Biology subjects

Saucedo-Mora, L.

Publications and source records attributed to Saucedo-Mora, L..

4 recordsLinked to original sources

bioRxiv2023

Numerical reproduction of the Sherrington-Adrian observations through a community of McCulloch-Pitts neurons with plastic remodelling

Neurons form a highly complex network that produces cognition from simple associative rules. From previous results, this work shows the natural capability of the numerical network produced to modulate the output signal with independence of the intensity of the stimuli. Moreover, the plastic remodelling implemented in the model is capable to change the latency of a wide range of stimuli to synchronize them and adjust to a required delay of the signal.

bioengineering↗

bioRxiv2023

A simple agent-based hybrid model to simulate the biophysics of glioblastoma multiforme cells and the concomitant evolution of the oxygen field

Background and objectives: Glioblastoma multiforme (GBM) is one of the most aggressive cancers of the central nervous system. It is characterized by a high mitotic activity and an infiltrative ability of the glioma cells, neovascularization and necrosis. GBM evolution entails the continuous interplay between heterogeneous cell populations, chemotaxis, and physical cues through different scales. In this work, an agent-based hybrid model is proposed to simulate the coupling of the multiscale biological events involved in the GBM invasion, specifically the individual and collective migration of GBM cells and the concurrent evolution of the oxygen field and phenotypic plasticity. An asset of the formulation is that it is conceptually and computationally simple but allows to reproduce the complexity and the progression of the GBM micro-environment at cell and tissue scales simultaneously. Methods: The migration is reproduced as the result of the interaction between every single cell and its micro-environment. The behavior of each individual cell is formulated through genotypic variables whereas the cell micro-environment is modeled in terms of the oxygen concentration and the cell density surrounding each cell. The collective behavior is formulated at a cellular scale through a flocking model. The phenotypic plasticity of the cells is induced by the micro-environment conditions, considering five phenotypes. Results: The model has been contrasted by benchmark problems and experimental tests showing the ability to reproduce different scenarios of glioma cell migration. In all cases, the individual and collective cell migration and the coupled evolution of both the oxygen field and phenotypic plasticity have been properly simulated. This simple formulation allows to mimic the formation of relevant hallmarks of glioblastoma multiforme, such as the necrotic cores, and to reproduce experimental evidences related to the mitotic activity in pseudopalisades. Conclusions: In the collective migration, the survival of the clusters prevails at the expense of cell mitosis, regardless of the size of the groups, which delays the formation of necrotic foci and reduces the rate of oxygen consumption.

bioengineering↗

bioRxiv2023

An agent based model (ABM) to reproduce the boolean logic behaviour of neuronal self organized communities through pulse delay modulation and generation of logic gates

The human brain is arguably the most complex "machine" to ever exist. Its detailed functioning is yet to be fully understood, let alone modeled. Neurological processes have logical signal-processing aspects and biophysical aspects, and both affect the brain structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence approaches inspired by its logical functioning. In this article, we present an approach to model some aspects of the brain learning and signal processing, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model, to demonstrate how dynamic neuroplasticity, neural inhibition and neurons migration can remodel the brain logical connectivity to syncronize signal processing and obtain target latencies. This work demonstrates the importance of dynamic logical and biophysical remodelling in brain plasticity.

bioengineering↗

bioRxiv2022

The Updated Properties Model (UPM): A topology optimization algorithm for the creation of macro-micro optimized structures with variable stiffness.

The design and manufacturing of high value industrial components is suffering a change of paradigm with 3D printing. In this change of paradigm, metamaterials have an important role because when a component is 3D-printed, it is performed from the micro level, where custom structures may be designed to endow the material and the component of special of customized mechanical properties. Topology optimization techniques facilitate the design of both the microstructures and the overall component topology, and today the component topology may be designed assuming a continuous spectrum of mechanical properties facilitated by different locally designed microstructures. However, current topology optimization techniques do not operate directly with the mechanical properties of the material, but through density intermediates, using density-based limits like a minimum or maximum density, assuming an homogeneous base material. We propose here a novel topology optimization algorithm which operates directly on the mechanical properties and energies, without employing density intermediates. The proposed approach reduces the algorithmic complexity since the optimization is performed by the direct iterative update of the mechanical properties, through information taken from its finite element analysis. We show that the proposed methodology can reach similar results as the current techniques based on a gradient descent optimization, eliminating the need for external parameters and, hence, increasing the easy of use and its robustness. The proposed technique is specially suitable for two-level concurrent material-component design using functionally graded metamaterials.

bioengineering↗