scholarly journals Intercellular Signaling Network Underlies Biological Time Across Multiple Temporal Scales

2018 ◽  
Author(s):  
Joshua Millstein ◽  
Keith C. Summa ◽  
Xia Yang ◽  
Jun Zhu ◽  
Huaiyu Mi ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Cellular Response ◽  
Signaling Network ◽  
Supplementary Information ◽  
Multiple Time ◽  
Molecular Signaling ◽  
Creb Binding Protein ◽  
Intercellular Signaling ◽  
Temporal Scales ◽  
Biological Time

AbstractMotivationCellular, physiological and molecular processes must be organized and regulated across multiple time domains throughout the lifespan of an organism. The technological revolution in molecular biology has led to the identification of numerous genes implicated in the regulation of diverse temporal biological processes. However, it is natural to question whether there is an underlying regulatory network governing multiple timescales simultaneously.ResultsUsing queries of relevant databases and literature searches, a single dense multiscale temporal regulatory network was identified involving core sets of genes that regulate circadian, cell cycle, and aging processes. The network was highly enriched for genes involved in signal transduction (P = 1.82e-82), with p53 and its regulators such as p300 and CREB binding protein forming key hubs, but also for genes involved in metabolism (P = 6.07e-127) and cellular response to stress (P = 1.56e-93). These results suggest an intertwined molecular signaling network that affects biological time across multiple temporal scales in response to environmental stimuli and available [email protected] informationSupplementary data are available online.

scholarly journals Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tien-Dzung Tran ◽  
Duc-Tinh Pham
Keyword(s):  
Anticancer Drug ◽  
Drug Target ◽  
Target Genes ◽  
Signaling Network ◽  
Competitive Dynamics ◽  
Signaling Networks ◽  
Molecular Signaling ◽  
Dynamics Model ◽  
Cancer Signaling ◽  
The Impact

AbstractEach cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.

SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties

2019 ◽  
Vol 35 (20) ◽  
pp. 4140-4146 ◽  
Author(s):  
Ghazaleh Taherzadeh ◽  
Abdollah Dehzangi ◽  
Maryam Golchin ◽  
Yaoqi Zhou ◽  
Matthew P Campbell
Keyword(s):  
Protein Glycosylation ◽  
Supplementary Information ◽  
Support Vector ◽  
Intercellular Signaling ◽  
Post Translational Modifications ◽  
Novel Structure ◽  
Glycosylation Sites ◽  
Improved Performance ◽  
Human And Mouse ◽  
Fold Cross Validation

Abstract Motivation Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation and host-pathogen interactions. However, due to the poor ionization efficiency and microheterogeneity of glycopeptides identifying glycosylation sites is a challenging task, and there is a demand for computational methods. Here, we constructed the largest dataset of human and mouse glycosylation sites to train deep learning neural networks and support vector machine classifiers to predict N-/O-linked glycosylation sites, respectively. Results The method, called SPRINT-Gly, achieved consistent results between ten-fold cross validation and independent test for predicting human and mouse glycosylation sites. For N-glycosylation, a mouse-trained model performs equally well in human glycoproteins and vice versa, however, due to significant differences in O-linked sites separate models were generated. Overall, SPRINT-Gly is 18% and 50% higher in Matthews correlation coefficient than the next best method compared in N-linked and O-linked sites, respectively. This improved performance is due to the inclusion of novel structure and sequence-based features. Availability and implementation http://sparks-lab.org/server/SPRINT-Gly/ Supplementary information Supplementary data are available at Bioinformatics online.

Cancerous perturbations within the ERK, PI3K/Akt, and Wnt/β-catenin signaling network constitutively activate inter-pathway positive feedback loops

2017 ◽  
Vol 13 (5) ◽  
pp. 830-840 ◽  
Author(s):  
Rahul Rao Padala ◽  
Rishabh Karnawat ◽  
Satish Bharathwaj Viswanathan ◽  
Abhishek Vijay Thakkar ◽  
Asim Bikas Das
Keyword(s):  
Signaling Pathways ◽  
Malignant Transformation ◽  
Positive Feedback ◽  
Feedback Loops ◽  
Signaling Network ◽  
Molecular Signaling ◽  
Activated State ◽  
Molecular Signaling Pathways

Perturbations in molecular signaling pathways result in a constitutively activated state, leading to malignant transformation of cells.

scholarly journals Joint eQTL mapping and Inference of Gene Regulatory Network Improves Power of Detecting both cis- and trans-eQTLs

2021 ◽  
Author(s):  
Xin Zhou ◽  
Xiaodong Cai
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Multiple Testing ◽  
Critical Role ◽  
Supplementary Information ◽  
Effective Sample Size ◽  
Eqtl Mapping ◽  
Gene Regulatory ◽  
Cis And Trans

Abstract Motivation Genetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: 1) relatively small size of samples available, and 2) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when one tries to identify trans-eQTLs that are far away from the genes they influence. If one can exploit co-expressed genes jointly in eQTL-mapping, effective sample size can be increased. Furthermore, using the structure of the gene regulatory network (GRN) may help to identify trans-eQTLs without increasing multiple testing burden. Results In this paper, we employ the structure equation model (SEM) to model both GRN and effect of eQTLs on gene expression, and then develop a novel algorithm, named sparse SEM for eQTL mapping (SSEMQ), to conduct joint eQTL mapping and GRN inference. The SEM can exploit co-expressed genes jointly in eQTL mapping and also use GRN to determine trans-eQTLs. Computer simulations demonstrate that our SSEMQ significantly outperforms nine existing eQTL mapping methods. SSEMQ is further employed to analyze two real datasets of human breast and whole blood tissues, yielding a number of cis- and trans-eQTLs. Availability R package ssemQr is available at https://github.com/Ivis4ml/ssemQr.git. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals TopicNet: a framework for measuring transcriptional regulatory network change

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i474-i481
Author(s):  
Shaoke Lou ◽  
Tianxiao Li ◽  
Xiangmeng Kong ◽  
Jing Zhang ◽  
Jason Liu ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Transcriptional Regulatory Network ◽  
Network Connectivity ◽  
Supplementary Information ◽  
Differential Survival ◽  
Network Change ◽  
Low Dimensional

Abstract Motivation Recently, many chromatin immunoprecipitation sequencing experiments have been carried out for a diverse group of transcription factors (TFs) in many different types of human cells. These experiments manifest large-scale and dynamic changes in regulatory network connectivity (i.e. network ‘rewiring’), highlighting the different regulatory programs operating in disparate cellular states. However, due to the dense and noisy nature of current regulatory networks, directly comparing the gains and losses of targets of key TFs across cell states is often not informative. Thus, here, we seek an abstracted, low-dimensional representation to understand the main features of network change. Results We propose a method called TopicNet that applies latent Dirichlet allocation to extract functional topics for a collection of genes regulated by a given TF. We then define a rewiring score to quantify regulatory-network changes in terms of the topic changes for this TF. Using this framework, we can pinpoint particular TFs that change greatly in network connectivity between different cellular states (such as observed in oncogenesis). Also, incorporating gene expression data, we define a topic activity score that measures the degree to which a given topic is active in a particular cellular state. And we show how activity differences can indicate differential survival in various cancers. Availability and Implementation The TopicNet framework and related analysis were implemented using R and all codes are available at https://github.com/gersteinlab/topicnet. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Estrogen receptors promote NSCLC progression by modulating the membrane receptor signaling network: a systems biology perspective

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Xiujuan Gao ◽  
Yue Cai ◽  
Zhuo Wang ◽  
Wenjuan He ◽  
Sisi Cao ◽  
...  
Keyword(s):  
Systems Biology ◽  
Estrogen Receptors ◽  
Molecular Mechanisms ◽  
Receptor Activation ◽  
Signaling Network ◽  
Membrane Receptor ◽  
Receptor Signaling ◽  
Molecular Signaling ◽  
Cell Behaviors ◽  
Cancer Systems Biology

Abstract Background Estrogen receptors (ERs) are thought to play an important role in non-small cell lung cancer (NSCLC). However, the effect of ERs in NSCLC is still controversial and needs further investigation. A new consideration is that ERs may affect NSCLC progression through complicated molecular signaling networks rather than individual targets. Therefore, this study aims to explore the effect of ERs in NSCLC from the perspective of cancer systems biology. Methods The gene expression profile of NSCLC samples in TCGA dataset was analyzed by bioinformatics method. Variations of cell behaviors and protein expression were detected in vitro. The kinetic process of molecular signaling network was illustrated by a systemic computational model. At last, immunohistochemical (IHC) and survival analysis was applied to evaluate the clinical relevance and prognostic effect of key receptors in NSCLC. Results Bioinformatics analysis revealed that ERs might affect many cancer-related molecular events and pathways in NSCLC, particularly membrane receptor activation and signal transduction, which might ultimately lead to changes in cell behaviors. Experimental results confirmed that ERs could regulate cell behaviors including cell proliferation, apoptosis, invasion and migration; ERs also regulated the expression or activation of key members in membrane receptor signaling pathways such as epidermal growth factor receptor (EGFR), Notch1 and Glycogen synthase kinase-3β/β-Catenin (GSK3β/β-Catenin) pathways. Modeling results illustrated that the promotive effect of ERs in NSCLC was implemented by modulating the signaling network composed of EGFR, Notch1 and GSK3β/β-Catenin pathways; ERs maintained and enhanced the output of oncogenic signals by adding redundant and positive-feedback paths into the network. IHC results echoed that high expression of ERs, EGFR and Notch1 had a synergistic effect on poor prognosis of advanced NSCLC. Conclusions This study indicated that ERs were likely to promote NSCLC progression by modulating the integrated membrane receptor signaling network composed of EGFR, Notch1 and GSK3β/β-Catenin pathways and then affecting tumor cell behaviors. It also complemented the molecular mechanisms underlying the progression of NSCLC and provided new opportunities for optimizing therapeutic scheme of NSCLC.

Impacts of Multiple Time Delays on a Gene Regulatory Network Mediated by Small Noncoding RNA

2020 ◽  
Vol 30 (05) ◽  
pp. 2050069
Author(s):  
Ming Liu ◽  
Fanwei Meng ◽  
Dongpo Hu
Keyword(s):  
Hopf Bifurcation ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Delays ◽  
Noncoding Rna ◽  
Multiple Time ◽  
Normal Form Theory ◽  
Small Noncoding Rna ◽  
Multiple Time Delays ◽  
Gene Regulatory

In this paper, the impacts of multiple time delays on a gene regulatory network mediated by small noncoding RNA is studied. By analyzing the associated characteristic equation of the corresponding linearized system, the asymptotic stability of the positive equilibrium is investigated and Hopf bifurcation is demonstrated. Furthermore, the explicit formulae for determining the direction of the Hopf bifurcation and the stability of the bifurcating periodic solutions are given by the center manifold theorem and the normal form theory for functional differential equations. Finally, some numerical simulations are demonstrated for supporting the theoretical results.

scholarly journals Skeletal Muscle Atrophy: Discovery of Mechanisms and Potential Therapies

Physiology ◽  
2019 ◽  
Vol 34 (4) ◽  
pp. 232-239 ◽  
Author(s):  
Scott M. Ebert ◽  
Asma Al-Zougbi ◽  
Sue C. Bodine ◽  
Christopher M. Adams
Keyword(s):  
Skeletal Muscle ◽  
Muscle Atrophy ◽  
Molecular Mechanisms ◽  
Signaling Network ◽  
Skeletal Muscle Atrophy ◽  
Molecular Signaling ◽  
Molecular Signaling Network

Skeletal muscle atrophy proceeds through a complex molecular signaling network that is just beginning to be understood. Here, we discuss examples of recently identified molecular mechanisms of muscle atrophy and how they highlight an immense need and opportunity for focused biochemical investigations and further unbiased discovery work.

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