scholarly journals Evidence of Highly Regulated Genes (in-Hubs) in Gene Networks of Saccharomyces Cerevisiae

2008 ◽  
Vol 2 ◽  
pp. BBI.S853 ◽  
Author(s):  
Jesper Lundström ◽  
Johan Björkegren ◽  
Jesper Tegnér
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Repeated Measurements ◽  
Experimental Conditions ◽  
Cellular Processes ◽  
Binding Data ◽  
External Stimuli

Uncovering interactions between genes, gene networks, is important to increase our understanding of intrinsic cellular processes and responses to external stimuli such as drugs. Gene networks can be computationally inferred from repeated measurements of gene expression, using algorithms, which assume that each gene is controlled by only a small number of other proteins. Here, by extending the transcription network with cofactors (defined from protein-protein binding data) as active regulators, we identified the effective gene network, providing evidence of in-hubs in the gene regulatory networks of yeast. Then, using the notion that in-hub genes will be differentially expressed over several experimental conditions, we designed an algorithm, the HubDetector, enabling identification of in-hubs directly from gene expression data. Applying the HubDetector to 488 genome-wide expression profiles from two independent datasets, we identified putative in-hubs overlapping significantly with in-hubs in the effective gene network.

EFFECTIVE MODELS FOR GENE NETWORKS AND THEIR APPLICATIONS

2012 ◽  
Vol 07 (01n02) ◽  
pp. 41-70 ◽  
Author(s):  
JASON SHULMAN ◽  
LARS SEEMANN ◽  
GREGG W. ROMAN ◽  
GEMUNU H. GUNARATNE
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Stationary Solutions ◽  
The State ◽  
Effective Model ◽  
Knockout Mutant ◽  
Double Knockout

Networks are used to abstract large, highly-coupled sets of objects. Their analyses have included network classification into a few broad classes and selection of small substructures that perform simple yet common tasks. One issue that has received little attention is how the state of a network can be moved according to a pre-specified set of guidelines. In this paper, we address this question in the context of gene networks. In general, neither the full membership of the gene network associated with a biological process nor the precise form of interactions between nodes is known. What is available, through microarrays or sequencing, are gene expression profiles of an organism or its viable mutants. Our approach relies only on these expression profiles, and not on the availability of an accurate model for the network. The first step is to select a small set of core- or master- nodes, such as transcription factors or microRNAs, that can be used to alter the levels of many of the remaining genes in the network. We ask how the state — or solution — of the gene network changes as the levels of these master nodes are altered externally. The object of our study is, not the network, but the surface of these solutions. We argue that it can be approximated using gene expression profiles of the organism and single manipulation of master node activity. This is done through an "effective model." The effective model as well as error estimates for its predictions can be derived from experimental data. The method is validated using synthetic gene networks that have stationary solutions and those that are periodically driven, e.g., circadian networks. An effective model for the oxygen-deprivation network of E.coli is constructed using previously published gene expression profiles, and used to predict the expression levels in a double knockout mutant. Less that 30% of the predictions lie outside the 5% confidence level. We propose the use of the effective model methodology to compute how Drosophila melanogaster in the normal state can be genetically altered into a pre-defined sleep deprived-like state.

scholarly journals EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction

2021 ◽  
Author(s):  
Hakimeh Khojasteh ◽  
Mohammad Hossein Olyaee ◽  
Alireza Khanteymoori
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Expression Data ◽  
Class Imbalance Problem ◽  
Experimental Conditions ◽  
Topological Features ◽  
Gene Regulatory

The development of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many machine learning methods have been developed, including supervised, unsupervised, and semi-supervised to infer gene regulatory networks. Most of these methods ignore the class imbalance problem which can lead to decreasing the accuracy of predicting regulatory interactions in the network. Therefore, developing an effective method considering imbalanced data is a challenging task. In this paper, we propose EnGRNT approach to infer GRNs with high accuracy that uses ensemble-based methods. The proposed approach, as well as the gene expression data, considers the topological features of GRN. We applied our approach to the simulated Escherichia coli dataset. Experimental results demonstrate that the appropriateness of the inference method relies on the size and type of expression profiles in microarray data. Except for multifactorial experimental conditions, the proposed approach outperforms unsupervised methods. The obtained results recommend the application of EnGRNT on the imbalanced datasets.

scholarly journals Gene Network Exploration of Crosstalk between Apoptosis and Autophagy in Chronic Myelogenous Leukemia

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Fengfeng Wang ◽  
William C. S. Cho ◽  
Lawrence W. C. Chan ◽  
S. C. Cesar Wong ◽  
Nancy B. Y. Tsui ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Expression Profiles ◽  
Microarray Dataset ◽  
Underlying Mechanism ◽  
Gene Expression Omnibus ◽  
Myelogenous Leukemia ◽  
Underlying Mechanisms ◽  
Graphical Illustration

Background. Gene expression levels change to adapt the stress, such as starvation, toxin, and radiation. The changes are signals transmitted through molecular interactions, eventually leading to two cellular fates, apoptosis and autophagy. Due to genetic variations, the signals may not be effectively transmitted to modulate apoptotic and autophagic responses. Such aberrant modulation may lead to carcinogenesis and drug resistance. The balance between apoptosis and autophagy becomes very crucial in coping with the stress. Though there have been evidences illustrating the apoptosis-autophagy interplay, the underlying mechanism and the participation of the regulators including transcription factors (TFs) and microRNAs (miRNAs) remain unclear.Results. Gene network is a graphical illustration for exploring the functional linkages and the potential coordinate regulations of genes. Microarray dataset for the study of chronic myeloid leukemia was obtained from Gene Expression Omnibus. The expression profiles of those genes related to apoptosis and autophagy, including MCL1, BCL2, ATG, beclin-1, BAX, BAK, E2F, cMYC, PI3K, AKT, BAD, and LC3, were extracted from the dataset to construct the gene networks.Conclusion. The network analysis of these genes explored the underlying mechanisms and the roles of TFs and miRNAs for the crosstalk between apoptosis and autophagy.

scholarly journals Switching On Static Gene Regulatory Networks to Compute Cellular Decisions

2020 ◽  
Author(s):  
Clara E. Pavillet ◽  
Dimitrios Voukantsis ◽  
Francesca M. Buffa
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Computational Models ◽  
Cell Behaviour ◽  
Cellular Processes ◽  
Health And Disease ◽  
Gene Regulatory ◽  
Cellular Behaviour

AbstractMotivationGene networks are complex sets of regulators and interactions that govern cellular processes. Their perturbations can disrupt regular biological functions, translating into a change in cell behaviour and ability to respond to internal and external cues. Computational models of these networks can boost translation of our scientific knowledge into medical applications by predicting how cells will behave in health and disease, or respond to stimuli such as a drug treatment. The development of such models requires effective ways to read, manipulate and analyse the increasing amount of existing, and newly deposited gene network data.ResultsWe developed BioSWITCH, a command-line program using the BioPAX standardised language to “switch on” static regulatory networks so that they can be executed in GINML to predict cellular behaviour. Using a previously published haematopoiesis gene network, we show that BioSWITCH successfully and faithfully automates the network de-coding and re-coding into an executable logical network. BioSWITCH also supports the integration of a BioPAX model into an existing GINML graph.AvailabilitySource code available at https://github.com/CBigOxf/[email protected]; [email protected]

scholarly journals MCO: towards an ontology and unified vocabulary for a framework-based annotation of microbial growth conditions

2017 ◽  
Author(s):  
VH Tierrafría ◽  
C Mejía-Almonte ◽  
JM Camacho-Zaragoza ◽  
H Salgado ◽  
K Alquicira ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Phenotypic Expression ◽  
Biomedical Ontology ◽  
Growth Conditions ◽  
Controlled Vocabulary ◽  
Experimental Conditions ◽  
E Coli ◽  
K 12

AbstractMotivationA major component in our understanding of the biology of an organism is the mapping of its genotypic potential into the repertoire of its phenotypic expression profiles. This genotypic to phenotypic mapping is executed by the machinery of gene regulation that turns genes on and off, which in microorganisms is essentially studied by changes in growth conditions and genetic modifications. Although many efforts have been made to systematize the annotation of experimental conditions in microbiology, the available annotation is not based on a consistent and controlled vocabulary for the unambiguous description of growth conditions, making difficult the identification of biologically meaningful comparisons of knowledge generated in different experiments or laboratories, a task urgently needed given the massive amounts of data generated by high throughput (HT) technologies.ResultsWe curated terms related to experimental conditions that affect gene expression inE. coliK-12. Since this is the best studied microorganism, the collected terms are the seed for the first version of the Microbial Conditions Ontology (MCO), a controlled and structured vocabulary that can be expanded to annotate microbial conditions in general. Moreover, we developed an annotation framework using the MCO terms to describe experimental conditions, providing the foundation to identify regulatory networks that operate under a particular condition. MCO supports comparisons of HT-derived data from different repositories. In this sense, we started to map common RegulonDB terms and Colombos bacterial expression compendia terms to MCO.Availability and ImplementationAs far as we know, MCO is the first ontology for growth conditions of any bacterial organism and it is available athttp://regulondb.ccg.unam.mx/. Furthermore, we will disseminate MCO throughout the Open Biomedical Ontology (OBO) Foundry in order to set a standard for the annotation of gene expression data derived from conventional as well as HT experiments inE. coliand other microbial organisms. This will enable the comparison of data from diverse data [email protected],[email protected]

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks

2017 ◽  
Vol 15 (02) ◽  
pp. 1650045 ◽  
Author(s):  
Olga V. Petrovskaya ◽  
Evgeny D. Petrovskiy ◽  
Inna N. Lavrik ◽  
Vladimir A. Ivanisenko
Keyword(s):  
Mathematical Modeling ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Network Modeling ◽  
Computer Experiments ◽  
Linear Control ◽  
Expression Of Genes ◽  
Gene Regulatory

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

scholarly journals Analysis of blood-based gene expression in idiopathic Parkinson disease

Neurology ◽  
2017 ◽  
Vol 89 (16) ◽  
pp. 1676-1683 ◽  
Author(s):  
Ron Shamir ◽  
Christine Klein ◽  
David Amar ◽  
Eva-Juliane Vollstedt ◽  
Michael Bonin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson Disease ◽  
Gene Networks ◽  
Large Scale ◽  
Expression Profiles ◽  
Area Under The Curve ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
Gene Profiles ◽  
Independent Test

Objective:To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).Methods:Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.Results:A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E–6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E–4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.Conclusions:We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.

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