Maestría en Biociencias (tesis)
URI permanente para esta colección
Examinar
Envíos recientes
Publicación Biogas production from organic solid waste through anaerobic digestion : a meta-analysis(Universidad EAFIT, 2024) Triviño Pineda, Jairo Smith; Sánchez Rodríguez, Aminael; Pinel Peláez, NicolásPublicación Evaluation of a Landfill Leachate Bioremediation System Using Spirulina sp.(Universidad EAFIT, 2025) González López, Federico; Rendón Castrillón, Leidy Johanna; Ramírez Carmona, Margarita Enid; Ocampo López, Carlos; Rendón Castrillón, Leidy JohannaPublicación In Silico Identification of Alternative Molecular Targets from Probiotic Strains for Potential Therapeutic Repositioning in Gut Microbiome-Associated Diseases(Universidad EAFIT, 2025) Buitrago Roldán, Nicolás; García Zea, Jerson Alexander; Sierra Zapata, LauraHuman gut hosts over 100 trillion symbiotic bacteria, far exceeding the number of host cells. These microorganisms collectively form what is known as the gut microbiota. This microbiota performs a wide range of functions crucial for the human body, including protection against pathogens, nutrient extraction, metabolism, and immunity, which, under healthy conditions, result in stability, resilience, and beneficial symbiotic interactions. A probiotic microorganism is defined as a live microorganism that confers a health benefit to the host when administered in adequate amounts. Consequently, in recent years, the number of studies linking probiotic strains to prevent and treat several diseases, such as autoimmune diseases, atopy, metabolic syndrome, metabolic disorders, cancer, and certain behavioral disorders, has increased significantly. Similarly, more research is emerging that employs omics sciences, which involves obtaining comprehensive data that includes genomic, proteomic, metabolic, and other omics information, aiming to assess this data before and after probiotic treatment administration. In this context, and thanks to open access data and cooperative Omics bioinformatic tools, this project proposes an in-silico approach to analyze the effects of probiotic strains on human cells, focusing on differentially expressed genes and their protein-protein interactions. The results highlight the ability of probiotics, such as Propionibacterium freudenreichii ITG P9 and Bacillus subtilis CW14, to modulate human cellular responses, particularly in pathways related to immunity and the cell cycle. This study emphasizes the role of probiotics in regulating genes associated with metabolic, neurological, and autoimmune diseases, revealing potential neuroprotective and antitumoral properties.Publicación Analysis of gene expression regulation of colon adenocarcinoma genes treated with three probiotic species(Universidad EAFIT, 2024) Vélez García, Alejandra; García Zea, Jerson Alexander; Sierra Zapata, LauraPublicación First genetic assessment of Colombian bunching onion Germplasm bank accessions : unexpected variation and global context(Universidad EAFIT, 2024) Florian Cruz, Andrés Felipe; Correa Álvarez, Javier; Correa Álvarez, JavierPublicación Microbiología predictiva mediante aprendizaje automatizado para la optimización de procesos productivos : Metanálisis(Universidad EAFIT, 2024) Yepes Medina, Verónica; Pinel Peláez, NicolásUnsafe food containing harmful bacteria, viruses, parasites or chemicals can cause more than 200 different illnesses, from diarrhea to cancer. Worldwide, an estimated 600 million (nearly 1 in 10 people) fall ill each year after eating contaminated food, resulting in 420.000 deaths and the loss of 33 million years of healthy life. Therefore, it is necessary to detect and respond to public health threats associated with unsafe food with enabling technologies or tools. Predictive microbiology is concerned with preventing, controlling or limiting the existence of microorganisms by mapping their potential responses to particular environmental conditions, such as temperature, pH, nutrients (protein and fat), water activity (aw) and others. And machine learning as a branch or artificial intelligence learns from these data, identifying patterns for decision making. Recent studies are based in the use of supervised machine learning models to predict the presence of a foodborne pathogenic microorganism at any stage of the production chain, the most commonly used models include Random Forest and support vector machine with rating metrics for accuracy and sensitivity >80%. The main evaluation metrics of the algorithms are: accuracy, F1 score, confusion matrix, sensitivity, specificity and area under the curve (ROC-AUC, Receiver-Operating-Characteristic). Studies have shown that Random Forest was the best model, exhibiting an accuracy of 95% and a F1 score of 98%. Here were evaluated twenty five (25) articles with library metafor of Rstudio version 4.2.1 and this information provides new opportunities to explore non-destructive models for rapid detection of microorganisms in the production chain.Publicación The random forest machine learning model performs better in predicting drug repositioning using networks : systematic review and meta-analysis(Universidad EAFIT, 2024) García Marín, Darlyn Juranny; García Zea, Jerson Alexander; García Zea, Jerson AlexanderÍtem Identification of non-model mammal species using the MinION DNA sequences from Oxford Nanopore(Universidad EAFIT, 2023) Velásquez-Restrepo, Sara; Díaz-Nieto, Juan Fernando; Díaz Nieto, Juan FernandoÍtem Coalescent-delimitation framework and ecogeographic patterns disentangle the species boundaries within the Neotropical mouse-opossums subgenus Marmosops(Universidad EAFIT, 2022) Carillo Restrepo, Jhan Carlos; Díaz Nieto, Juan FernandoThe Neotropics encompasses a wide range of biomes and habitat types that place it as one of the most important Earth's regions for understanding the prevalence of cryptic and unknown diversity. However, it has been shown that this region is one of the least represented in genetic data in the tree of life. Therefore, advancing intra and interspecific genetic revisions in this region represents a major scientific priority to reduce our ignorance of the planet's biodiversity. American marsupials of Marmosops subgenus are distributed in a wide variety of Neotropical habitats, so it is an attractive group to undertake studies on Neotropical diversification processes, but such research is hindered by the fact that we do not yet fully understand the species limits of some groups within the subgenus. Herein, we evaluate the evolutionary independence of 13 morphologically-cryptic mtDNA haplogroups within Marmosops that were identified by our previous single-locus species delimitation analyses. For this purpose, we analyzed a multi-locus dataset (12 unlinked nuclear loci and one mtDNA locus) in a Bayesian Multi-Species Coalescent framework implemented in BPP, combined with heuristic criterion (gdi) that incorporated the speciation-continuum process into species delimitation analyses, to further understand the genetic boundaries within this Neotropical mouse opossum’s clade. Our BPP analyses recovered each of the 13 haplogroups as independent evolutionary lineages. However, heuristic gdi showed that the tested lineages are across the entire spectrum of the speciation continuum, and that only seven lineages recognized by BPP correspond to “true” species. Three of these seven lineages are currently recognized as valid species, demonstrating the effectiveness of our study; while ecogeographic patterns information revealed that the remaining four lineages have promising information to be recognized as possible new species for science.Ítem Bacillus spp. antibacterial activity induced by triphenyl tetrazolium chloride : metabolome changes and oxidative stress response(Universidad EAFIT, 2023) González Marín, Carolina; Villegas Escobar, ValeskaÍtem Genomic Prediction and Genome-Wide Association Analysis in Common Bean (Phaseolus vulgaris l.) × Tepary bean (P. acutifolius a. gray) Inter-specific Advanced Lines at the Caribbean Coast of Colombia(Universidad EAFIT, 2023) López Hernández, Luis Felipe; Villanueva Mejía, Diego Fernando; Cortés Vera, Andrés JavierThe negative effects of the climate change are risking global food security with 828 million people facing hunger, which is almost 16 times the population of Colombia. Given this scenario, legumes as common bean has offered a nature-based solution to source nutrients for rural communities in Latin America thanks to their high content of nutrients. For this reason, it is imperative to speed up the molecular genetic breading of common beans so that they can be cultivated in regions affected by extreme climate change, one of which is coastal Colombian. Therefore, in order to bridge this gap, this study aimed coupling an advanced panel of common bean (Phaseolus vulgaris L.) × tepary bean (P. acutifolius A. Gray) inter-specific lines with Bayesian regression algorithms to identify novel sources of adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with inter-specific ancestries were genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components and two biomass variables were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities in coastal Colombia. We explored the comparative analysis of several regression approaches where the model with the best performance in all traits and environments was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori GWAS models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per environment and trait were determined to the top 500 most explicative markers according to their β regression effects. These 500 SNPs on average overlapped in 5.24 % across localities, which reinforced the environmentally dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs), and selected the top 10 genotypes for each environment and trait as part of a recommendation scheme targeting narrow adaption. The genotypes and SNP markers identified in this study as candidates for abiotic stress have the potential to be used in the following cycles as part of the long-term bean breeding program for coastal tropical regions.Ítem Microbial and Chemical Diversity of the Gut Microbiota in a cohort of pregnant and lactating women from Antioquia-Colombia(Universidad EAFIT, 2023) Londoño Osorio, Sara; Sierra Zapata, LauraÍtem Analysis of bioenergetic function alterations as part of the antifungal effect of cyclic lipopeptides and cinnamon extract against Fusarium spp. and Colletotrichum spp.(Universidad EAFIT, 2023) Ramírez Mejía, Julieta María; Gómez Ramírez, Luis Alejandro; Villegas Escobar, ValeskaÍtem Multi-omics characterization of the microbial and chemical ecology of a water Kefir fermentation(Universidad EAFIT, 2023) Arrieta Echeverri, Maria Clara; Sierra Zapata, Laura; Fernández García, Geysson JavierÍtem Silenciamiento del gen ácido graso desaturasa 2 (FAD2) en Ricinus communis (Malpighiales: Euphorbiaceae) mediante edición genética basada en el uso de la tecnología CRISPR/Cas9(Universidad EAFIT, 2022) Susunaga Gómez, Danna Melissa; Villanueva Mejía, Diego FernandoÍtem Culturable Gut Microbiota of Pregnant and Breastfeeding Women Involved in The Metabolism of a Dietary Source Rich in Choline : Biological Diversity and Biochemical Characterization of a Pilot Cohort in Colombia(Universidad EAFIT, 2022) Gómez Mesa, Laura; Sierra Zapata, LauraÍtem Estudio del efecto del selenio en diferentes microalgas con propósitos de producción de biomasa enriquecida(Universidad EAFIT, 2022) Hoyos Gutiérrez, Brenda Seleny; Sáez Vega, Álex Armando; Villanueva Mejía, Diego Fernando; Miranda Parra, Alejandra MaríaÍtem Molecular elements underlying Bacillus tequilensis unrestrained loss of social traits(Universidad EAFIT, 2022) García-Botero, Camilo Alejandro; Villegas Escobar, Valeska; Cuellar Gaviria, Tatiana Zazini