Strategies to overcome interferences during biomass monitoring with dielectric spectroscopy

Dielectric spectroscopy is extensively used to measure the level of viable biomass during fermentations but can suffer from interference by a variety of factors including the presence of dead cells, bubbles, electric and magnetic fields, changes in the medium composition, conductivity changes and the presence of non-cellular particles. Three different approaches were used to overcome these problems. The first involved the separate measurement of the spectra of the interferent and the cells. If the spectra were significantly different then spectra containing the signals of both cells and the interferent could be deconvoluted to separately determine the relative contribution of the cells and the interferent to the spectra. This deconvolution approach was successfully used to estimate the biomass levels of yeast in the presence of spent grains of barley and hardwood in the medium. A similar approach allowed the interference of electrode polarisation on spectra of yeast and microalgae to be compensated for. An attempt to determine the concentration of non-viable cells in a mixture of dead and live cells was less successful because the signal of the non-viable cells was quite small compared to that of viable cells. A second approach involved the use of a filter to keep the interferent away from the probe surface. This was used successfully in the measurement of the yeast concentration in the presence of spent barley grains. A third approach involved the use of a second sensor in addition to the biomass sensor. This allows the signal of the biomass sensor to be compensated for the interferent. In one set of experiments microelectrodes were developed which were able to confine the electric field to a small volume near the electrode surface. Covering the electrode surface with a gel or a membrane stopped cells from entering this volume whilst allowing medium to diffuse through. This allowed the measurement of changes in the electrical properties of the medium without a contribution by the cells. Whilst this approach worked, the response time was too long for practical use. More successful was the simultaneous measurement of the biomass with an infrared optical probe and a dielectric probe. It was found that the signal of the optical probe was independent of the cell viability, whilst the dielectric probe was quite insensitive to non-viable cells. The combined use of the dielectric probe and the optical probe allowed the culture viability to be determined in a straightforward manner

Finding directionality and gene-disease predictions in disease associations

Understanding the underlying molecular mechanisms in human diseases is important for diagnosis and treatment of complex conditions and has traditionally been done by establishing associations between disorder-genes and their associated diseases. This kind of network analysis usually includes only the interaction of molecular components and shared genes. The present study offers a network and association analysis under a bioinformatics frame involving the integration of HUGO Gene Nomenclature Committee approved gene symbols, KEGG metabolic pathways and ICD-10-CM codes for the analysis of human diseases based on the level of inclusion and hypergeometric enrichment between genes and metabolic pathways shared by the different human disorders. Methods: The present study offers the integration of HGNC approved gene symbols, KEGG metabolic pathways andICD-10-CM codes for the analysis of associations based on the level of inclusion and hypergeometricenrichment between genes and metabolic pathways shared by different diseases. Results: 880 unique ICD-10-CM codes were mapped to the 4315 OMIM phenotypes and 3083 genes with phenotype-causing mutation. From this, a total of 705 ICD-10-CM codes were linked to 1587 genes with phenotype-causing mutations and 801 KEGG pathways creating a tripartite network composed by 15,455 code-gene-pathway interactions. These associations were further used for an inclusion analysis between diseases along with gene-disease predictions based on a hypergeometric enrichment methodology. Conclusions: The results demonstrate that even though a large number of genes and metabolic pathways are shared between diseases of the same categories, inclusion levels between these genes and pathways are directional and independent of the disease classification. However, the gene-disease-pathway associations can be used for prediction of new gene-disease interactions that will be useful in drug discovery and therapeutic applications

BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data

Analysis of large data sets using computational and mathematical tools have become a central part of biological sciences. Large amounts of data are being generated each year from different biological research fields leading to a constant development of software and algorithms aimed to deal with the increasing creation of information. The BioMet Toolbox 2.0 integrates a number of functionalities in a user-friendly environment enabling the user to work with biological data in a web interface. The unique and distinguishing feature of the BioMet Toolbox 2.0 is to provide a web user interface to tools for metabolic pathways and omics analysis developed under different platform-dependent environments enabling easy access to these computational tools

La enseñanza de la arquitectura en primer año: estudios comparados

Esta comunicación hace parte del proyecto de investigación: “La enseñanza de la arquitectura en primer año”, adelantado por el grupo de investigación en Arquitectura, Ciudad y Educación -ACE, del programa de Arquitectura de la Universidad de los Andes, Bogotá-Colombia. El proyecto se origina a partir del compromiso con la educación en arquitectura y en particular con la formación en la etapa inicial, primer año o ciclo básico. Plantea la revisión de planes de estudio en diversas escuelas con el propósito de analizar los contenidos (proyectuales, históricos, técnicos, urbanísticos), las metodologías de transmisión de conocimiento y proponer un modelo de enseñanza que permita orientar estrategias aplicables en diversos planes curriculares, además de construir un material de referencia útil sobre educación en arquitectura. Esta comunicación se centra en la apertura de cuatro temáticas transversales que permiten orientar la discusión a problemáticas comunes susceptibles de ser verificadas en las diferentes áreas de conocimiento en que se compartimentan los programas de arquitectura, en particular en primer año de formación. The following proposal is part of the research project: “First Year Architectural Education”, lead by the research group in Architecture, City and Education-ACE, of the Architecture program at Universidad de los Andes, Bogotá-Colombia. The project emerges from the commitment to education in architecture and in particular with the training in the initial stage: the first year. The project intends to review curricula at various schools, extract their contents (architectural design, history, construction and urbanism related) and their methodologies of knowledge transfer, in order to propose a teaching model that suggests strategies applicable in various curricula, as well as to contribute knowledge on architectural education. This paper focuses on the proposals of four cross-subjects or common problems in different knowledge areas in which architecture teaching use to be divided, especially in its first year

Glycolytic flux in Saccharomyces cerevisiae is dependent on RNA polymerase III and its negative regulator Maf1.

Protein biosynthesis is energetically costly, is tightly regulated and is coupled to stress conditions including glucose deprivation. RNA polymerase III (RNAP III)-driven transcription of tDNA genes for production of tRNAs is a key element in efficient protein biosynthesis. Here we present an analysis of the effects of altered RNAP III activity on the Saccharomyces cerevisiae proteome and metabolism under glucose-rich conditions. We show for the first time that RNAP III is tightly coupled to the glycolytic system at the molecular systems level. Decreased RNAP III activity or the absence of the RNAP III negative regulator, Maf1 elicit broad changes in the abundance profiles of enzymes engaged in fundamental metabolism in S. cerevisiae In a mutant compromised in RNAP III activity, there is a repartitioning towards amino acids synthesis de novo at the expense of glycolytic throughput. Conversely, cells lacking Maf1 protein have greater potential for glycolytic flu

Single cell analyses identify a highly regenerative and homogenous human CD34+ hematopoietic stem cell population

The heterogeneous nature of human CD34+ hematopoietic stem cells (HSCs) has hampered our understanding of the cellular and molecular trajectories that HSCs navigate during lineage commitment. Using various platforms including single cell RNA-sequencing and extensive xenotransplantation, we have uncovered an uncharacterized human CD34+ HSC population. These CD34+EPCR+(CD38/CD45RA)− (simply as EPCR+) HSCs have a high repopulating and self-renewal abilities, reaching a stem cell frequency of ~1 in 3 cells, the highest described to date. Their unique transcriptomic wiring in which many gene modules associated with differentiated cell lineages confers their multilineage lineage output both in vivo and in vitro. At the single cell level, EPCR+ HSCs are the most transcriptomically and functionally homogenous human HSC population defined to date and can also be easily identified in post-natal tissues. Therefore, this EPCR+ population not only offers a high human HSC resolution but also a well-structured human hematopoietic hierarchical organization at the most primitive level

Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies.

When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation-known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells

A quantitative and temporal map of proteostasis during heat shock in Saccharomyces cerevisiae

Temporal changes in the yeast proteome under heat stress are mapped and integrated to protein networks to reveal cognate groups of chaperones (orange and blue circles) acting on coherent groups of substrate proteins (red and green).</p