scholarly journals A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism

2021 ◽  
Vol 17 (4) ◽  
pp. e1008891
Author(s):  
Linnea Österberg ◽  
Iván Domenzain ◽  
Julia Münch ◽  
Jens Nielsen ◽  
Stefan Hohmann ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Glucose Repression ◽  
Central Carbon Metabolism ◽  
Yeast Cells ◽  
Modeling Framework ◽  
Model Combining ◽  
Nutrient Signaling ◽  
Regulatory Effects ◽  
Comprehensive Framework ◽  
And Control

The interplay between nutrient-induced signaling and metabolism plays an important role in maintaining homeostasis and its malfunction has been implicated in many different human diseases such as obesity, type 2 diabetes, cancer, and neurological disorders. Therefore, unraveling the role of nutrients as signaling molecules and metabolites together with their interconnectivity may provide a deeper understanding of how these conditions occur. Both signaling and metabolism have been extensively studied using various systems biology approaches. However, they are mainly studied individually and in addition, current models lack both the complexity of the dynamics and the effects of the crosstalk in the signaling system. To gain a better understanding of the interconnectivity between nutrient signaling and metabolism in yeast cells, we developed a hybrid model, combining a Boolean module, describing the main pathways of glucose and nitrogen signaling, and an enzyme-constrained model accounting for the central carbon metabolism of Saccharomyces cerevisiae, using a regulatory network as a link. The resulting hybrid model was able to capture a diverse utalization of isoenzymes and to our knowledge outperforms constraint-based models in the prediction of individual enzymes for both respiratory and mixed metabolism. The model showed that during fermentation, enzyme utilization has a major contribution in governing protein allocation, while in low glucose conditions robustness and control are prioritized. In addition, the model was capable of reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression, as well as regulatory effects associated with lifespan increase during caloric restriction. Overall, we show that our hybrid model provides a comprehensive framework for the study of the non-trivial effects of the interplay between signaling and metabolism, suggesting connections between the Snf1 signaling pathways and processes that have been related to chronological lifespan of yeast cells.

scholarly journals A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism

2020 ◽  
Author(s):  
Linnea Österberg ◽  
Iván Domenzain ◽  
Julia Münch ◽  
Jens Nielsen ◽  
Stefan Hohmann ◽  
...  
Keyword(s):  
Neurological Disorders ◽  
Regulatory Network ◽  
Glucose Repression ◽  
Hybrid Modeling ◽  
Human Diseases ◽  
Crabtree Effect ◽  
Modeling Framework ◽  
Chronological Lifespan ◽  
Regulatory Effects ◽  
Constraint Model

AbstractThe interplay between nutrient-induced signaling and metabolism plays an important role in maintaining homeostasis and its malfunction has been implicated in many different human diseases such as obesity, type 2 diabetes, cancer and neurological disorders. Therefore, unravelling the role of nutrients as signaling molecules and metabolites as well as their interconnectivity may provide a deeper understanding of how these conditions occur. Both signalling and metabolism have been extensively studied using various systems biology approaches. However, they are mainly studied individually and in addition current models lack both the complexity of the dynamics and the effects of the crosstalk in the signaling system. To gain a better understanding of the interconnectivity between nutrient signaling and metabolism, we developed a hybrid model, combining Boolean model, describing the signalling layer and the enzyme constraint model accounting for metabolism using a regulatory network as a link. The model was capable of reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression. We show that using this methodology one can investigat intrinsically different systems, such as signaling and metabolism, in the same model and gain insight into how the interplay between them can have non-trivial effects by showing a connection between Snf1 signaling and chronological lifespan by the regulation of NDE and NDI usage in respiring conditions. In addition, the model showed that during fermentation, enzyme utilization is the more important factor governing the protein allocation, while in low glucose conditions robustness and control is prioritized.Author summaryElucidating the complex relationship between nutrient-induced signaling and metabolism represents a key in understanding the onset of many different human diseases like obesity, type 3 diabetes, cancer and many neurological disorders. In this work we proposed a hybrid modeling approach, combining Boolean representation of singaling pathways, like Snf11, TORC1 and PKA with the enzyme constrained model of metabolism linking them via the regulatory network. This allowed us to improve individual model predictions and elucidate how single components in the dynamic signaling layer affect the steady-state metabolism. The model has been tested under respiration and fermentation, reveling novel connections and further reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression. Finally, we show a connection between Snf1 signaling and chronological lifespan by the regulation of NDE and NDI usage in respiring conditions.

scholarly journals Krishimitr: A Cloud Computing and Platform for Disease Detection in Agriculture

2019 ◽  
Vol 8 (12) ◽  
pp. 2967-2970
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Sustainable Agriculture ◽  
Hybrid Model ◽  
Plant Diseases ◽  
Disease Detection ◽  
Model Combining ◽  
Comprehensive Framework

Automating disease detection is a cornerstone in the journey to achieving sustainable agriculture. We describe a framework utilizing Machine Learning, Cloud Computing and Internet-of-Things which brings experts to farmers, allowing for timely detection of diseases. This innovative and comprehensive framework provides agronomists and farmers with a solution for diagnosing plant diseases. By leveraging modern ICT capabilities, this extensible framework is currently trained for over 15 plant types and more than 51 disease types. Our framework employs a hybrid model combining use of both online and offline resources to provide up-to-date information to farmers even in case of patchy connectivity

Building an integrated AI and mathmatical modeling framework for online supervision and control of water resource recovery facilities

2018 ◽  
Vol 2018 (10) ◽  
pp. 4025-4028
Author(s):  
Jose Porro ◽  
Chaïm De Mulder ◽  
Youri Amerlinck ◽  
Elena Torfs ◽  
Sophie Balemans ◽  
...  
Keyword(s):  
Water Resource ◽  
Resource Recovery ◽  
Modeling Framework ◽  
Online Supervision ◽  
And Control

Metabolic phenotyping of the Yersinia high-pathogenicity island that regulates central carbon metabolism

The Analyst ◽  
2015 ◽  
Vol 140 (10) ◽  
pp. 3356-3361 ◽  
Author(s):  
Leyu Yan ◽  
Wenna Nie ◽  
Haitao Lv
Keyword(s):  
Carbon Metabolism ◽  
Virulence Genes ◽  
Pathogenicity Island ◽  
Central Carbon Metabolism ◽  
Metabolic Phenotyping ◽  
High Pathogenicity ◽  
Central Carbon ◽  
Regulatory Effects

The regulatory effects of the HPI virulence genes on central carbon metabolism differentiate UPEC from non-UPEC.

A new hybrid model combining EMD and neural network for multi-step ahead load forecasting

2021 ◽  
pp. 1-16
Author(s):  
Hasmat Malik ◽  
Majed A. Alotaibi ◽  
Abdulaziz Almutairi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Hybrid Model ◽  
Storage System ◽  
Load Forecasting ◽  
Model Performance ◽  
Energy Storage System ◽  
Electric Load ◽  
Real Time Analysis ◽  
Model Combining

The electric load forecasting (ELF) is a key area of the modern power system (MPS) applications and also for the virtual power plant (VPP) analysis. The ELF is most prominent for the distinct applications of MPS and VPP such as real-time analysis of energy storage system, distributed energy resources, demand side management and electric vehicles etc. To manage the real-time challenges and map the stable power demand, in different time steps, the ELF is evaluated in yearly, monthly, weekly, daily, and hourly, etc. basis. In this study, an intelligent load predictor which is able to forecast the electric load for next month or day or hour is proposed. The proposed approach is a hybrid model combining empirical mode decomposition (EMD) and neural network (NN) for multi-step ahead load forecasting. The model performance is demonstrated by suing historical dataset collected form GEFCom2012 and GEFCom2014. For the demonstration of the performance, three case studies are analyzed into two categories. The demonstrated results represents the higher acceptability of the proposed approach with respect to the standard value of MAPE (mean absolute percent error).

GRR1 of Saccharomyces cerevisiae is required for glucose repression and encodes a protein with leucine-rich repeats

1991 ◽  
Vol 11 (10) ◽  
pp. 5101-5112
Author(s):  
J S Flick ◽  
M Johnston
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Protein Interactions ◽  
Tandem Repeats ◽  
Glucose Repression ◽  
Molecular Data ◽  
Primary Response ◽  
Yeast Cells ◽  
Cell Fractionation ◽  
Protein Protein Interactions ◽  
Yeast Saccharomyces Cerevisiae

Growth of the yeast Saccharomyces cerevisiae on glucose leads to repression of transcription of many genes required for alternative carbohydrate metabolism. The GRR1 gene appears to be of central importance to the glucose repression mechanism, because mutations in GRR1 result in a pleiotropic loss of glucose repression (R. Bailey and A. Woodword, Mol. Gen. Genet. 193:507-512, 1984). We have isolated the GRR1 gene and determined that null mutants are viable and display a number of growth defects in addition to the loss of glucose repression. Surprisingly, grr1 mutations convert SUC2, normally a glucose-repressed gene, into a glucose-induced gene. GRR1 encodes a protein of 1,151 amino acids that is expressed constitutively at low levels in yeast cells. GRR1 protein contains 12 tandem repeats of a sequence similar to leucine-rich motifs found in other proteins that may mediate protein-protein interactions. Indeed, cell fractionation studies are consistent with this view, suggesting that GRR1 protein is tightly associated with a particulate protein fraction in yeast extracts. The combined genetic and molecular data are consistent with the idea that GRR1 protein is a primary response element in the glucose repression pathway and is required for the generation or interpretation of the signal that induces glucose repression.

scholarly journals The influence of carbohydrates in the interaction of Paracoccidioides brasiliensis with CCL-6 cells in vitro

2012 ◽  
Vol 45 (6) ◽  
pp. 739-744 ◽  
Author(s):  
Francisco Laurindo da Silva ◽  
Raphael Sanzio Pimenta ◽  
Juliana Fonseca Moreira da Silva ◽  
Déborah Aparecida Negrão Corrêa ◽  
Ary Corrêa Junior
Keyword(s):  
Epithelial Cell ◽  
Paracoccidioides Brasiliensis ◽  
Yeast Cells ◽  
Epithelial Cell Line ◽  
Con A ◽  
Culture Methods ◽  
Disease Treatment ◽  
Adhesion Behavior ◽  
And Control

INTRODUCTION: Little is known about the early events in the interaction between Paracoccidioides brasiliensis and its host. To understand the effect of carbohydrates in the interaction between the fungus and epithelial cell in culture, we analyzed the influence of different carbohydrate solutions on the adhesion of P. brasiliensis yeast cells to CCL-6 cells in culture. METHODS: Fungal cells were cultivated with the epithelial cell line, and different concentrations of D-fucose, N-acetyl-glucosamine, D-mannose, D-glucosamine, D-galactosamine, sorbitol and fructose were added at the beginning of the experiment. Six hours after the treatment, the cells were fixed and observed by light microscopy. The number of P. brasiliensis cells that were adhered to the CCL-6 monolayer was estimated. RESULTS: The number of adhesion events was diminished following treatments with D-fucose, N-acetyl-glucosamine, D-mannose, D-glucosamine and D-galactosamine as compared to the untreated controls. Sorbitol and fructose-treated cells had the same adhesion behavior as the observed in the control. P. brasiliensis propagules were treated with fluorescent lectins. The FITC-labeled lectins WGA and Con-A bound to P. brasiliensis yeast cells, while SBA and PNA did not. CONCLUSIONS: The perceptual of adhesion between P. brasiliensis and CCL-6 cells decreased with the use of D-mannose, N-acetyl-glucosamine and D-glucosamine. The assay using FITC-labeled lectins suggests the presence of N-acetyl-glucosamine, α-mannose and α-glucose on the P. brasiliensis cell surface. An enhanced knowledge of the mediators of adhesion on P. brasiliensis could be useful in the future for the development of more efficient and less harmful methods for disease treatment and control.

scholarly journals Adaptation of Cryptococcus neoformans to Mammalian Hosts: Integrated Regulation of Metabolism and Virulence

2011 ◽  
Vol 11 (2) ◽  
pp. 109-118 ◽  
Author(s):  
Jim Kronstad ◽  
Sanjay Saikia ◽  
Erik David Nielson ◽  
Matthias Kretschmer ◽  
Wonhee Jung ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Cryptococcus Neoformans ◽  
Central Carbon Metabolism ◽  
Yeast Cells ◽  
Phagocytic Cells ◽  
Content Type ◽  
The Central Nervous System ◽  
Mechanisms Of Adaptation ◽  
Complex Integration

ABSTRACTThe basidiomycete fungusCryptococcus neoformansinfects humans via inhalation of desiccated yeast cells or spores from the environment. In the absence of effective immune containment, the initial pulmonary infection often spreads to the central nervous system to result in meningoencephalitis. The fungus must therefore make the transition from the environment to different mammalian niches that include the intracellular locale of phagocytic cells and extracellular sites in the lung, bloodstream, and central nervous system. Recent studies provide insights into mechanisms of adaptation during this transition that include the expression of antiphagocytic functions, the remodeling of central carbon metabolism, the expression of specific nutrient acquisition systems, and the response to hypoxia. Specific transcription factors regulate these functions as well as the expression of one or more of the major known virulence factors ofC. neoformans. Therefore, virulence factor expression is to a large extent embedded in the regulation of a variety of functions needed for growth in mammalian hosts. In this regard, the complex integration of these processes is reminiscent of the master regulators of virulence in bacterial pathogens.

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