Dataset Information

Kareva2010 - Myeloid cells in tumour-immune interactions.

ABSTRACT: Myeloid cells in tumour-immune interactions. Kareva I1, Berezovskaya F, Castillo-Chavez C. Author information Abstract Despite highly developed specific immune responses, tumour cells often manage to escape recognition by the immune system, continuing to grow uncontrollably. Experimental work suggests that mature myeloid cells may be central to the activation of the specific immune response. Recognition and subsequent control of tumour growth by the cells of the specific immune response depend on the balance between immature (ImC) and mature (MmC) myeloid cells in the body. However, tumour cells produce cytokines that inhibit ImC maturation, altering the balance between ImC and MmC. Hence, the focus of this manuscript is on the study of the potential role of this inhibiting mechanism on tumour growth dynamics. A conceptual predator-prey type model that incorporates the dynamics and interactions of tumour cells, CD8(+) T cells, ImC and MmC is proposed in order to address the role of this mechanism. The prey (tumour) has a defence mechanism (blocking the maturation of ImC) that prevents the predator (immune system) from recognizing it. The model, a four-dimensional nonlinear system of ordinary differential equations, is reduced to a two-dimensional system using time-scale arguments that are tied to the maturation rate of ImC. Analysis shows that the model is capable of supporting biologically reasonable patterns of behaviour depending on the initial conditions. A range of parameters, where healing without external influences can occur, is identified both qualitatively and quantitatively.

SUBMITTER: Mohammad Umer Sharif Shohan

PROVIDER: MODEL2001310001 | BioModels | 2022-05-10

REPOSITORIES: BioModels

ACCESS DATA

Publications

Myeloid cells in tumour-immune interactions.

Kareva Irina I Berezovskaya Faina F Castillo-Chavez Carlos C

Journal of biological dynamics 20100701 4

Despite highly developed specific immune responses, tumour cells often manage to escape recognition by the immune system, continuing to grow uncontrollably. Experimental work suggests that mature myeloid cells may be central to the activation of the specific immune response. Recognition and subsequent control of tumour growth by the cells of the specific immune response depend on the balance between immature (ImC) and mature (MmC) myeloid cells in the body. However, tumour cells produce cytokine ...[more]

PMID: 22881128

Similar Datasets

Project description:This is the reduced model described in the article: A synthetic Escherichia coli predator–prey ecosystem Balagaddé FK, Song H, Ozaki J, Collins CH, Barnet M, Arnold FH, Quake SR, You L.Mol Syst Biol. 2008;4:187. Epub 2008 Apr 15. PMID: 18414488; DOI:10.1038/msb.2008.24 Abstract: We have constructed a synthetic ecosystem consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. Our synthetic ecosystem resembles canonical predator–prey systems in terms of logic and dynamics. The predator cells kill the prey by inducing expression of a killer protein in the prey, while the prey rescue the predators by eliciting expression of an antidote protein in the predator. Extinction, coexistence and oscillatory dynamics of the predator and prey populations are possible depending on the operating conditions as experimentally validated by long-term culturing of the system in microchemostats. A simple mathematical model is developed to capture these system dynamics. Coherent interplay between experiments and mathematical analysis enables exploration of the dynamics of interacting populations in a predictable manner. In the article the cell density is given in per 103 cells per microlitre. To evade a conversion factor in the SBML implementation, the unit for the cell densities was just left the same as for the AHLs A and A2 (nM). This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team. For more information see the terms of use. 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.

Project description:This model is based on the publication: "CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models". Odelaisy León-Triana, Soukaina Sabir, Gabriel F. Calvo, Juan Belmonte-Beitia, Salvador Chulián, Álvaro Martínez-Rubio, María Rosa, Antonio Pérez-Martínez, Manuel Ramirez-Orellana, Víctor M. Pérez-García doi: 10.1016/j.cnsns.2020.105570 Comment: This model is based on equations (4a)-(4c). Abstract: Immunotherapies use components of the patient immune system to selectively target can- cer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies –leukaemias and lymphomas–is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extract- ing the patient’s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19 + , which is expressed in these haematological cancers. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compart- ments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell ex- pansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19 + cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.

Dataset Information

Kareva2010 - Myeloid cells in tumour-immune interactions.

Publications

Myeloid cells in tumour-immune interactions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure