3,069 research outputs found
Mechanical and Systems Biology of Cancer
Mechanics and biochemical signaling are both often deregulated in cancer,
leading to cancer cell phenotypes that exhibit increased invasiveness,
proliferation, and survival. The dynamics and interactions of cytoskeletal
components control basic mechanical properties, such as cell tension,
stiffness, and engagement with the extracellular environment, which can lead to
extracellular matrix remodeling. Intracellular mechanics can alter signaling
and transcription factors, impacting cell decision making. Additionally,
signaling from soluble and mechanical factors in the extracellular environment,
such as substrate stiffness and ligand density, can modulate cytoskeletal
dynamics. Computational models closely integrated with experimental support,
incorporating cancer-specific parameters, can provide quantitative assessments
and serve as predictive tools toward dissecting the feedback between signaling
and mechanics and across multiple scales and domains in tumor progression.Comment: 18 pages, 3 figure
Blood Vessel Tortuosity Selects against Evolution of Agressive Tumor Cells in Confined Tissue Environments: a Modeling Approach
Cancer is a disease of cellular regulation, often initiated by genetic
mutation within cells, and leading to a heterogeneous cell population within
tissues. In the competition for nutrients and growth space within the tumors
the phenotype of each cell determines its success. Selection in this process is
imposed by both the microenvironment (neighboring cells, extracellular matrix,
and diffusing substances), and the whole of the organism through for example
the blood supply. In this view, the development of tumor cells is in close
interaction with their increasingly changing environment: the more cells can
change, the more their environment will change. Furthermore, instabilities are
also introduced on the organism level: blood supply can be blocked by increased
tissue pressure or the tortuosity of the tumor-neovascular vessels. This
coupling between cell, microenvironment, and organism results in behavior that
is hard to predict. Here we introduce a cell-based computational model to study
the effect of blood flow obstruction on the micro-evolution of cells within a
cancerous tissue. We demonstrate that stages of tumor development emerge
naturally, without the need for sequential mutation of specific genes.
Secondly, we show that instabilities in blood supply can impact the overall
development of tumors and lead to the extinction of the dominant aggressive
phenotype, showing a clear distinction between the fitness at the cell level
and survival of the population. This provides new insights into potential side
effects of recent tumor vasculature renormalization approaches
Homogenization Model for Aberrant Crypt Foci
Several explanations can be found in the literature about the origin of
colorectal cancer. There is however some agreement on the fact that the
carcinogenic process is a result of several genetic mutations of normal cells.
The colon epithelium is characterized by millions of invaginations, very small
cavities, called crypts, where most of the cellular activity occurs. It is
consensual in the medical community, that a potential first manifestation of
the carcinogenic process, observed in conventional colonoscopy images, is the
appearance of Aberrant Crypt Foci (ACF). These are clusters of abnormal crypts,
morphologically characterized by an atypical behavior of the cells that
populate the crypts. In this work an homogenization model is proposed, for
representing the cellular dynamics in the colon epithelium. The goal is to
simulate and predict, in silico, the spread and evolution of ACF, as it can be
observed in colonoscopy images. By assuming that the colon is an heterogeneous
media, exhibiting a periodic distribution of crypts, we start this work by
describing a periodic model, that represents the ACF cell-dynamics in a
two-dimensional setting. Then, homogenization techniques are applied to this
periodic model, to find a simpler model, whose solution symbolizes the averaged
behavior of ACF at the tissue level. Some theoretical results concerning the
existence of solution of the homogenized model are proven, applying a fixed
point theorem. Numerical results showing the convergence of the periodic model
to the homogenized model are presented.Comment: 26 pages, 4 figure
Simulating non-small cell lung cancer with a multiscale agent-based model
Background The epidermal growth factor receptor (EGFR) is frequently
overexpressed in many cancers, including non-small cell lung cancer (NSCLC). In
silcio modeling is considered to be an increasingly promising tool to add
useful insights into the dynamics of the EGFR signal transduction pathway.
However, most of the previous modeling work focused on the molecular or the
cellular level only, neglecting the crucial feedback between these scales as
well as the interaction with the heterogeneous biochemical microenvironment.
Results We developed a multiscale model for investigating expansion dynamics
of NSCLC within a two-dimensional in silico microenvironment. At the molecular
level, a specific EGFR-ERK intracellular signal transduction pathway was
implemented. Dynamical alterations of these molecules were used to trigger
phenotypic changes at the cellular level. Examining the relationship between
extrinsic ligand concentrations, intrinsic molecular profiles and microscopic
patterns, the results confirmed that increasing the amount of available growth
factor leads to a spatially more aggressive cancer system. Moreover, for the
cell closest to nutrient abundance, a phase-transition emerges where a minimal
increase in extrinsic ligand abolishes the proliferative phenotype altogether.
Conclusions Our in silico results indicate that, in NSCLC, in the presence of
a strong extrinsic chemotactic stimulus, and depending on the cell's location,
downstream EGFR-ERK signaling may be processed more efficiently, thereby
yielding a migration-dominant cell phenotype and overall, an accelerated
spatio-temporal expansion rate.Comment: 37 pages, 7 figure
A Review of Mathematical Models for the Formation of\ud Vascular Networks
Mainly two mechanisms are involved in the formation of blood vasculature: vasculogenesis and angiogenesis. The former consists of the formation of a capillary-like network from either a dispersed or a monolayered population of endothelial cells, reproducible also in vitro by specific experimental assays. The latter consists of the sprouting of new vessels from an existing capillary or post-capillary venule. Similar phenomena are also involved in the formation of the lymphatic system through a process generally called lymphangiogenesis.\ud
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A number of mathematical approaches have analysed these phenomena. This paper reviews the different modelling procedures, with a special emphasis on their ability to reproduce the biological system and to predict measured quantities which describe the overall processes. A comparison between the different methods is also made, highlighting their specific features
Investigating biocomplexity through the agent-based paradigm.
Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex
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