scholarly journals Formalizing metabolic-regulatory networks by hybrid automata

2019 ◽  
Author(s):  
Lin Liu ◽  
Alexander Bockmayr
Keyword(s):  
Transcriptional Regulation ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Discrete Control ◽  
Hybrid Automata ◽  
Hybrid Automaton ◽  
Metabolic System ◽  
Computational Approaches ◽  
Cellular Processes ◽  
Dynamic Interplay

AbstractComputational approaches in systems biology have become a powerful tool for understanding the fundamental mechanisms of cellular metabolism and regulation. However, the interplay between the regulatory and the metabolic system is still poorly understood. In particular, there is a need for formal mathematical frameworks that allow analyzing metabolism together with dynamic enzyme resources and regulatory events. Here, we introduce a metabolic-regulatory network model (MRN) that allows integrating metabolism with transcriptional regulation, macromolecule production and enzyme resources. Using this model, we show that the dynamic interplay between these different cellular processes can be formalized by a hybrid automaton, combining continuous dynamics and discrete control.

scholarly journals miRDRN – miRNA Disease Regulatory Network: A tool for exploring disease and tissue-specific microRNA regulatory networks

2019 ◽  
Author(s):  
Hsueh-Chuan Liu ◽  
Yi-Shian Peng ◽  
Hoong-Chien Lee
Keyword(s):  
Web Service ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Gene Annotation ◽  
Service Platform ◽  
Tissue Specific ◽  
Cellular Processes ◽  
Specific Subset ◽  
Pathway Gene

Background. MiRNA regulates cellular processes through acting on specific target genes. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. Cellular processes, including those related to disease, proceed through multiple interactions, are often organized into pathways among genes and gene products. Large databases on protein-protein interactions (PPIs) are available. Here, we have integrated the information mentioned above to build a web service platform, miRNA Disease Regulatory Network, or miRDRN, for users to construct disease and tissue-specific miRNA-protein regulatory networks. Methods. Data on human protein interaction, disease-associated miRNA, tumor-associated gene, miRNA targeted gene, molecular interaction and reaction network or pathway, gene ontology, gene annotation and gene product information, and gene expression were collected from publicly available databases and integrated. A complete set of regulatory sub-pathways (RSPs) having the form (M, T, G1, G2) were built from the integrated data and stored in the database part of miRDRN, where M is a disease-associated miRNA, T is its regulatory target gene, G1 (G2) is a gene/protein interacting with T (G1). Each sequence (T, G1, G2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways. Results. A web service platform, miRDRN ( http://mirdrn.ncu.edu.tw/mirdrn/), was built to allow users to retrieve a disease and tissue-specific subset of RSPs, from which a miRNA regulatory network is constructed. miRDRN is a database that currently contains 6,973,875 p-valued sub-pathways associated with 119 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes, and a web tool that facilitates the construction and visualization of disease and tissue-specific miRNA-protein regulatory networks, for exploring single diseases, or for exploring the comorbidity of disease-pairs. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer's disease-Type 2 diabetes (AD-T2D), in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

scholarly journals Absolute quantification of transcription factors reveals principles of gene regulation in erythropoiesis

2019 ◽  
Author(s):  
Mark A. Gillespie ◽  
Carmen G. Palii ◽  
Daniel Sanchez-Taltavull ◽  
Paul Shannon ◽  
William J.R. Longabaugh ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Transcriptional Regulation ◽  
Transcription Factors ◽  
Cell Fate ◽  
Regulatory Network ◽  
Copy Number ◽  
Absolute Quantification ◽  
Cellular Processes ◽  
Gene Regulatory ◽  
Surprising Discovery

SummaryDynamic cellular processes such as differentiation are driven by changes in the abundances of transcription factors (TFs). Yet, despite years of studies we still do not know the protein copy number of TFs in the nucleus. Here, by determining the absolute abundances of 103 TFs and co-factors during the course of human erythropoiesis, we provide a dynamic and quantitative scale for TFs in the nucleus. Furthermore, we establish the first Gene Regulatory Network of cell fate commitment that integrates temporal protein stoichiometry data with mRNA measurements. The model revealed quantitative imbalances in TFs cross-antagonistic relationships that underlie lineage determination. Finally, we made the surprising discovery that in the nucleus, corepressors are dramatically more abundant than coactivators at the protein, but not at the RNA level, with profound implications for understanding transcriptional regulation. These analyses provide a unique quantitative framework to understand transcriptional regulation of cell differentiation in a dynamic context.

scholarly journals miRDRN—miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7309
Author(s):  
Hsueh-Chuan Liu ◽  
Yi-Shian Peng ◽  
Hoong-Chien Lee
Keyword(s):  
Web Service ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Mirna Target ◽  
Target Genes ◽  
Gene Annotation ◽  
Biological Pathways ◽  
Tissue Specific ◽  
Cellular Processes ◽  
Disease Pair

Background MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein–protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action. Methods Data on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G1, G2) was built from the integrated data and stored, where T is a miRNA-associated target gene, G1 (G2) is a gene/protein interacting with T (G1). Each sequence (T, G1, G2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways. Results A web service platform, miRDRN (http://mirdrn.ncu.edu.tw/mirdrn/), was built. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer’s disease-Type 2 diabetes, in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

scholarly journals miRDRN – miRNA Disease Regulatory Network: A tool for exploring disease and tissue-specific microRNA regulatory networks

2019 ◽  
Author(s):  
Hsueh-Chuan Liu ◽  
Yi-Shian Peng ◽  
Hoong-Chien Lee
Keyword(s):  
Web Service ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Gene Annotation ◽  
Service Platform ◽  
Tissue Specific ◽  
Cellular Processes ◽  
Specific Subset ◽  
Pathway Gene

Background. MiRNA regulates cellular processes through acting on specific target genes. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. Cellular processes, including those related to disease, proceed through multiple interactions, are often organized into pathways among genes and gene products. Large databases on protein-protein interactions (PPIs) are available. Here, we have integrated the information mentioned above to build a web service platform, miRNA Disease Regulatory Network, or miRDRN, for users to construct disease and tissue-specific miRNA-protein regulatory networks. Methods. Data on human protein interaction, disease-associated miRNA, tumor-associated gene, miRNA targeted gene, molecular interaction and reaction network or pathway, gene ontology, gene annotation and gene product information, and gene expression were collected from publicly available databases and integrated. A complete set of regulatory sub-pathways (RSPs) having the form (M, T, G1, G2) were built from the integrated data and stored in the database part of miRDRN, where M is a disease-associated miRNA, T is its regulatory target gene, G1 (G2) is a gene/protein interacting with T (G1). Each sequence (T, G1, G2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways. Results. A web service platform, miRDRN ( http://mirdrn.ncu.edu.tw/mirdrn/), was built to allow users to retrieve a disease and tissue-specific subset of RSPs, from which a miRNA regulatory network is constructed. miRDRN is a database that currently contains 6,973,875 p-valued sub-pathways associated with 119 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes, and a web tool that facilitates the construction and visualization of disease and tissue-specific miRNA-protein regulatory networks, for exploring single diseases, or for exploring the comorbidity of disease-pairs. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer's disease-Type 2 diabetes (AD-T2D), in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

scholarly journals Stochastic activation and bistability in a Rab GTPase regulatory network

2020 ◽  
Vol 117 (12) ◽  
pp. 6540-6549
Author(s):  
Urban Bezeljak ◽  
Hrushikesh Loya ◽  
Beata Kaczmarek ◽  
Timothy E. Saunders ◽  
Martin Loose
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Membrane Surface ◽  
Activity Patterns ◽  
Small Gtpases ◽  
Signaling Networks ◽  
Rab Gtpase ◽  
Endomembrane System ◽  
In Vitro Reconstitution

The eukaryotic endomembrane system is controlled by small GTPases of the Rab family, which are activated at defined times and locations in a switch-like manner. While this switch is well understood for an individual protein, how regulatory networks produce intracellular activity patterns is currently not known. Here, we combine in vitro reconstitution experiments with computational modeling to study a minimal Rab5 activation network. We find that the molecular interactions in this system give rise to a positive feedback and bistable collective switching of Rab5. Furthermore, we find that switching near the critical point is intrinsically stochastic and provide evidence that controlling the inactive population of Rab5 on the membrane can shape the network response. Notably, we demonstrate that collective switching can spread on the membrane surface as a traveling wave of Rab5 activation. Together, our findings reveal how biochemical signaling networks control vesicle trafficking pathways and how their nonequilibrium properties define the spatiotemporal organization of the cell.

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.

scholarly journals Relationships between medical students’ co-regulatory network characteristics and self-regulated learning: a social network study

2021 ◽  
Author(s):  
Derk Bransen ◽  
Marjan J. B. Govaerts ◽  
Dominique M. A. Sluijsmans ◽  
Jeroen Donkers ◽  
Piet G. C. Van den Bossche ◽  
...  
Keyword(s):  
Social Network ◽  
Medical Students ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Learning Opportunities ◽  
Network Size ◽  
Tie Strength ◽  
Network Characteristics ◽  
Self Regulated Learning ◽  
Regulated Learning

Abstract Introduction Recent conceptualizations of self-regulated learning acknowledge the importance of co-regulation, i.e., students’ interactions with others in their networks to support self-regulation. Using a social network approach, the aim of this study is to explore relationships between characteristics of medical students’ co-regulatory networks, perceived learning opportunities, and self-regulated learning. Methods The authors surveyed 403 undergraduate medical students during their clinical clerkships (response rate 65.5%). Using multiple regression analysis, structural equation modelling techniques, and analysis of variance, the authors explored relationships between co-regulatory network characteristics (network size, network diversity, and interaction frequency), students’ perceptions of learning opportunities in the workplace setting, and self-reported self-regulated learning. Results Across all clerkships, data showed positive relationships between tie strength and self-regulated learning (β = 0.095, p < 0.05) and between network size and tie strength (β = 0.530, p < 0.001), and a negative relationship between network diversity and tie strength (β = −0.474, p < 0.001). Students’ perceptions of learning opportunities showed positive relationships with both self-regulated learning (β = 0.295, p < 0.001) and co-regulatory network size (β = 0.134, p < 0.01). Characteristics of clerkship contexts influenced both co-regulatory network characteristics (size and tie strength) and relationships between network characteristics, self-regulated learning, and students’ perceptions of learning opportunities. Discussion The present study reinforces the importance of co-regulatory networks for medical students’ self-regulated learning during clinical clerkships. Findings imply that supporting development of strong networks aimed at frequent co-regulatory interactions may enhance medical students’ self-regulated learning in challenging clinical learning environments. Social network approaches offer promising ways of further understanding and conceptualising self- and co-regulated learning in clinical workplaces.

scholarly journals The Role of Autophagy and lncRNAs in the Maintenance of Cancer Stem Cells

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1239
Author(s):  
Leila Jahangiri ◽  
Tala Ishola ◽  
Perla Pucci ◽  
Ricky M. Trigg ◽  
Joao Pereira ◽  
...  
Keyword(s):  
Stem Cells ◽  
Cancer Stem Cells ◽  
Cancer Progression ◽  
Regulatory Networks ◽  
Epithelial To Mesenchymal Transition ◽  
Tumour Microenvironment ◽  
Repair Capacity ◽  
Mesenchymal Transition ◽  
Cellular Processes

Cancer stem cells (CSCs) possess properties such as self-renewal, resistance to apoptotic cues, quiescence, and DNA-damage repair capacity. Moreover, CSCs strongly influence the tumour microenvironment (TME) and may account for cancer progression, recurrence, and relapse. CSCs represent a distinct subpopulation in tumours and the detection, characterisation, and understanding of the regulatory landscape and cellular processes that govern their maintenance may pave the way to improving prognosis, selective targeted therapy, and therapy outcomes. In this review, we have discussed the characteristics of CSCs identified in various cancer types and the role of autophagy and long noncoding RNAs (lncRNAs) in maintaining the homeostasis of CSCs. Further, we have discussed methods to detect CSCs and strategies for treatment and relapse, taking into account the requirement to inhibit CSC growth and survival within the complex backdrop of cellular processes, microenvironmental interactions, and regulatory networks associated with cancer. Finally, we critique the computationally reinforced triangle of factors inclusive of CSC properties, the process of autophagy, and lncRNA and their associated networks with respect to hypoxia, epithelial-to-mesenchymal transition (EMT), and signalling pathways.

scholarly journals TFEB Signalling-Related MicroRNAs and Autophagy

Biomolecules ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 985
Author(s):  
Davide Corà ◽  
Federico Bussolino ◽  
Gabriella Doronzo
Keyword(s):  
Transcriptional Regulation ◽  
Stress Factors ◽  
Cellular Functions ◽  
Post Translational Modifications ◽  
Cellular Processes ◽  
Factor Deficiency ◽  
Transcription Factor Eb ◽  
Important Regulator ◽  
Response To Stress ◽  
Post Transcriptional Regulation

The oncogenic Transcription Factor EB (TFEB), a member of MITF-TFE family, is known to be the most important regulator of the transcription of genes responsible for the control of lysosomal biogenesis and functions, autophagy, and vesicles flux. TFEB activation occurs in response to stress factors such as nutrient and growth factor deficiency, hypoxia, lysosomal stress, and mitochondrial damage. To reach the final functional status, TFEB is regulated in multimodal ways, including transcriptional rate, post-transcriptional regulation, and post-translational modifications. Post-transcriptional regulation is in part mediated by miRNAs. miRNAs have been linked to many cellular processes involved both in physiology and pathology, such as cell migration, proliferation, differentiation, and apoptosis. miRNAs also play a significant role in autophagy, which exerts a crucial role in cell behaviour during stress or survival responses. In particular, several miRNAs directly recognise TFEB transcript or indirectly regulate its function by targeting accessory molecules or enzymes involved in its post-translational modifications. Moreover, the transcriptional programs triggered by TFEB may be influenced by the miRNA-mediated regulation of TFEB targets. Finally, recent important studies indicate that the transcription of many miRNAs is regulated by TFEB itself. In this review, we describe the interplay between miRNAs with TFEB and focus on how these types of crosstalk affect TFEB activation and cellular functions.

scholarly journals The HrpG/HrpX Regulon of Xanthomonads—An Insight to the Complexity of Regulation of Virulence Traits in Phytopathogenic Bacteria

2021 ◽  
Vol 9 (1) ◽  
pp. 187
Author(s):  
Doron Teper ◽  
Sheo Shankar Pandey ◽  
Nian Wang
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Focal Point ◽  
Genetic Regulation ◽  
Transcriptional Regulators ◽  
Secretion System ◽  
In Planta ◽  
Type 3 Secretion System ◽  
Type 3 ◽  
Made In

Bacteria of the genus Xanthomonas cause a wide variety of economically important diseases in most crops. The virulence of the majority of Xanthomonas spp. is dependent on secretion and translocation of effectors by the type 3 secretion system (T3SS) that is controlled by two master transcriptional regulators HrpG and HrpX. Since their discovery in the 1990s, the two regulators were the focal point of many studies aiming to decipher the regulatory network that controls pathogenicity in Xanthomonas bacteria. HrpG controls the expression of HrpX, which subsequently controls the expression of T3SS apparatus genes and effectors. The HrpG/HrpX regulon is activated in planta and subjected to tight metabolic and genetic regulation. In this review, we cover the advances made in understanding the regulatory networks that control and are controlled by the HrpG/HrpX regulon and their conservation between different Xanthomonas spp.

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