcation analysis of their model to
clarify this. An extended model is also considered which
incorporates addition of exogenous chemical regulators. In this
case, the model admits the possibility of tumour regression,
and the therapeutic implications of this are discussed.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000670.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-06-13 | BIOMD0000000670 | BioModels
Project description:The paper describes a model of tumour-induced neoneurogenesis and perineural tumour growth.
Created by COPASI 4.25 (Build 207)
This model is described in the article:
Tumour-induced neoneurogenesis and perineural tumour growth: a mathematical approach
Georgios Lolas, Arianna Bianchi and Konstantinos N. Syrigos
Scientific Reports 6:20684
Abstract:
It is well-known that tumours induce the formation of a lymphatic and a blood vasculature around themselves. A similar but far less studied process occurs in relation to the nervous system and is referred to as neoneurogenesis. The relationship between tumour progression and the nervous system is still poorly understood and is likely to involve a multitude of factors. It is therefore relevant to study tumour-nerve interactions through mathematical modelling: this may reveal the most significant factors of the plethora of interacting elements regulating neoneurogenesis. The present work is a first attempt to model the neurobiological aspect of cancer development through a system of differential equations. The model confirms the experimental observations that a tumour is able to promote nerve formation/elongation around itself, and that high levels of nerve growth factor and axon guidance molecules are recorded in the presence of a tumour. Our results also reflect the observation that high stress levels (represented by higher norepinephrine release by sympathetic nerves) contribute to tumour development and spread, indicating a mutually beneficial relationship between tumour cells and neurons. The model predictions suggest novel therapeutic strategies, aimed at blocking the stress effects on tumour growth and dissemination.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide.
Please refer to CC0 Public Domain Dedication for more information.
2019-07-16 | BIOMD0000000750 | BioModels
Project description:We show here that the T helper-fate is not fixed and that mature antigen-stimulated CD4 T cells can switch off Thpok expression and reactivate CD8- lineage genes. This unexpected plasticity results in the post-thymic termination of the T helper- program and the functional differentiation of distinct MHC class II restricted CD4 cytotoxic T lymphocytes Small intestine CD4 intraepithelial T lymphocytes from ThPOK-GFP reposter mice were isolated and sorted (FACS Aria) based on ThPOK and CD8aa expression. Cell were isolated either from non-experimental ThPOK-GFP reporter mice (WT) or after transfering CD4 naive T cells from ThPOK-GFP reporter mice to RAG-/-recipient animals (TR) as an experimental colitis model. Experimet was done in duplicate.
2012-12-10 | E-GEOD-42277 | ArrayExpress
Project description:In response to infection, antigen-specific CD8+ T cells are primed in the T cell zone of secondary lymphoid organs and differentiate into cytotoxic effector T (TC) cells. Concurrently, CD4+ T cells differentiate into follicular helper T (TFH) cells that localize to B cell follicles and promote protective antibody responses. During unresolved infections, however, some viruses including human immunodeficiency virus (HIV) or Epstein–Barr virus (EBV) escape immune control and persist in TFH cells and B cells, respectively. Exclusion of Tc cells from B cell follicles is thought to be a major mechanism of immune evasion. New strategies are therefore needed to eradicate infected cells in follicles for a permanent cure. Using mouse infection models and human samples, we here identify a specialized group of TC cells expressing the chemokine receptor CXCR5 that can selectively enter B cell follicles and eradicate infected TFH and B cells. We demonstrate that differentiation of these cells, which we term follicular cytotoxic T (TFC) cells, requires the transcription factors Bcl6, E2A and Tcf1, whereas the transcriptional regulators Blimp1, Id3 and Id2 inhibit their development. We demonstrate that Blimp1 and E2A directly regulate Cxcr5 expression, and together with Bcl6 and Tcf1 form a transcriptional circuit that guides the TFC differentiation. The identification of a follicular subset of TC cells has far reaching implications for developing better strategies for the control of infections that target B cells and TFH cells and for the eradication of B cell derived malignancies. There is no associated input. The E2A Bio-ChIP-seq was performed with total thymocytes from Tcf3Bio/Bio Rosa26BirA/BirA mice
2016-08-01 | E-GEOD-84974 | ArrayExpress
Project description:This is an updated version of a previous model that described the dynamics of cancer treatment, with descriptions of tumour cell numbers, specific and non-specific immune cells (NK, CD8+ T cells and other lymphocytes) with inclusion of chemo- and immunotherapy. This model incorporates new data to provide an improved and more comprehensive model, with specific emphasis on better descriptions of IL-2 dynamics.
2019-08-05 | BIOMD0000000779 | BioModels
Project description:This a model from the article:
Specific therapy regimes could lead to long-term immunological control of HIV.
Wodarz D, Nowak MA. Proc Natl Acad Sci U S A
1999 Dec 7;96(25):14464-9 10588728
,
Abstract:
We use mathematical models to study the relationship between HIV and the immune
system during the natural course of infection and in the context of different
antiviral treatment regimes. The models suggest that an efficient cytotoxic T
lymphocyte (CTL) memory response is required to control the virus. We define CTL
memory as long-term persistence of CTL precursors in the absence of antigen.
Infection and depletion of CD4(+) T helper cells interfere with CTL memory
generation, resulting in persistent viral replication and disease progression.
We find that antiviral drug therapy during primary infection can enable the
development of CTL memory. In chronically infected patients, specific treatment
schedules, either including deliberate drug holidays or antigenic boosts of the
immune system, can lead to a re-establishment of CTL memory. Whether such
treatment regimes would lead to long-term immunologic control deserves
investigation under carefully controlled conditions.
This model was taken from the CellML repository
and automatically converted to SBML.
The original model was:
Wodarz D, Nowak MA. (1999) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information.
In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2010-06-25 | BIOMD0000000683 | BioModels