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Intensified Doxorubicin-Based Regimen Efficacy in Residual Non-Hodgkin's Lymphoma Disease: Towards a Computationally Supported Treatment Improvement

Y. Kogan, B. Ribba, K. Marron, N. Dahan, V. Vainstein, Z. Agur (2010)

Mathematical Modelling of Natural Phenomena

Despite recent advances, treatment of patients with aggressive Non-Hodgkin's lymphoma (NHL2) has yet to be optimally designed. Notwithstanding the contribution of molecular treatments, intensification of chemotherapeutic regimens may still be beneficial. Hoping to aid in the design of intensified chemotherapy, we put forward a mathematical and computational model that analyses the effect of Doxorubicin on NHL over a wide range of patho-physiological conditions. The model represents tumour growth...

Intracellular Modelling of Cell-Matrix Adhesion during Cancer Cell Invasion

V. Andasari, M.A.J. Chaplain (2012)

Mathematical Modelling of Natural Phenomena

When invading the tissue, malignant tumour cells (i.e. cancer cells) need to detach from neighbouring cells, degrade the basement membrane, and migrate through the extracellular matrix. These processes require loss of cell-cell adhesion and enhancement of cell-matrix adhesion. In this paper we present a mathematical model of an intracellular pathway for the interactions between a cancer cell and the extracellular matrix. Cancer cells use similar...

Kermack-McKendrick epidemic model revisited

Josef Štěpán, Daniel Hlubinka (2007)

Kybernetika

This paper proposes a stochastic diffusion model for the spread of a susceptible-infective-removed Kermack–McKendric epidemic (M1) in a population which size is a martingale N t that solves the Engelbert–Schmidt stochastic differential equation (). The model is given by the stochastic differential equation (M2) or equivalently by the ordinary differential equation (M3) whose coefficients depend on the size N t . Theorems on a unique strong and weak existence of the solution to (M2) are proved and computer...

Kermack-McKendrick epidemics vaccinated

Jakub Staněk (2008)

Kybernetika

This paper proposes a deterministic model for the spread of an epidemic. We extend the classical Kermack–McKendrick model, so that a more general contact rate is chosen and a vaccination added. The model is governed by a differential equation (DE) for the time dynamics of the susceptibles, infectives and removals subpopulation. We present some conditions on the existence and uniqueness of a solution to the nonlinear DE. The existence of limits and uniqueness of maximum of infected individuals are...

Mean-Field Optimal Control

Massimo Fornasier, Francesco Solombrino (2014)

ESAIM: Control, Optimisation and Calculus of Variations

We introduce the concept of mean-field optimal control which is the rigorous limit process connecting finite dimensional optimal control problems with ODE constraints modeling multi-agent interactions to an infinite dimensional optimal control problem with a constraint given by a PDE of Vlasov-type, governing the dynamics of the probability distribution of interacting agents. While in the classical mean-field theory one studies the behavior of a large number of small individuals freely interacting...

Model of AIDS-related tumour with time delay

Marek Bodnar, Urszula Foryś, Zuzanna Szymańska (2009)

Applicationes Mathematicae

We present and compare two simple models of immune system and cancer cell interactions. The first model reflects simple cancer disease progression and serves as our "control" case. The second describes the progression of a cancer disease in the case of a patient infected with the HIV-1 virus.

Modeling the Cancer Stem Cell Hypothesis

C. Calmelet, A. Prokop, J. Mensah, L. J. McCawley, P. S. Crooke (2010)

Mathematical Modelling of Natural Phenomena

Solid tumors and hematological cancers contain small population of tumor cells that are believed to play a critical role in the development and progression of the disease. These cells, named Cancer Stem Cells (CSCs), have been found in leukemia, myeloma, breast, prostate, pancreas, colon, brain and lung cancers. It is also thought that CSCs drive the metastatic spread of cancer. The CSC compartment features a specific and phenotypically defined cell...

Modelling the spiders ballooning effect on the vineyard ecology

E. Venturino, M. Isaia, F. Bona, E. Issoglio, V. Triolo, G. Badino (2010)

Mathematical Modelling of Natural Phenomena

We consider an ecosystem in which spiders may be transported by the wind from vineyards into the surrounding woods and vice versa. The model takes into account this tranport phenomenon without building space explicitly into the governing equations. The equilibria of the dynamical system are analyzed together with their stability, showing that bifurcations may occur. Then the effects of indiscriminated spraying to keep pests under control is also investigated via suitable simulations.

Modelling Tuberculosis and Hepatitis B Co-infections

S. Bowong, J. Kurths (2010)

Mathematical Modelling of Natural Phenomena

Tuberculosis (TB) is the leading cause of death among individuals infected with the hepatitis B virus (HBV). The study of the joint dynamics of HBV and TB present formidable mathematical challenges due to the fact that the models of transmission are quite distinct. We formulate and analyze a deterministic mathematical model which incorporates of the co-dynamics of hepatitis B and tuberculosis. Two sub-models, namely: HBV-only and TB-only sub-models...

Monte Carlo simulation and analytic approximation of epidemic processes on large networks

Noémi Nagy, Péter Simon (2013)

Open Mathematics

Low dimensional ODE approximations that capture the main characteristics of SIS-type epidemic propagation along a cycle graph are derived. Three different methods are shown that can accurately predict the expected number of infected nodes in the graph. The first method is based on the derivation of a master equation for the number of infected nodes. This uses the average number of SI edges for a given number of the infected nodes. The second approach is based on the observation that the epidemic...

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