Metabolic Systems: Substrate Utilization

2014 ◽  
Author(s):  
Andrew R. Coggan
Keyword(s):  
Substrate Utilization ◽  
Metabolic Systems

Glucocorticoid treatment rapidly increases AgRP and food intake with delayed effects on other metabolic systems

2016 ◽  
Author(s):  
Erika Harno ◽  
Alison Davies ◽  
Tiffany-Jayne Allen ◽  
Charlotte Sefton ◽  
Jonathan R Wray ◽  
...  
Keyword(s):  
Food Intake ◽  
Glucocorticoid Treatment ◽  
Delayed Effects ◽  
Metabolic Systems

COMPARATIVE ANALYSIS OF THE PHYSICAL PERFORMANCE VARIABILITY IN ATHLETES OF DIFFERENT BIORHYTHMOTYPES

2020 ◽  
pp. 5-10
Keyword(s):  
Comparative Analysis ◽  
Physical Performance ◽  
Mechanical Work ◽  
Performance Variability ◽  
Metabolic Systems ◽  
Account Planning

The variability of physical performance in representatives of various biorhythmotypes at different times of the day was studied. It has been revealed that the efficiency of metabolic systems during hours of functional optimum makes it possible to carry out large (by volume and power) physical loads, which indicates greater efficiency of mechanical work in the corresponding period. Taking these circumstances into account, planning of training loads avoids the effect of overtraining and leads to an increase in the overall level of physical performance

Microbiological Detoxification of Hazardous Organic Contaminants: The Crucial Role of Substrate Interactions

1992 ◽  
Vol 25 (11) ◽  
pp. 403-410 ◽  
Author(s):  
B. E. Rittmann
Keyword(s):  
Organic Pollutants ◽  
Information Flow ◽  
Organic Contaminants ◽  
Crucial Role ◽  
Substrate Utilization ◽  
Energy Flows ◽  
Hazardous Pollutants ◽  
Primary Substrate ◽  
Substrate Types

Microbiological detoxification of hazardous organic pollutants is highly promising, but its reliable implementation requires a sophisticated understanding of several different substrate types and how they interact. This paper carefully defines the substrate types and explains how their interactions affect the bacteria's electron and energy flows, information flow, and degradative activity. For example, primary substrates, which are essential for growth and maintenance of the bacteria, also interact with degradation of specific hazardous pollutants by being inducers, inhibitors, and direct or indirect cosubstrates. The target contaminants, which often are secondary substrates, also have the interactive roles of self-inhibitor, inhibitor of primary-substrate utilization, inducer, and a part of an aggregate primary substrate.

Applying phenotypic fingerprinting in the management of wastewater treatment systems

1998 ◽  
Vol 37 (4-5) ◽  
pp. 461-464 ◽  
Author(s):  
C. A. Schneider ◽  
K. Mo ◽  
S. N. Liss
Keyword(s):  
Paper Mill ◽  
Substrate Utilization ◽  
Pulp And Paper ◽  
Effluent Treatment ◽  
Management Tool ◽  
Carbon Substrate ◽  
Pulp And Paper Mill ◽  
Fame Analysis ◽  
Pulp And Paper Effluent ◽  
Biolog Gn

Carbon substrate utilization profiles, phenotypic fingerprints, of microbial communities from different pulp and paper effluent treatment systems are being determined using Biolog plates. The substrates from the Biolog GN plates that were deemed to be most significant in differentiating between communities are being employed as substrate panels on Biolog MT plates. Correlative microbiological tests including FAME analysis, heterotrophic plate counts, and epifluorescent microscopy are performed on the samples. By correlating the phenotypic fingerprints to pulp and paper mill processes and operation parameters in the treatment systems, the carbon substrate utilization profile has shown potential as a useful management tool.

Modeling mass transfer and substrate utilization in the boundary layer of biofilm systems

1998 ◽  
Vol 37 (4-5) ◽  
pp. 139-147 ◽  
Author(s):  
Harald Horn ◽  
Dietmar C. Hempel
Keyword(s):  
Boundary Layer ◽  
Mass Transfer ◽  
Reynolds Number ◽  
Film Theory ◽  
Substrate Utilization ◽  
The Other ◽  
Concentration Profiles ◽  
Hydrodynamic Conditions ◽  
Time Axis ◽  
Transfer Coefficients

The use of microelectrodes in biofilm research allows a better understanding of intrinsic biofilm processes. Little is known about mass transfer and substrate utilization in the boundary layer of biofilm systems. One possible description of mass transfer can be obtained by mass transfer coefficients, both on the basis of the stagnant film theory or with the Sherwood number. This approach is rather formal and not quite correct when the heterogeneity of the biofilm surface structure is taken into account. It could be shown that substrate loading is a major factor in the description of the development of the density. On the other hand, the time axis is an important factor which has to be considered when concentration profiles in biofilm systems are discussed. Finally, hydrodynamic conditions become important for the development of the biofilm surface when the Reynolds number increases above the range of 3000-4000.

Learning from metabolic networks: current trends and future directions for precision medicine.

2020 ◽  
Vol 28 ◽  
Author(s):  
Ilaria Granata ◽  
Mario Manzo ◽  
Ari Kusumastuti ◽  
Mario R Guarracino
Keyword(s):  
Systems Biology ◽  
Precision Medicine ◽  
Metabolic Networks ◽  
Information Modeling ◽  
Model Systems ◽  
Specific Information ◽  
Important Indicator ◽  
Predictive Values ◽  
Metabolic Systems ◽  
Metabolic Models

Purpose: Systems biology and network modeling represent, nowadays, the hallmark approaches for the development of predictive and targeted-treatment based precision medicine. The study of health and disease as properties of the human body system allows the understanding of the genotype-phenotype relationship through the definition of molecular interactions and dependencies. In this scenario, metabolism plays a central role as its interactions are well characterized and it is considered an important indicator of the genotype-phenotype associations. In metabolic systems biology, the genome-scale metabolic models are the primary scaffolds to integrate multi-omics data as well as cell-, tissue-, condition-specific information. Modeling the metabolism has both investigative and predictive values. Several methods have been proposed to model systems, which involve steady-state or kinetic approaches, and to extract knowledge through machine and deep learning. Method: This review collects, analyzes, and compares the suitable data and computational approaches for the exploration of metabolic networks as tools for the development of precision medicine. To this extent, we organized it into three main sections: "Data and Databases", "Methods and Tools", and "Metabolic Networks for medicine". In the first one, we have collected the most used data and relative databases to build and annotate metabolic models. In the second section, we have reported the state-of-the-art methods and relative tools to reconstruct, simulate, and interpret metabolic systems. Finally, we have reported the most recent and innovative studies which exploited metabolic networks for the study of several pathological conditions, not only those directly related to the metabolism. Conclusion: We think that this review can be a guide to researchers of different disciplines, from computer science to biology and medicine, in exploring the power, challenges and future promises of the metabolism as predictor and target of the so-called P4 medicine (predictive, preventive, personalized and participatory).

scholarly journals β1-blocker in sepsis

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Daisuke Hasegawa ◽  
Ryota Sato ◽  
Osamu Nishida
Keyword(s):  
Heart Rate ◽  
Oxygen Delivery ◽  
Sinus Tachycardia ◽  
Meta Analysis ◽  
Lactate Production ◽  
Volume Index ◽  
Initial Resuscitation ◽  
Randomized Controlled ◽  
Metabolic Systems

Abstract Background The use of ultrashort-acting β1-blockers recently has attracted attention in septic patients with non-compensatory tachycardia. We summarized the metabolic and hemodynamic effects and the clinical evidence of ultrashort-acting β1-blockers. Main body A recent meta-analysis showed that ultrashort-acting β1-blockers reduced the mortality in septic patients with persistent tachycardia. However, its mechanism to improve mortality is not fully understood yet. We often use lactate as a marker of oxygen delivery, but an impaired oxygen use rather than reduced oxygen delivery has been recently proposed as a more reasonable explanation of hyperlactatemia in patients with sepsis, leading to a question of whether β1-blockers affect metabolic systems. While the stimulation of the β2-receptor accelerates glycolysis and lactate production, the role of β1-blocker in lactate production remains unclear and studies investigating the role of β1-blockers in lactate kinetics are warranted. A meta-analysis also reported that ultrashort-acting β1-blockers increased stroke volume index, while it reduced heart rate, resulting in unchanged cardiac index, mean arterial pressure, and norepinephrine requirement at 24 h, leading to an improvement of cardiovascular efficiency. On the other hand, a recent study reported that heart rate reduction using fast esmolol titration in the very early phase of septic shock caused hemodynamic instability, suggesting that ultrashort-acting β1-blockers should be started only after completing initial resuscitation. While many clinicians still do not feel comfortable controlling sinus tachycardia, one randomized controlled trial in which the majority had sinus tachycardia suggested the mortality benefit of ultrashort-acting β1-blockers. Therefore, it still deems to be reasonable to control sinus tachycardia with ultrashort-acting β1-blockers after completing initial resuscitation. Conclusion Accumulating evidence is supporting the use of ultrashort-acting β1-blockers while larger randomized controlled trials to clarify the effect of ultrashort-acting β1-blockers are still warranted.

scholarly journals SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Y. Lee ◽  
Britney Nguyen ◽  
Carlos Orosco ◽  
Mark P. Styczynski
Keyword(s):  
Machine Learning ◽  
Metabolic Networks ◽  
Sampling Frequency ◽  
Low Noise ◽  
Training Data ◽  
High Noise ◽  
Regulatory Interactions ◽  
Learning Framework ◽  
Metabolic Systems ◽  
Noise Data

Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

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