Dataset Information

Bose2011 - Noise-assisted interactions of tumor and immune cells

ABSTRACT: Noise-assisted interactions of tumor and immune cells. Bose T1, Trimper S. Author information 1 Institute of Physics, Martin-Luther-University, D-06099 Halle, Germany. thomas.bose@physik.uni-halle.de Abstract We consider a three-state model comprising tumor cells, effector cells, and tumor-detecting cells under the influence of noises. It is demonstrated that inevitable stochastic forces existing in all three cell species are able to suppress tumor cell growth completely. Whereas the deterministic model does not reveal a stable tumor-free state, the auto-correlated noise combined with cross-correlation functions can either lead to tumor-dormant states, tumor progression, as well as to an elimination of tumor cells. The auto-correlation function exhibits a finite correlation time τ, while the cross-correlation functions shows a white-noise behavior. The evolution of each of the three kinds of cells leads to a multiplicative noise coupling. The model is investigated by means of a multivariate Fokker-Planck equation for small τ. The different behavior of the system is, above all, determined by the variation of the correlation time and the strength of the cross-correlation between tumor and tumor-detecting cells. The theoretical model is based on a biological background discussed in detail, and the results are tested using realistic parameters from experimental observations.

SUBMITTER: Mohammad Umer Sharif Shohan

PROVIDER: BIOMD0000000894 | BioModels | 2024-09-02

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	BIOMD0000000894?filename=Bose2011-biopax2.owl	Owl
	BIOMD0000000894?filename=Bose2011-biopax3.owl	Owl
	BIOMD0000000894?filename=Bose2011-matlab.m	Other
	BIOMD0000000894?filename=Bose2011-octave.m	Other
	BIOMD0000000894?filename=Bose2011.cps	Other

Items per page:

1 - 5 of 12

Publications

Noise-assisted interactions of tumor and immune cells.

Bose Thomas T Trimper Steffen S

Physical review. E, Statistical, nonlinear, and soft matter physics 20110823 2 Pt 1

We consider a three-state model comprising tumor cells, effector cells, and tumor-detecting cells under the influence of noises. It is demonstrated that inevitable stochastic forces existing in all three cell species are able to suppress tumor cell growth completely. Whereas the deterministic model does not reveal a stable tumor-free state, the auto-correlated noise combined with cross-correlation functions can either lead to tumor-dormant states, tumor progression, as well as to an elimination ...[more]

PMID: 21929038

Similar Datasets

Project description:Yuraszeck2010 - Vulnerabilities in the Tau Network in Tau Pathophysiology This model is described in the article: Vulnerabilities in the tau network and the role of ultrasensitive points in tau pathophysiology. Yuraszeck TM, Neveu P, Rodriguez-Fernandez M, Robinson A, Kosik KS, Doyle FJ 3rd. PLoS Comput. Biol. 2010; 6(11): e1000997 Abstract: The multifactorial nature of disease motivates the use of systems-level analyses to understand their pathology. We used a systems biology approach to study tau aggregation, one of the hallmark features of Alzheimer's disease. A mathematical model was constructed to capture the current state of knowledge concerning tau's behavior and interactions in cells. The model was implemented in silico in the form of ordinary differential equations. The identifiability of the model was assessed and parameters were estimated to generate two cellular states: a population of solutions that corresponds to normal tau homeostasis and a population of solutions that displays aggregation-prone behavior. The model of normal tau homeostasis was robust to perturbations, and disturbances in multiple processes were required to achieve an aggregation-prone state. The aggregation-prone state was ultrasensitive to perturbations in diverse subsets of networks. Tau aggregation requires that multiple cellular parameters are set coordinately to a set of values that drive pathological assembly of tau. This model provides a foundation on which to build and increase our understanding of the series of events that lead to tau aggregation and may ultimately be used to identify critical intervention points that can direct the cell away from tau aggregation to aid in the treatment of tau-mediated (or related) aggregation diseases including Alzheimer's. This model is hosted on BioModels Database and identified by: BIOMD0000000542. 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.

Project description:Background: The microtubule-associated protein Tau has attracted a diverse and ever-increasing research interest, with the protein being mentioned in the title/abstract of nearly 34,000 PubMed-indexed publications to-date. To facilitate studies into Tau biology and its role in neurodegeneration, a multitude of Tau-targeting antibodies have been developed, with hundreds of distinct antibody clones currently available for purchase. Nevertheless, concerns over antibody specificity and limited understanding of the performance of many of these reagents has hindered research into the protein. Methods: We have characterised the performance and specificity of 53 commercially-available Tau antibodies by Western blot, 35 of which were additionally profiled by immunohistochemistry. Antibodies were tested for their reactivity to both murine and human samples, with supporting data generated by mass spectrometry-based detection. Results: We show that presumed Tau knockout human cells continue to express residual protein, providing evidence of Tau isoforms generated by exon skipping. Our data also reveals that the binding of several antibodies presumed to detect Tau independently of any post translational modifications (i.e. “total” Tau antibodies, as well as an antibody targeting Asp421-cleaved Tau), were partially inhibited by phosphorylation. Moreover, we found that several total and phospho-Tau antibodies cross-reacted with MAP2 isoforms, while the “oligomer-specific” T22 antibody detected monomeric Tau on Western blot. With one exception, the phosphorylation-dependent Tau antibodies tested were found to not cross-react with the unphosphorylated protein. Importantly, several total and isoform-specific Tau antibodies failed to detect Tau expressed at low endogenous levels, highlighting the importance of antibody choice for studying physiological Tau expression, especially in non-neuronal cells and peripheral tissues. Conclusions: We identify Tau antibodies across all categories (total, PTM-dependent and isoform-specific) that can be employed in Western blotting and/or immunohistochemistry applications to reliably detect even low levels of Tau expression with high specificity. This work represents an extensive resource that serves as a point of reference for future studies with respect to anti-Tau antibody use and development.