1,720,993 research outputs found
Is the allee effect relevant in cancer evolution and therapy?
Full text linkMost models of cancer assume that tumor cells populations, at low densities, grow exponentially to be eventually limited by the available amount of resources such as space and nutrients. However, recent pre-clinical and clinical data of cancer onset or recurrence indicate the presence of a population dynamics in which growth rates increase with cell numbers. Such effect is analogous to the cooperative behavior in an ecosystem described by the so called Allee effect. In this work, we study the consequences of the Allee effect on cancer growth via the properties of dynamical models incorporating the Allee effect, and the implications that the occurrence of such effect has for the choice of the more appropriate therapy. Some simulations will be presented in which the model is used to fit data from in vitro experiments and clinical trials
A Mathematical Study of the Influence of Hypoxia and Acidity on the Evolutionary Dynamics of Cancer
Full text linkHypoxia and acidity act as environmental stressors promoting selection for cancer cells with a more aggressive phenotype. As a result, a deeper theoretical understanding of the spatio-temporal processes that drive the adaptation of tumour cells to hypoxic and acidic microenvironments may open up new avenues of research in oncology and cancer treatment. We present a mathematical model to study the influence of hypoxia and acidity on the evolutionary dynamics of cancer cells in vascularised tumours. The model is formulated as a system of partial integro-differential equations that describe the phenotypic evolution of cancer cells in response to dynamic variations in the spatial distribution of three abiotic factors that are key players in tumour metabolism: oxygen, glucose and lactate. The results of numerical simulations of a calibrated version of the model based on real data recapitulate the eco-evolutionary spatial dynamics of tumour cells and their adaptation to hypoxic and acidic microenvironments. Moreover, such results demonstrate how nonlinear interactions between tumour cells and abiotic factors can lead to the formation of environmental gradients which select for cells with phenotypic characteristics that vary with distance from intra-tumour blood vessels, thus promoting the emergence of intra-tumour phenotypic heterogeneity. Finally, our theoretical findings reconcile the conclusions of earlier studies by showing that the order in which resistance to hypoxia and resistance to acidity arise in tumours depend on the ways in which oxygen and lactate act as environmental stressors in the evolutionary dynamics of cancer cells
Multi-scale modeling of Snail-mediated response to hypoxia in tumor progression
Full text linkTumor cell migration within the microenvironment is a crucial aspect for cancer progression and, in this context, hypoxia has a significant role. An inadequate oxygen supply acts as an environmental stressor inducing migratory bias and phenotypic changes. In this paper, we propose a novel multi-scale mathematical model to analyze the pivotal role of Snail protein expression in the cellular responses to hypoxia. Starting from the description of single-cell dynamics driven by the Snail protein, we construct the corresponding kinetic transport equation that describes the evolution of the cell distribution. Subsequently, we employ proper scaling arguments to formally derive the equations for the statistical moments of the cell distribution, which govern the macroscopic tumor dynamics. Numerical simulations of the model are performed in various scenarios with biological relevance to provide insights into the role of the multiple tactic terms, the impact of Snail expression on cell proliferation, and the emergence of hypoxia-induced migration patterns. Moreover, quantitative comparisons with experimental data show the model's reliability in measuring the impact of Snail transcription on cell migratory potential. Through our findings, we shed light on the potential of our mathematical framework in advancing the understanding of the biological mechanisms driving tumor progression
Groundwater fluxes into a submerged sinkhole area, Central Italy, using radon and water chemistry
No full textThe groundwater contribution into Green Lake and Black Lake (Vescovo Lakes Group), two cover collapse sinkholes
in Pontina Plain (Central Italy), was estimated using water chemistry and a 222Rn budget. These data can constrain
the interactions between sinkholes and deep seated fluid circulation, with a special focus on the possibility of
the bedrock karst aquifer feeding the lake. The Rn budget accounted for all quantifiable surface and subsurface input
and output fluxes including the flux across the sediment–water interface. The total value of groundwater discharge into
Green Lake and Black Lake (540 ± 160 L s1) obtained from the Rn budget is lower than, but comparable with historical
data on the springs group discharge estimated in the same period of the year (800 ± 90 L s1). Besides being an
indirect test for the reliability of the Rn-budget ‘‘tool’’, it confirms that both Green and Black Lake are effectively
springs and not simply ‘‘water filled’’ sinkholes. New data on the water chemistry and the groundwater fluxes into
the sinkhole area of Vescovo Lakes allows the assessment of the mechanism responsible for sinkhole formation in Pontina
Plain and suggests the necessity of monitoring the changes of physical and chemical parameters of groundwater
below the plain in order to mitigate the associated ris
A phenotype-structured model to reproduce the avascular growth of a tumor and its interaction with the surrounding environment
Full text linkWe here propose a one-dimensional spatially explicit phenotype-structured model to analyze selected aspects of avascular tumor progression. In particular, our approach distinguishes viable and necrotic cell fractions. The metabolically active part of the disease is, in turn, differentiated according to a continuous trait, that identifies cell variants with different degrees of motility and proliferation potential. A parabolic partial differential equation (PDE) then governs the spatio-temporal evolution of the phenotypic distribution of active cells within the host tissue. In this respect, active tumor agents are allowed to duplicate, move upon haptotactic and pressure stimuli, and eventually undergo necrosis. The mutual influence between the emerging malignancy and its environment (in terms of molecular landscape) is implemented by coupling the evolution law of the viable tumor mass with a parabolic PDE for oxygen kinetics and a differential equation that accounts for local consumption of extracellular matrix (ECM) elements. The resulting numerical realizations reproduce tumor growth and invasion in a number scenarios that differ for cell properties (i.e., individual migratory behavior, duplication, and mutation potential) and environmental conditions (i.e., level of tissue oxygenation and homogeneity in the initial matrix profile). In particular, our simulations show that, in all cases, more mobile cell variants occupy the front edge of the tumor, whereas more proliferative clones are selected at more internal regions. A necrotic core constantly occupies the bulk of the mass due to nutrient deprivation. This work may eventually suggest some biomedical strategies to partially reduce tumor aggressiveness, i.e., to enhance necrosis of malignant tissue and to promote the presence of more proliferative cell phenotypes over more invasive ones
From Discrete Kinetic and Stochastic Game Theory to Modelling Complex Systems in Applied Sciences
No full textThis paper deals with some methodological aspects related to the discretization of a class of integro-differential equations modelling the evolution of the probability distribution over the microscopic state of a large system of interacting individuals. The microscopic state includes both mechanical and socio-biological variables. The discretization of the microscopic state generates a class of dynamical systems defining the evolution of the densities of the discretized state. In general, this yields a system of partial differential equations replacing the continuous integro-differential equation. As an example, a specific application is discussed, which refers to modelling in the field of social dynamics. The derivation of the evolution equation needs the development of a stochastic game theory
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Una nuova potenzialità per la datazione di travertini Olocenici non idrotermali: la misura del 226Ra
No full textCancer development: a population theoretical perspective
Full text linkIn the history of life, immune system and cancer have been engaged in an evolutionary arms race driven by the twin forces of mutation and selection. Ideally therapies should be a resolutive weapon, but, despite great progresses during the last 50 years or so, the race still goes on. The aim of this paper is to present a mathematical model, which can be used as in silico laboratory, to provide some indication on the effectiveness of therapies. Here we focus on two cancer populations competing for resources and subjected to the action of two types of immune system cells: thus the model results in a system of 4 differential equation that is analytically and computationally studied to elucidate its properties and emerging behaviors. At the beginning, some speci.c subsystems are analyzed and the effects of different therapies simulated; in particular .rst the system comprising a single cancer and immune cells type is considered and next the case of two cancer clones in absence of the immune cells. The complete model is then presented, which yields a rich variety of behaviors; in particular it is shown that for strong intertumoral competition, and high recognition levels by the immune system, stable stationary states are replaced by sustained oscillations. Finally some conclusion about therapy effectiveness are drawn, based on the results of simulations
- …
