Biology subjects

Garcia Garcia, A.

Publications and source records attributed to Garcia Garcia, A..

3 recordsLinked to original sources

bioRxiv2021

Engineering of human mini-bones for the standardized modeling of healthy hematopoiesis, leukemia and solid tumor metastasis

The bone marrow microenvironment provides indispensable factors to sustain blood production throughout life. It is also a hotspot for the progression of hematologic disorders and the most frequent site of solid tumor metastasis. Pre-clinical research relies on xenograft mouse models, precluding the human-specific functional interactions of stem cells with their bone marrow microenvironment. Human mesenchymal cells can be exploited for the in vivo engineering of humanized ossicles (hOss). Those mini-bones provide a human niche conferring engraftment of human healthy and malignant blood samples, yet suffering from major reproducibility issue. Here, we report the standardized generation of hOss by developmental priming of a custom-designed human mesenchymal cell line. We demonstrate superior engraftment of cord blood hematopoietic cells and primary acute myeloid leukemia samples, but also validate our hOss as metastatic site for breast cancer cells. Finally, we report the first engraftment of neuroblastoma patient-derived xenograft cells in a humanized model, recapitulating clinically reported osteolytic lesions. Collectively, our hOss constitute a powerful standardized and malleable platform to model normal hematopoiesis, leukemia and solid tumor metastasis.

bioengineering↗

bioRxiv2021

Modeling Vertical Migrations of Zooplankton Based on Maximizing Fitness

The purpose of the work is to calculate the evolutionarily stable strategy of zooplankton diel vertical migrations from known data of the environment using principles of evolutionary optimality and selection. At the first stage of the research, the fitness function is identified using artificial neural network technologies. The training sample is formed based on empirical observations. It includes pairwise comparison results of the selective advantages of a certain set of species. Key parameters of each strategy are calculated: energy gain from ingested food, metabolic losses, energy costs on movement, population losses from predation and unfavorable living conditions. The problem of finding coefficients of the fitness function is reduced to a classification problem. The single-layer neural network is built to solve this problem. The use of this technology allows one to construct the fitness function in the form of a linear convolution of key parameters with identified coefficients. At the second stage, an evolutionarily stable strategy of the zooplankton behavior is found by maximizing the identified fitness function. The maximization problem is solved using optimal control methods. A feature of this work is the use of piecewise linear approximations of environmental factors: the distribution of food and predator depending on the depth. As a result of the study, mathematical and software tools have been created for modeling and analyzing the hereditary behavior of living organisms in an aquatic ecosystem. Mathematical modeling of diel vertical migrations of zooplankton in Saanich Bay has been carried out.

evolutionary biology↗

bioRxiv2020

Mathematical modelling evolutionarily stablebehavior of zooplankton with state constraints

The purpose of this work is to create mathematical base and software for solving the problem of finding an evolutionarily stable strategy of zooplankton diel vertical migrations and explaining the observed effects in aquatic ecosystems using this software (in particular, in the northeastern part of the Black Sea). An essential feature of this study is the inclusion in the mathematical model of state constraints on the strategy of behavior, which reflect the vertical limited zone of zooplankton habitat. The presence of state constraints creates the main mathematical difficulties for solving the optimal control problem used in the analysis of the model. The general methodological basis for defining evolutionarily stable behavior is the Darwinian principle "survival of the fittest". However, it remains a problem to construct a mathematical expression for the fitness function of hereditary elements. The efforts of the authors were aimed at creating a software package that allows predicting the evolutionarily stable behavior of zooplankton based on the actual universal extreme principle. The created software package includes, as a main component, a computational module for solving the set optimal control problem with state constraints.

evolutionary biology↗