Cell behavior under hypoxic conditions. Computational 3D model

During the early stages of bone regeneration, oxygen plays a key role, recruiting mesenchymal stem cells and regulating the processes of differentiation, proliferation, and apoptosis. To study in these effects, a 3D computational model has been developed, where the effects of oxygen in the mentioned processes are considered

Role of oxygen concentration in the osteoblasts behavior: A finite element model

Oxygen concentration plays a key role in cell survival and viability. Besides, it has important effects on essential cellular biological processes such as cell migration, differentiation, proliferation and apoptosis. Therefore, the prediction of the cellular response to the alterations of the oxygen concentration can help significantly in the advances of cell culture research. Here, we present a 3D computational mechanotactic model to simulate all the previously mentioned cell processes under different oxygen concentrations. With this model, three cases have been studied. Starting with mesenchymal stem cells within an extracellular matrix with mechanical properties suitable for its differentiation into osteoblasts, and under different oxygen conditions to evaluate their behavior under normoxia, hypoxia and anoxia. The obtained results, which are consistent with the experimental observations, indicate that cells tend to migrate toward zones with higher oxygen concentration where they accelerate their differentiation and proliferation. This technique can be employed to control cell migration toward fracture zones to accelerate the healing process. Besides, as expected, to avoid cell apoptosis under conditions of anoxia and to avoid the inhibition of the differentiation and proliferation processes under conditions of hypoxia, the state of normoxia should be maintained throughout the entire cell-culture process

Computational modeling of multiple myeloma interactions with resident bone marrow cells

The interaction of multiple myeloma with bone marrow resident cells plays a key role in tumor progression and the development of drug resistance. The tumor cell response involves contact-mediated and paracrine interactions. The heterogeneity of myeloma cells and bone marrow cells makes it difficult to reproduce this environment in in-vitro experiments. The use of in-silico established tools can help to understand these complex problems. In this article, we present a computational model based on the finite element method to define the interactions of multiple myeloma cells with resident bone marrow cells. This model includes cell migration, which is controlled by stress–strain equilibrium, and cell processes such as proliferation, differentiation, and apoptosis. A series of computational experiments were performed to validate the proposed model. Cell proliferation by the growth factor IGF-1 is studied for different concentrations ranging from 0–10 ng/mL. Cell motility is studied for different concentrations of VEGF and fibronectin in the range of 0–100 ng/mL. Finally, cells were simulated under a combination of IGF-1 and VEGF stimuli whose concentrations are considered to be dependent on the cancer-associated fibroblasts in the extracellular matrix. Results show a good agreement with previous in-vitro results. Multiple myeloma growth and migration are shown to correlate linearly to the IGF-1 stimuli. These stimuli are coupled with the mechanical environment, which also improves cell growth. Moreover, cell migration depends on the fiber and VEGF concentration in the extracellular matrix. Finally, our computational model shows myeloma cells trigger mesenchymal stem cells to differentiate into cancer-associated fibroblasts, in a dose-dependent manner

Finite Element Model for Cardiac Cell mechano-electrical stimulation

Cardiomyocyte behavior is highly dependent on the mechanical and electrical stimuli. We present a computational model, based on the FEM, to evaluate their behavior under electro-mechanical stimulation. Cell migration, adhesion, and collective behavior have been considered. Low stiffness and high alternating electric field have shown to be the best combination

Cell dynamics in microfluidic devices under heterogeneous chemotaxis and growth conditions: a mathematical study

As motivated by studies of cellular motility driven by spatiotemporal chemotactic gradients in microdevices, we develop a framework for constructing approximate analytical solutions for the location, speed and cellular densities for cell chemotaxis waves in heterogeneous fields of chemoattractant from the underlying partial differential equation models. In particular, such chemotactic waves are not in general translationally invariant travelling waves, but possess a spatial variation that evolves in time, and may even may oscillate back and forth in time, according to the details of the chemotactic gradients. The analytical framework exploits the observation that unbiased cellular diffusive flux is typically small compared to chemotactic fluxes and is first developed and validated for a range of exemplar scenarios. The framework is subsequently applied to more complex models considering the full dynamics of the chemoattractant and how this may be driven and controlled within a microdevice by considering a range of boundary conditions. In particular, even though solutions cannot be constructed in all cases, a wide variety of scenarios can be considered analytically, firstly providing global insight into the important mechanisms and features of cell motility in complex spatiotemporal fields of chemoattractant. Such analytical solutions also provide a means of rapid evaluation of model predictions, with the prospect of application in computationally demanding investigations relating theoretical models and experimental observation, such as Bayesian parameter estimation.Comment: 32 pages, 11 figure

A Mathematical Modelling Study of Chemotactic Dynamics in Cell Cultures: The Impact of Spatio-temporal Heterogeneity

As motivated by studies of cellular motility driven by spatiotemporal chemotactic gradients in microdevices, we develop a framework for constructing approximate analytical solutions for the location, speed and cellular densities for cell chemotaxis waves in heterogeneous fields of chemoattractant from the underlying partial differential equation models. In particular, such chemotactic waves are not in general translationally invariant travelling waves, but possess a spatial variation that evolves in time, and may even oscillate back and forth in time, according to the details of the chemotactic gradients. The analytical framework exploits the observation that unbiased cellular diffusive flux is typically small compared to chemotactic fluxes and is first developed and validated for a range of exemplar scenarios. The framework is subsequently applied to more complex models considering the chemoattractant dynamics under more general settings, potentially including those of relevance for representing pathophysiology scenarios in microdevice studies. In particular, even though solutions cannot be constructed in all cases, a wide variety of scenarios can be considered analytically, firstly providing global insight into the important mechanisms and features of cell motility in complex spatiotemporal fields of chemoattractant. Such analytical solutions also provide a means of rapid evaluation of model predictions, with the prospect of application in computationally demanding investigations relating theoretical models and experimental observation, such as Bayesian parameter estimation

A new reliability-based data-driven approach for noisy experimental data with physical constraints

Data Science has burst into simulation-based engineering sciences with an impressive impulse. However, data are never uncertainty-free and a suitable approach is needed to face data measurement errors and their intrinsic randomness in problems with well-established physical constraints. As in previous works, this problem is here faced by hybridizing a standard mathematical modeling approach with a new data-driven solver accounting for the phenomenological part of the problem, with the aim of finding a solution point, satisfying some constraints, that minimizes a distance to a given data-set. However, unlike such works that are established in a deterministic framework, we use the Mahalanobis distance in order to incorporate statistical second order uncertainty of data in computations, i.e. variance and correlation. We develop the underlying stochastic theoretical framework and establish the fundamental mathematical and statistical properties. The performance of the resulting reliability-based data-driven procedure is evaluated in a simple but illustrative unidimensional problem as well as in a more realistic solution of a 3D structural problem with a material with intrinsically random constitutive behavior as concrete. The results show, in comparison with other data-driven solvers, better convergence, higher accuracy, clearer interpretation, and major flexibility besides the relevance of allowing uncertainty management with low computational demand

De la realidad histológica a la metabólica: desentrañando la respuesta celular a partir de la evolución de cultivos celulares utilizando redes neuronales guiadas por la física.

The combination of experiments in microfluidic devices with artificial intelligence techniques makes it possible to study complex cellular phenomena that are difficult to face with traditional methods. In this work, physically-guided neural networks are used to explain cellular metabolic changes and predict the behavior of glioblastoma cultures in response to variable stimuli, taking the first steps towards personalized in silico medicine.La combinación de medidas en dispositivos microfluídicos con técnicas de inteligencia artificial permite estudiar fenómenos celulares complejos difíciles de encarar con los métodos tradicionales. En este trabajo se usan redes neuronales guiadas por la física para explicar los cambios metabólicos celulares y predecir el comportamiento de cultivos de glioblastoma ante estímulos variables, dando los primeros pasos hacia la medicina in silico personalizada

Dense discrete phase model for tumor cell growth analysis in fluid environments

Cell-cell and cell-extracellular matrix interactions play a major role in tumor growth, which involves complex molecular intercommunications. We have developed a single-cell computational model in which fluid dynamics and cell-cell interaction are coupled to evaluate the growth of cancer cells in fluidic environments. The results demonstrate that, once the cell concentration increases, the cell-cell interaction increases, decreasing cell maturation time and increasing tumor growth rate