2,707 research outputs found
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
Modeling the growth of multicellular cancer spheroids in a\ud bioengineered 3D microenvironment and their treatment with an\ud anti-cancer drug
A critical step in the dissemination of ovarian cancer cells is the formation of multicellular spheroids from cells shed from the primary tumor. The objectives of this study were to establish and validate bioengineered three-dimensional (3D) microenvironments for culturing ovarian cancer cells in vitro and simultaneously to develop computational models describing the growth of multicellular spheroids in these bioengineered matrices. Cancer cells derived from human epithelial ovarian carcinoma were embedded within biomimetic hydrogels of varying stiffness and cultured for up to 4 weeks. Immunohistochemistry was used to quantify the dependence of cell proliferation and apoptosis on matrix stiffness, long-term culture and treatment with the anti-cancer drug paclitaxel.\ud
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Two computational models were developed. In the first model, each spheroid was treated as an incompressible porous medium, whereas in the second model the concept of morphoelasticity was used to incorporate details about internal stresses and strains. Each model was formulated as a free boundary problem. Functional forms for cell proliferation and apoptosis motivated by the experimental work were applied and the predictions of both models compared with the output from the experiments. Both models simulated how the growth of cancer spheroids was influenced by mechanical and biochemical stimuli including matrix stiffness, culture time and treatment with paclitaxel. Our mathematical models provide new perspectives on previous experimental results and have informed the design of new 3D studies of multicellular cancer spheroids
Investigation of bone resorption within a cortical basic multicellular unit using a lattice-based computational model
In this paper we develop a lattice-based computational model focused on bone
resorption by osteoclasts in a single cortical basic multicellular unit (BMU).
Our model takes into account the interaction of osteoclasts with the bone
matrix, the interaction of osteoclasts with each other, the generation of
osteoclasts from a growing blood vessel, and the renewal of osteoclast nuclei
by cell fusion. All these features are shown to strongly influence the
geometrical properties of the developing resorption cavity including its size,
shape and progression rate, and are also shown to influence the distribution,
resorption pattern and trajectories of individual osteoclasts within the BMU.
We demonstrate that for certain parameter combinations, resorption cavity
shapes can be recovered from the computational model that closely resemble
resorption cavity shapes observed from microCT imaging of human cortical bone.Comment: 17 pages, 11 figures, 1 table. Revised version: paper entirely
rewritten for a more biology-oriented readership. Technical points of model
description now in Appendix. Addition of two new figures (Fig. 5 and Fig. 9)
and removal of former Fig.
Collective motion of cells: from experiments to models
Swarming or collective motion of living entities is one of the most common
and spectacular manifestations of living systems having been extensively
studied in recent years. A number of general principles have been established.
The interactions at the level of cells are quite different from those among
individual animals therefore the study of collective motion of cells is likely
to reveal some specific important features which are overviewed in this paper.
In addition to presenting the most appealing results from the quickly growing
related literature we also deliver a critical discussion of the emerging
picture and summarize our present understanding of collective motion at the
cellular level. Collective motion of cells plays an essential role in a number
of experimental and real-life situations. In most cases the coordinated motion
is a helpful aspect of the given phenomenon and results in making a related
process more efficient (e.g., embryogenesis or wound healing), while in the
case of tumor cell invasion it appears to speed up the progression of the
disease. In these mechanisms cells both have to be motile and adhere to one
another, the adherence feature being the most specific to this sort of
collective behavior. One of the central aims of this review is both presenting
the related experimental observations and treating them in the light of a few
basic computational models so as to make an interpretation of the phenomena at
a quantitative level as well.Comment: 24 pages, 25 figures, 13 reference video link
Mechanical Properties of Growing Melanocytic Nevi and the Progression to Melanoma
Melanocytic nevi are benign proliferations that sometimes turn into malignant
melanoma in a way that is still unclear from the biochemical and genetic point
of view. Diagnostic and prognostic tools are then mostly based on dermoscopic
examination and morphological analysis of histological tissues. To investigate
the role of mechanics and geometry in the morpholgical dynamics of melanocytic
nevi, we study a computation model for cell proliferation in a layered
non-linear elastic tissue. Numerical simulations suggest that the morphology of
the nevus is correlated to the initial location of the proliferating cell
starting the growth process and to the mechanical properties of the tissue. Our
results also support that melanocytes are subject to compressive stresses that
fluctuate widely in the nevus and depend on the growth stage. Numerical
simulations of cells in the epidermis releasing matrix metalloproteinases
display an accelerated invasion of the dermis by destroying the basal membrane.
Moreover, we suggest experimentally that osmotic stress and collagen inhibit
growth in primary melanoma cells while the effect is much weaker in metastatic
cells. Knowing that morphological features of nevi might also reflect geometry
and mechanics rather than malignancy could be relevant for diagnostic purpose
Simulation of the spatial structure and cellular organization evolution of cell aggregates arranged in various simple geometries, using a kinetic monte carlo method applied to a lattice model
ilustraciones, graficasEsta tesis trata los modelos de morfogénesis, en particular los modelos de evolución guiada por contacto que son coherentes con la hipótesis de la adhesión diferencial. Se presenta una revisión de algunos modelos, sus principios biológicos subyacentes, la relevancia y aplicaciones en el marco de la bioimpresión, la ingenierÃa de tejidos y la bioconvergencia. Luego, se presentan los detalles de los modelos basados en métodos de Monte Carlo para profundizar más adelante en el modelo basados en algoritmos Kinetic Monte Carlo (KMC) , más especÃficamente, se describe en detalle un modelo KMC de autoaprendizaje (SL-KMC). Se presenta y explica la estructura algorÃtmica del código implementado, se evalúa el rendimiento del modelo y se compara con un modelo KMC tradicional. Finalmente, se realizan los procesos de calibración y validación, se observó que el modelo es capaz de replicar la evolución del sistema multicelular cuando las condiciones de energÃa interfacial del sistema simulado son similares a las del sistema de calibraciones.
(Texto tomado de la fuente)This thesis treats the models for morphogenesis, in particular the contact-guided evolution models that are
coherent with the differential adhesion hypothesis. A review of some models, their biological underpinning
principles, the relevance and applications in the framework of bioprinting, tissue engineering and bioconvergence
are presented. Then the details for the Monte Carlo methods-based models are presented to
later deep dive into the Kinetic Monte Carlo (KMC) based model, and more specifically a Self-Learning
KMC (SL-KMC) model is described to detail. The algorithmic structure of the implemented code is
presented and explained, the model performance is assessed and compared with a traditional KMC model.
Finally, the calibration and validation processes have been carried out, it was observed that the model is
able to replicate the multicellular system evolution when the interfacial energy conditions of the simulated
system are similar to those of the calibrations system.MaestrÃaMagÃster en IngenierÃa - IngenierÃa QuÃmic
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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