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PUBLIC RELEASE DATE:
25-Jun-2014

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Contact: Karthika Muthukumaraswamy
karthika@siam.org
267-350-6383
Society for Industrial and Applied Mathematics
@SIAMconnect

Using math to analyze movement of cells, organisms, and disease

IMAGE: The atomic structure of the bluetongue virus core is shown. Ramsol image of PDB 2BTV by Dr. J.-Y. Sgro, UW-Madison.

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Traveling waves model tumor invasion

Cell migration, which is involved in wound healing, cancer and tumor growth, and embryonic growth and development, has been a topic of interest to mathematicians and biologists for decades.

In a paper published recently in the SIAM Journal on Applied Dynamical Systems, authors Kristen Harley, Peter van Heijster, Robert Marangell, Graeme Pettet, and Martin Wechselberger study a model describing cell invasion through directional outgrowth or movement in the context of malignant tumors, in particular, melanoma or skin cancer. Tumor cells move up a gradient, based on the presence of a chemical or chemoattractant - this process is called haptotaxis. Receptors on the exterior of cell walls detect and allow passing of the chemoattractant. Based on the locations of these receptors, cells determine the most favorable migration direction.

Continuum mathematical models that describe cell migration usually give rise to traveling waves--waves in which the medium moves in the direction of propagation at a constant speed. In this paper, the authors prove the existence and uniqueness of traveling waves to the model of malignant tumor invasion. The model described takes into account the speed of the traveling waves, which corresponds to the rate of invasion of cells, as well as the extracellular matrix concentration (the medium surrounding cells that provides structural and biochemical support to cells). Movement of cells through diffusion is omitted as it is shown to play a relatively small role in the migration process.

View the paper: Existence of Traveling Wave Solutions for a Model of Tumor Invasion http://epubs.siam.org/doi/abs/10.1137/130923129 SIAM Journal on Applied Dynamical Systems, 13 (1), 366-396


Mathematically modeling species dispersal Dispersal is an ecological process involving the movement of an organism or multiple organisms away from their birth site to another location or population where they settle and reproduce. An important topic in ecology and evolutionary biology, dispersal can either be random or directed. Random movement, as the name indicates, describes dispersal patterns that are unbiased and random, whereas directed or biased movement occurs when organisms sense and respond to local environmental cues by moving directionally. Dispersal is dependent on a variety of factors such as climate, food, and predators, and is often biased.

Fitness-dependent dispersal is a type of biased dispersal; the fitness of a species is given by its per capita growth rate. In many mathematical models of fitness-dependent dispersal, movement of organisms into and out of an area or region depends on the fitness differences between the organisms' resident patch and other patches in the habitat, and there is a net movement from patches of lower to higher fitness. In a recent paper published in the SIAM Journal on Mathematical Analysis, authors Yuan Lou, Youshan Tao, and Michael Winkler propose a continuous-time and continuous-space reaction diffusion model for fitness-dependent dispersal where the species moves upward along its fitness gradient.

In ecology, ideal free distribution refers to the way in which organisms distribute themselves among patches proportional to the amount of resources available in each patch. Such a distribution minimizes resource competition and maximizes fitness. Thus it is natural to expect that dispersal strategies leading to ideal free distribution of populations would be favored over the course of evolution. The authors, in this paper, determine that fitness-dependent dispersal conveys advantages to approaching such ideal free distribution.

View the paper: Approaching the Ideal Free Distribution in Two-Species Competition Models with Fitness-Dependent Dispersal http://epubs.siam.org/doi/abs/10.1137/130934246 SIAM Journal on Mathematical Analysis, 46 (2), 1228-1262


A model for Bluetongue disease dynamics in cattle

In a paper recently published in the SIAM Journal on Mathematical Analysis, authors Stephen Gourley, Gergely Röst, and Horst Thieme model disease persistence of a virus called Bluetongue using a system of several delay differential equations. The disease affects sheep and cattle, and is spread by biting midges. In sheep, the bluetongue virus can cause abortion, congenital abnormalities and death, though mild cases completely recover. In cattle, bluetongue does not generally cause death.

The basic reproduction number for a disease is defined as the expected number of secondary cases produced by a single infection in a susceptible population. As in many infectious disease models, uniform disease persistence of bluetongue occurs if the basic reproduction number for the whole system exceeds one. But an additional factor influences the disease state in the case of this disease, which is that it affects sheep much more severely than cattle. As a result, uniform disease persistence can occur in two different scenarios. If the disease reproduction number for the cattle-midge-bluetongue system with or without sheep is greater than one, bluetongue persists in cattle and midges even though it may eradicate the sheep, relying on cattle as a reservoir. In the second situation, where the reproduction number of all host and vector species coexisting is greater than one, while the reproduction number for the cattle-midge-bluetongue system (without sheep) is less than one, bluetongue and all host and vector species coexist, and bluetongue does not eradicate sheep because it cannot persist on midges and cattle alone. The authors use different approaches of dynamical systems persistence theory to analyze the two situations.

View the paper: Uniform Persistence in a Model for Bluetongue Dynamics

http://epubs.siam.org/doi/abs/10.1137/120878197

SIAM Journal on Mathematical Analysis, 46 (2), 1160-1184

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