Browse
Submit Data
Databases
API
Help

Dataset Information

21 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Mathematical modeling of the immune system recognition to mammary carcinoma antigen.

ABSTRACT: The definition of artificial immunity, realized through vaccinations, is nowadays a practice widely developed in order to eliminate cancer disease. The present paper deals with an improved version of a mathematical model recently analyzed and related to the competition between immune system cells and mammary carcinoma cells under the action of a vaccine (Triplex). The model describes in detail both the humoral and cellular response of the immune system to the tumor associate antigen and the recognition process between B cells, T cells and antigen presenting cells. The control of the tumor cells growth occurs through the definition of different vaccine protocols. The performed numerical simulations of the model are in agreement with in vivo experiments on transgenic mice.

SUBMITTER: Bianca C

PROVIDER: S-EPMC3521211 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Mathematical modeling of the immune system recognition to mammary carcinoma antigen.

Bianca Carlo C Chiacchio Ferdinando F Pappalardo Francesco F Pennisi Marzio M

BMC bioinformatics 20121213

The definition of artificial immunity, realized through vaccinations, is nowadays a practice widely developed in order to eliminate cancer disease. The present paper deals with an improved version of a mathematical model recently analyzed and related to the competition between immune system cells and mammary carcinoma cells under the action of a vaccine (Triplex). The model describes in detail both the humoral and cellular response of the immune system to the tumor associate antigen and the reco ...[more]

PMID: 23281916

Similar Datasets

Bianca2012 - Mathematical modeling of the immune system recognition to mammary carcinoma antigen

Project description:This model describes the humoral and cellular response of the immune system to a tumor associate antigen and the recognition process between B cells, T cells and antigen presenting cells. The model is used to analyze and investigate the competition between immune system cells and mammary carcinoma cells under the action of a vaccine.

2019-11-13 | MODEL1907260002 | BioModels

Mathematical modeling of Trypanosoma brucei transcription

Project description:Trypanosome RNA polymerase II transcription is polycistronic, individual mRNAs being excised by trans splicing and polyadenylation. In this study, we refined the previously published mathematical model for bloodstream form parasites and extended it to the procyclic form. We used the model, together with known mRNA half-lives, to predict the abundances of individual mRNAs, assuming rapid, unregulated mRNA processing; then we compared the results with measured mRNA abundances. Remarkably, the abundances of most mRNAs in procyclic forms are predicted quite well by the model, being largely explained by variations in mRNA decay rates and length. In bloodstream forms substantially more mRNAs are less abundant than predicted. We list mRNAs that are likely to show particularly slow or inefficient processing, either in both forms or with developmental regulation. We also measured ribosome occupancies of all mRNAs in trypanosomes grown in the same conditions as were used to measure mRNA turnover. In procyclic forms there was a weak positive correlation between ribosome density and mRNA half-life, suggesting cross-talk between translation and mRNA decay; ribosome density was related to the proportion of the mRNA on polysomes, indicating control of translation initiation. Ribosomal protein mRNAs in procyclics appeared to be exceptionally rapidly processed but poorly translated. Through this study, we conclude that lLevels of mRNAs in procyclic form trypanosomes are determined mainly by length and mRNA decay, with some control of precursor processing. In bloodstream forms variations in nuclear events play a larger role in transcriptome regulation, suggesting acquisition of new control mechanisms during adaptation to mammalian parasitism. Ribosome profiling and mRNA libraries were constructed in triplicate from in vitro PCF and in duplicate from in vitro T. brucei Lister427, to understand global differntial gene transcription.

2016-03-16 | E-GEOD-79208 | biostudies-arrayexpress

Mathematical modeling of tumor-immune cell interactions.

Project description:The anti-tumor activity of the immune system is increasingly recognized as critical for the mounting of a prolonged and effective response to cancer growth and invasion, and for preventing recurrence following resection or treatment. As the knowledge of tumor-immune cell interactions has advanced, experimental investigation has been complemented by mathematical modeling with the goal to quantify and predict these interactions. This succinct review offers an overview of recent tumor-immune continuum modeling approaches, highlighting spatial models. The focus is on work published in the past decade, incorporating one or more immune cell types and evaluating immune cell effects on tumor progression. Due to their relevance to cancer, the following immune cells and their combinations are described: macrophages, Cytotoxic T Lymphocytes, Natural Killer cells, dendritic cells, T regulatory cells, and CD4+ T helper cells. Although important insight has been gained from a mathematical modeling perspective, the development of models incorporating patient-specific data remains an important goal yet to be realized for potential clinical benefit.

| S-EPMC6579737 | biostudies-literature

Mathematical modeling of Trypanosoma brucei transcription

2016-03-16 | GSE79208 | GEO

Mathematical Modeling of Yarn Dynamics in a Generalized Twisting System.

Project description:Twisting is an important process to form a continuous yarn from short fibres and to determine the structure and properties of the resultant yarn. This paper proposes a new theoretical model of yarn dynamics in a generalized twisting system, which deals with two important phenomena simultaneously, that is, twist generation and twist propagation. Equations of yarn motion are established and the boundary value problems are numerically solved by Newton-Raphson method. The simulation results are validated by experiments and a good agreement has been demonstrated for the system with a moving rigid cylinder as the twisting element. For the first time, influences of several parameters on the twisting process have been revealed in terms of twist efficiency of the moving rigid cylinder, propagation coefficients of twist trapping and congestion. It was found that the wrap angle and yarn tension have large influence on the twisting process, and the yarn torsional rigidity varies with the twisting parameters.

| S-EPMC4832211 | biostudies-literature

Ten steps to investigate a cellular system with mathematical modeling.

Project description:Cellular and intracellular processes are inherently complex due to the large number of components and interactions, which are often nonlinear and occur at different spatiotemporal scales. Because of this complexity, mathematical modeling is increasingly used to simulate such systems and perform experiments in silico, many orders of magnitude faster than real experiments and often at a higher spatiotemporal resolution. In this article, we will focus on the generic modeling process and illustrate it with an example model of membrane lipid turnover.

| S-EPMC8118325 | biostudies-literature

Inflammation and immune system activation in aging: a mathematical approach.

Project description:Memory and learning declines are consequences of normal aging. Since those functions are associated with the hippocampus, I analyzed the global gene expression data from post-mortem hippocampal tissue of 25 old (age ? 60 yrs) and 15 young (age ? 45 yrs) cognitively intact human subjects. By employing a rigorous, multi-method bioinformatic approach, I identified 36 genes that were the most significant in terms of differential expression; and by employing mathematical modeling, I demonstrated that 7 of the 36 genes were able to discriminate between the old and young subjects with high accuracy. Remarkably, 90% of the known genes from those 36 most significant genes are associated with either inflammation or immune system activation. This suggests that chronic inflammation and immune system over-activity may underlie the aging process of the human brain, and that potential anti-inflammatory treatments targeting those genes may slow down this process and alleviate its symptoms.

| S-EPMC3832874 | biostudies-literature

Mathematical modeling provides kinetic details of the human immune response to vaccination.

Project description:With major advances in experimental techniques to track antigen-specific immune responses many basic questions on the kinetics of virus-specific immunity in humans remain unanswered. To gain insights into kinetics of T and B cell responses in human volunteers we combined mathematical models and experimental data from recent studies employing vaccines against yellow fever and smallpox. Yellow fever virus-specific CD8 T cell population expanded slowly with the average doubling time of 2 days peaking 2.5 weeks post immunization. Interestingly, we found that the peak of the yellow fever-specific CD8 T cell response was determined by the rate of T cell proliferation and not by the precursor frequency of antigen-specific cells as has been suggested in several studies in mice. We also found that while the frequency of virus-specific T cells increased slowly, the slow increase could still accurately explain clearance of yellow fever virus in the blood. Our additional mathematical model described well the kinetics of virus-specific antibody-secreting cell and antibody response to vaccinia virus in vaccinated individuals suggesting that most of antibodies in 3 months post immunization were derived from the population of circulating antibody-secreting cells. Taken together, our analysis provided novel insights into mechanisms by which live vaccines induce immunity to viral infections and highlighted challenges of applying methods of mathematical modeling to the current, state-of-the-art yet limited immunological data.

| S-EPMC4288384 | biostudies-literature

Mathematical Modeling of Oncolytic Virus Therapy Reveals Role of the Immune Response.

Project description:Oncolytic adenoviruses (OAds) present a promising path for cancer treatment due to their selectivity in infecting and lysing tumor cells and their ability to stimulate the immune response. In this study, we use an ordinary differential equation (ODE) model of tumor growth inhibited by oncolytic virus activity to parameterize previous research on the effect of genetically re-engineered OAds in A549 lung cancer tumors in murine models. We find that the data are best fit by a model that accounts for an immune response, and that the immune response provides a mechanism for elimination of the tumor. We also find that parameter estimates for the most effective OAds share characteristics, most notably a high infection rate and low viral clearance rate, that might be potential reasons for these viruses' efficacy in delaying tumor growth. Further studies observing E1A and P19 recombined viruses in different tumor environments may further illuminate the extent of the effects of these genetic modifications.

| S-EPMC10536413 | biostudies-literature

Mathematical modeling identifies Smad nucleocytoplasmic shuttling as a dynamic signal-interpreting system.

Project description:TGF-beta-induced Smad signal transduction from the membrane into the nucleus is not linear and unidirectional, but rather a dynamic network that couples Smad phosphorylation and dephosphorylation through continuous nucleocytoplasmic shuttling of Smads. To understand the quantitative behavior of this network, we have developed a tightly constrained computational model, exploiting the interplay between mathematical modeling and experimental strategies. The model simultaneously reproduces four distinct datasets with excellent accuracy and provides mechanistic insights into how the network operates. We use the model to make predictions about the outcome of fluorescence recovery after photobleaching experiments and the behavior of a functionally impaired Smad2 mutant, which we then verify experimentally. Successful model performance strongly supports the hypothesis of a dynamic maintenance of Smad nuclear accumulation during active signaling. The presented work establishes Smad nucleocytoplasmic shuttling as a dynamic network that flexibly transmits quantitative features of the extracellular TGF-beta signal, such as its duration and intensity, into the nucleus.

| S-EPMC2373357 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data