scholarly journals Big data in yeast systems biology

2019 ◽  
Vol 19 (7) ◽  
Author(s):  
Rosemary Yu ◽  
Jens Nielsen
Keyword(s):  
Gene Expression ◽  
Big Data ◽  
Systems Biology ◽  
Model Organism ◽  
Cell Factory ◽  
Yeast Saccharomyces Cerevisiae ◽  
Complex Interactions ◽  
Biological Insight ◽  
Big Data Technologies ◽  
Gene Expression Dynamics

ABSTRACT Systems biology uses computational and mathematical modeling to study complex interactions in a biological system. The yeast Saccharomyces cerevisiae, which has served as both an important model organism and cell factory, has pioneered both the early development of such models and modeling concepts, and the more recent integration of multi-omics big data in these models to elucidate fundamental principles of biology. Here, we review the advancement of big data technologies to gain biological insight in three aspects of yeast systems biology: gene expression dynamics, cellular metabolism and the regulation network between gene expression and metabolism. The role of big data and complementary modeling approaches, including the expansion of genome-scale metabolic models and machine learning methodologies, are discussed as key drivers in the rapid advancement of yeast systems biology.

scholarly journals Leveraging budding yeast Saccharomyces cerevisiae for discovering aging modulation substances for functional food

2019 ◽  
Vol 9 (5) ◽  
pp. 297
Author(s):  
Shaoyu Wang
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Bioactive Compounds ◽  
Functional Food ◽  
Budding Yeast ◽  
Model Organism ◽  
Cellular Systems ◽  
Cell Factory ◽  
Compression Of Morbidity ◽  
Bioactive Substances ◽  
Yeast Saccharomyces Cerevisiae

Background: Discovery of bioactive substances contained in functional food and the mechanism of their aging modulation are imperative steps in developing better, potent and safer functional food for promoting health and compression of morbidity in the aging population.  Budding yeast (Saccharomyces cerevisiae) is invaluable model organism for aging modulation and bioactive compounds discovery. In this paper we have conceptualised a framework for achieving such aim. This framework consists of four components: discovering targets for aging modulation, discovering and validating caloric restriction mimetics, acting as cellular systems for screening natural products or compounds for aging modulation and being a biological factory for producing bioactive compounds according to the roles the yeast systems play. It have been argued that the component of being a biological factory for producing bioactive compounds has much underexplored which also present an opportunity for new active substance discovery and validation for health promotion in functional food industry.Keywords: Aging modulation, budding yeast, functional food, bioactive substances, cell factory

scholarly journals Melanoma expression analysis with Big Data technologies

2017 ◽  
Author(s):  
Alicia Fernandez-Rovira ◽  
Rocio Lavado-Valenzuela ◽  
Miguel Ángel Berciano Guerrero ◽  
Ismael Navas-Delgado ◽  
José F Aldana-Montes
Keyword(s):  
Gene Expression ◽  
Big Data ◽  
Immune System ◽  
Clinical Study ◽  
Tumor Regression ◽  
Scale Up ◽  
Clinical Information ◽  
Initial Study ◽  
Expression Platform ◽  
Big Data Technologies

Melanoma is a highly immunogenic tumor. Therefore, in recent years physicians have incorporated drugs that alter the immune system into their therapeutic arsenal against this disease, revolutionizing in the treatment of patients in an advanced stage of the disease. This has led us to explore and deepen our knowledge of the immunology surrounding melanoma, in order to optimize its approach. At present, immunotherapy for metastatic melanoma is based on stimulating an individual’s own immune system through the use of specific monoclonal antibodies. The use of immunotherapy has meant that many of patients with melanoma have survived and therefore it constitutes a present and future treatment in this field. At the same time, drugs have been developed targeting specific mutations, specifically BRAF, resulting in large responses in tumor regression (set up in this clinical study to 18 months), as well as a higher percentage of long-term survivors. The analysis of the gene expression changes and their correlation with clinical changes can be developed using the tools provided by those companies which currently provide gene expression platforms. The gene expression platform used in this clinical study is NanoString, which provides nCounter. However, nCounter has some limitations as the type of analysis is restricted to a predefined set, and the introduction of clinical features is a complex task. This paper presents an approach to collect the clinical information using a structured database and a Web user interface to introduce this information, including the results of the gene expression measurements, to go a step further than the nCounter tool. As part of this work, we present an initial analysis of changes in the gene expression of a set of patients before and after targeted therapy. This analysis has been carried out using Big Data technologies (Apache Spark) with the final goal being to scale up to large numbers of patients, even though this initial study has a limited number of enrolled patients (12 in the first analysis). This is not a Big Data problem, but the underlaying study aims at targeting 20 patients per year just in Málaga, and this could be extended to be used to analyze the 3.600 patients diagnosed with melanoma per year.

scholarly journals Melanoma expression analysis with Big Data technologies

2017 ◽  
Author(s):  
Alicia Fernandez-Rovira ◽  
Rocio Lavado ◽  
Miguel Ángel Berciano Guerrero ◽  
Ismael Navas-Delgado ◽  
José F Aldana-Montes
Keyword(s):  
Gene Expression ◽  
Big Data ◽  
Immune System ◽  
Clinical Study ◽  
Tumor Regression ◽  
Scale Up ◽  
Clinical Information ◽  
Initial Study ◽  
Expression Platform ◽  
Big Data Technologies

Melanoma is a highly immunogenic tumor. Therefore, in recent years physicians have incorporated drugs that alter the immune system into their therapeutic arsenal against this disease, revolutionizing in the treatment of patients in an advanced stage of the disease. This has led us to explore and deepen our knowledge of the immunology surrounding melanoma, in order to optimize its approach. At present, immunotherapy for metastatic melanoma is based on stimulating an individual’s own immune system through the use of specific monoclonal antibodies. The use of immunotherapy has meant that many of patients with melanoma have survived and therefore it constitutes a present and future treatment in this field. At the same time, drugs have been developed targeting specific mutations, specifically BRAF, resulting in large responses in tumor regression (set up in this clinical study to 18 months), as well as a higher percentage of long-term survivors. The analysis of the gene expression changes and their correlation with clinical changes can be developed using the tools provided by those companies which currently provide gene expression platforms. The gene expression platform used in this clinical study is NanoString, which provides nCounter. However, nCounter has some limitations as the type of analysis is restricted to a predefined set, and the introduction of clinical features is a complex task. This paper presents an approach to collect the clinical information using a structured database and a Web user interface to introduce this information, including the results of the gene expression measurements, to go a step further than the nCounter tool. As part of this work, we present an initial analysis of changes in the gene expression of a set of patients before and after targeted therapy. This analysis has been carried out using Big Data technologies (Apache Spark) with the final goal being to scale up to large numbers of patients, even though this initial study has a limited number of enrolled patients (12 in the first analysis). This is not a Big Data problem, but the underlaying study aims at targeting 20 patients per year just in Málaga, and this could be extended to be used to analyze the 3.600 patients diagnosed with melanoma per year.

scholarly journals Melanoma expression analysis with Big Data technologies

2017 ◽  
Author(s):  
Alicia Fernandez-Rovira ◽  
Rocio Lavado-Valenzuela ◽  
Miguel Ángel Berciano Guerrero ◽  
Ismael Navas-Delgado ◽  
José F Aldana-Montes
Keyword(s):  
Gene Expression ◽  
Big Data ◽  
Immune System ◽  
Clinical Study ◽  
Tumor Regression ◽  
Scale Up ◽  
Clinical Information ◽  
Initial Study ◽  
Expression Platform ◽  
Big Data Technologies

Melanoma is a highly immunogenic tumor. Therefore, in recent years physicians have incorporated drugs that alter the immune system into their therapeutic arsenal against this disease, revolutionizing in the treatment of patients in an advanced stage of the disease. This has led us to explore and deepen our knowledge of the immunology surrounding melanoma, in order to optimize its approach. At present, immunotherapy for metastatic melanoma is based on stimulating an individual’s own immune system through the use of specific monoclonal antibodies. The use of immunotherapy has meant that many of patients with melanoma have survived and therefore it constitutes a present and future treatment in this field. At the same time, drugs have been developed targeting specific mutations, specifically BRAF, resulting in large responses in tumor regression (set up in this clinical study to 18 months), as well as a higher percentage of long-term survivors. The analysis of the gene expression changes and their correlation with clinical changes can be developed using the tools provided by those companies which currently provide gene expression platforms. The gene expression platform used in this clinical study is NanoString, which provides nCounter. However, nCounter has some limitations as the type of analysis is restricted to a predefined set, and the introduction of clinical features is a complex task. This paper presents an approach to collect the clinical information using a structured database and a Web user interface to introduce this information, including the results of the gene expression measurements, to go a step further than the nCounter tool. As part of this work, we present an initial analysis of changes in the gene expression of a set of patients before and after targeted therapy. This analysis has been carried out using Big Data technologies (Apache Spark) with the final goal being to scale up to large numbers of patients, even though this initial study has a limited number of enrolled patients (12 in the first analysis). This is not a Big Data problem, but the underlaying study aims at targeting 20 patients per year just in Málaga, and this could be extended to be used to analyze the 3.600 patients diagnosed with melanoma per year.

The Mechanisms Behind the Biological Activity of Flavonoids

2019 ◽  
Vol 26 (39) ◽  
pp. 6976-6990 ◽  
Author(s):  
Ana María González-Paramás ◽  
Begoña Ayuda-Durán ◽  
Sofía Martínez ◽  
Susana González-Manzano ◽  
Celestino Santos-Buelga
Keyword(s):  
Gene Expression ◽  
Biological Activity ◽  
Phenolic Compounds ◽  
Intracellular Signaling ◽  
Molecular Mechanisms ◽  
Radical Scavenging ◽  
Model Organism ◽  
Stress Theory ◽  
Radical Scavenging Properties

: Flavonoids are phenolic compounds widely distributed in the human diet. Their intake has been associated with a decreased risk of different diseases such as cancer, immune dysfunction or coronary heart disease. However, the knowledge about the mechanisms behind their in vivo activity is limited and still under discussion. For years, their bioactivity was associated with the direct antioxidant and radical scavenging properties of phenolic compounds, but nowadays this assumption is unlikely to explain their putative health effects, or at least to be the only explanation for them. New hypotheses about possible mechanisms have been postulated, including the influence of the interaction of polyphenols and gut microbiota and also the possibility that flavonoids or their metabolites could modify gene expression or act as potential modulators of intracellular signaling cascades. This paper reviews all these topics, from the classical view as antioxidants in the context of the Oxidative Stress theory to the most recent tendencies related with the modulation of redox signaling pathways, modification of gene expression or interactions with the intestinal microbiota. The use of C. elegans as a model organism for the study of the molecular mechanisms involved in biological activity of flavonoids is also discussed.

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