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 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 Multi-omic regulatory networks capture downstream effects of kinase inhibition in Mycobacterium tuberculosis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Albert T. Young ◽  
Xavier Carette ◽  
Michaela Helmel ◽  
Hanno Steen ◽  
Robert N. Husson ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Mycobacterium Tuberculosis ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Protein Interaction Data ◽  
Kinase Inhibition ◽  
Protein Protein Interaction ◽  
Downstream Effects ◽  
Regulate Cell Growth ◽  
Regulatory Effects

AbstractThe ability of Mycobacterium tuberculosis (Mtb) to adapt to diverse stresses in its host environment is crucial for pathogenesis. Two essential Mtb serine/threonine protein kinases, PknA and PknB, regulate cell growth in response to environmental stimuli, but little is known about their downstream effects. By combining RNA-Seq data, following treatment with either an inhibitor of both PknA and PknB or an inactive control, with publicly available ChIP-Seq and protein–protein interaction data for transcription factors, we show that the Mtb transcription factor (TF) regulatory network propagates the effects of kinase inhibition and leads to widespread changes in regulatory programs involved in cell wall integrity, stress response, and energy production, among others. We also observe that changes in TF regulatory activity correlate with kinase-specific phosphorylation of those TFs. In addition to characterizing the downstream regulatory effects of PknA/PknB inhibition, this demonstrates the need for regulatory network approaches that can incorporate signal-driven transcription factor modifications.

Modeling the response of non-perennial streams to climate change impact: The Bouregreg watershed in Morocco

2021 ◽  
Author(s):  
Anna Maria De Girolamo ◽  
Youssef Brouziyne ◽  
Lahcen Benaabidate ◽  
Aziz Aboubdillah ◽  
Ali El Bilali ◽  
...  
Keyword(s):  
Climate Change ◽  
Assessment Tool ◽  
Swat Model ◽  
Circulation Model ◽  
Hybrid Modeling ◽  
Climate Data ◽  
Global Circulation Model ◽  
Stream Network ◽  
Modeling Framework ◽  
Modeling Approach

<p>The non-perennial streams and rivers are predominant in the Mediterranean region and play an important ecological role in the ecosystem diversity in this region. This class of streams is particularly vulnerable to climate change effects that are expected to amplify further under most climatic projections. Understanding the potential response of the hydrologic regime attributes to climatic stress helps in planning better conservation and management strategies. Bouregreg watershed (BW) in Morocco, is a strategic watershed for the region with a developed non-perennial stream network, and with typical assets and challenges of most Mediterranean watersheds. In this study, a hybrid modeling approach, based on the Soil and Water Assessment Tool (SWAT) model and Indicator of Hydrologic Alteration (IHA) program, was used to simulate the response of BW's stream network to climate change during the period: 2035-2050. Downscaled daily climate data from the global circulation model CNRM-CM5 were used to force the hybrid modeling framework over the study area. Results showed that, under the changing climate, the magnitude of the alteration will be different across the stream network; however, almost the entire flow regime attributes will be affected. Under the RCP8.5 scenario, the average number of zero-flow days will rise up from 3 to 17.5 days per year in some streams, the timing of the maximum flow was calculated to occur earlier by 17 days than in baseline, and the timing of the minimal flow should occur later by 170 days in some streams. The used modeling approach in this study contributed in identifying the most vulnerable streams in the BW to climate change for potential prioritization in conservation plans.</p>

scholarly journals Advances in the Identification of Circular RNAs and Research Into circRNAs in Human Diseases

2021 ◽  
Vol 12 ◽  
Author(s):  
Shihu Jiao ◽  
Song Wu ◽  
Shan Huang ◽  
Mingyang Liu ◽  
Bo Gao
Keyword(s):  
Neurological Disorders ◽  
Closed Loop ◽  
Significant Proportion ◽  
Human Diseases ◽  
Circular Rnas ◽  
The Status ◽  
Research Questions ◽  
Non Coding Rnas ◽  
Microrna Sponges

Circular RNAs (circRNAs) are a class of endogenous non-coding RNAs (ncRNAs) with a closed-loop structure that are mainly produced by variable processing of precursor mRNAs (pre-mRNAs). They are widely present in all eukaryotes and are very stable. Currently, circRNA studies have become a hotspot in RNA research. It has been reported that circRNAs constitute a significant proportion of transcript expression, and some are significantly more abundantly expressed than other transcripts. CircRNAs have regulatory roles in gene expression and critical biological functions in the development of organisms, such as acting as microRNA sponges or as endogenous RNAs and biomarkers. As such, they may have useful functions in the diagnosis and treatment of diseases. CircRNAs have been found to play an important role in the development of several diseases, including atherosclerosis, neurological disorders, diabetes, and cancer. In this paper, we review the status of circRNA research, describe circRNA-related databases and the identification of circRNAs, discuss the role of circRNAs in human diseases such as colon cancer, atherosclerosis, and gastric cancer, and identify remaining research questions related to circRNAs.

A Framework for Energy-Based Kinetostatic Modeling of Compliant Mechanisms

2017 ◽  
Author(s):  
Guimin Chen ◽  
Fulei Ma ◽  
Ruiyu Bai ◽  
Spencer P. Magleby ◽  
Larry L. Howell
Keyword(s):  
Strain Energy ◽  
Compliant Mechanisms ◽  
Modeling Framework ◽  
Geometric Nonlinearities ◽  
Complementary Strain ◽  
Minimum Strain Energy ◽  
Constraint Model ◽  
Future Work

Although energy-based methods have advantages over the Newtonian methods for kinetostatic modeling, the geometric nonlinearities inherent in deflections of compliant mechanisms preclude most of the energy-based theorems. Castigliano’s first theorem and the Crotti-Engesser theorem, which don’t require the problem being solved to be linear, are selected to construct the energy-based kinetostatic modeling framework for compliant mechanisms in this work. Utilization of these two theorems requires explicitly formulating the strain energy in terms of deflections and the complementary strain energy in terms of loads, which are derived based on the beam constraint model. The kinetostatic modeling of two compliant mechanisms are provided to demonstrate the effectiveness of using Castigliano’s first theorem and the Crotti-Engesser theorem with the explicit formulations in this framework. Future work will be focused on incorporating use of the principle of minimum strain energy and the principle of minimum complementary strain energy.

scholarly journals Regulatory Network Connecting Two Glucose Signal Transduction Pathways in Saccharomyces cerevisiae

2004 ◽  
Vol 3 (1) ◽  
pp. 221-231 ◽  
Author(s):  
Aneta Kaniak ◽  
Zhixiong Xue ◽  
Daniel Macool ◽  
Jeong-Ho Kim ◽  
Mark Johnston
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Signal Transduction ◽  
Transcription Factor ◽  
Regulatory Network ◽  
Target Genes ◽  
Glucose Repression ◽  
Signal Transduction Pathways ◽  
Glucose Signaling ◽  
Genes Encoding ◽  
Glucose Induction

ABSTRACT The yeast Saccharomyces cerevisiae senses glucose, its preferred carbon source, through multiple signal transduction pathways. In one pathway, glucose represses the expression of many genes through the Mig1 transcriptional repressor, which is regulated by the Snf1 protein kinase. In another pathway, glucose induces the expression of HXT genes encoding glucose transporters through two glucose sensors on the cell surface that generate an intracellular signal that affects function of the Rgt1 transcription factor. We profiled the yeast transcriptome to determine the range of genes targeted by this second pathway. Candidate target genes were verified by testing for Rgt1 binding to their promoters by chromatin immunoprecipitation and by measuring the regulation of the expression of promoter lacZ fusions. Relatively few genes could be validated as targets of this pathway, suggesting that this pathway is primarily dedicated to regulating the expression of HXT genes. Among the genes regulated by this glucose signaling pathway are several genes involved in the glucose induction and glucose repression pathways. The Snf3/Rgt2-Rgt1 glucose induction pathway contributes to glucose repression by inducing the transcription of MIG2, which encodes a repressor of glucose-repressed genes, and regulates itself by inducing the expression of STD1, which encodes a regulator of the Rgt1 transcription factor. The Snf1-Mig1 glucose repression pathway contributes to glucose induction by repressing the expression of SNF3 and MTH1, which encodes another regulator of Rgt1, and also regulates itself by repressing the transcription of MIG1. Thus, these two glucose signaling pathways are intertwined in a regulatory network that serves to integrate the different glucose signals operating in these two pathways.

scholarly journals An Energy-Based Framework for Nonlinear Kinetostatic Modeling of Compliant Mechanisms Utilizing Beam Flexures

2021 ◽  
pp. 1-18
Author(s):  
Guimin Chen ◽  
Fulei Ma ◽  
Ruiyu Bai ◽  
Weidong Zhu ◽  
Spencer P Magleby ◽  
...  
Keyword(s):  
Strain Energy ◽  
Compliant Mechanisms ◽  
Modeling Framework ◽  
Geometric Nonlinearities ◽  
Complementary Strain ◽  
Constraint Model

Abstract Although energy-based methods have advantages over the Newtonian methods for kinetostatic modeling, the geometric nonlinearities inherent in deflections of compliant mechanisms preclude most of the energy-based theorems. Castigliano's first theorem and the Crotti-Engesser theorem, which don't require the problem being solved to be linear, are selected to construct the energy-based kinetostatic modeling framework for compliant mechanisms in this work. Utilization of these two theorems requires explicitly formulating the strain energy in terms of deflections and the complementary strain energy in terms of loads, which are derived based on the beam constraint model. The kinetostatic modeling of two compliant mechanisms are provided to demonstrate the effectiveness of the explicit formulations in this framework derived from Castigliano's first theorem and the Crotti-Engesser theorem.

scholarly journals Functional-Hybrid Modeling through automated adaptive symbolic regression for interpretable mathematical expressions

2021 ◽  
Author(s):  
Harini Narayanan ◽  
M. Nicolas Cruz Bournazou ◽  
Gonzalo Guillen-Gosalbez ◽  
Alessandro Butte
Keyword(s):  
Hybrid Model ◽  
Dynamic Models ◽  
Learning Algorithm ◽  
Symbolic Regression ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Support Vector ◽  
Successful Implementation ◽  
Modeling Framework ◽  
Improved Performance

Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledge or black-box data-driven models based on patterns observed in data. However, in the past two-decade, hybrid modeling that explores the synergy between the two paradigms has emerged as a pragmatic compromise. The data-driven part of these have been largely based on conventional machine learning algorithm (e.g., artificial neural network, support vector regression), which prevents interpretability of the finally learnt model by the domain-experts. In this work we present a novel hybrid modeling framework, the Functional-Hybrid model, that uses the ranked domain-specific functional beliefs together with symbolic regression to develop dynamic models. We demonstrate the successful implementation of these hybrid models for four benchmark systems and a microbial fermentation reactor, all of which are systems of (bio)chemical relevance. We also demonstrate that compared to a similar implementation with the conventional ANN, the performance of Functional-Hybrid model is at least two times better in interpolation and extrapolation. Additionally, the proposed framework can learn the dynamics in 50% lower number of experiments. This improved performance can be attributed to the structure imposed by the functional transformations introduced in the Functional-Hybrid model.

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