De novomutations, protein–protein interactions and functional regulatory networks toward novel diagnostics in autism

2012 ◽  
Vol 9 (5) ◽  
pp. 473-476
Author(s):  
Chee-Seng Ku ◽  
Dimitrios H Roukos
Keyword(s):  
Protein Interactions ◽  
Regulatory Networks ◽  
Protein Protein Interactions

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

2019 ◽  
Vol 19 (6) ◽  
pp. 413-425 ◽  
Author(s):  
Athanasios Alexiou ◽  
Stylianos Chatzichronis ◽  
Asma Perveen ◽  
Abdul Hafeez ◽  
Ghulam Md. Ashraf
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Review Paper ◽  
Boolean Networks ◽  
Diagnostic Tools ◽  
Complex Nature ◽  
Cellular Interactions ◽  
Protein Protein Interactions ◽  
Deterministic Models

Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.Results:GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.Conclusion:In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

In Silico Recognition of Protein-Protein Interaction

2011 ◽  
pp. 248-268
Author(s):  
Byung-Hoon Park ◽  
Phuongan Dam ◽  
Chongle Pan ◽  
Ying Xu ◽  
Al Geist ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Regulatory Networks ◽  
Machine Learning Techniques ◽  
Translation Regulation ◽  
Future Research ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Protein Levels ◽  
Protein Functions ◽  
Model Protein

Protein-protein interactions are fundamental to cellular processes. They are responsible for phenomena like DNA replication, gene transcription, protein translation, regulation of metabolic pathways, immunologic recognition, signal transduction, etc. The identification of interacting proteins is therefore an important prerequisite step in understanding their physiological functions. Due to the invaluable importance to various biophysical activities, reliable computational methods to infer protein-protein interactions from either structural or genome sequences are in heavy demand lately. Successful predictions, for instance, will facilitate a drug design process and the reconstruction of metabolic or regulatory networks. In this chapter, we review: (a) high-throughput experimental methods for identification of protein-protein interactions, (b) existing databases of protein-protein interactions, (c) computational approaches to predicting protein-protein interactions at both residue and protein levels, (d) various statistical and machine learning techniques to model protein-protein interactions, and (e) applications of protein-protein interactions in predicting protein functions. We also discuss intrinsic drawbacks of the existing approaches and future research directions.

scholarly journals Signaling Protein Networks as Targets of New Antineoplastic Drugs

2003 ◽  
Vol 18 (1) ◽  
pp. 57-61 ◽  
Author(s):  
S. Alberti ◽  
S. Parodi
Keyword(s):  
Protein Interactions ◽  
High Throughput Screening ◽  
Regulatory Networks ◽  
Signaling Proteins ◽  
Protein Protein Interactions ◽  
Therapeutic Approaches ◽  
Chemical Libraries ◽  
Drug Candidates ◽  
Microbial Extracts ◽  
Depth Analysis

In-depth analysis of molecular regulatory networks in cancer holds the promise of improved knowledge of the pathophysiology of tumor cells so that it will become possible to design a detailed molecular tumor taxonomy. This knowledge will also offer new opportunities for the identification and validation of key molecular tumor targets to be exploited for novel therapeutic approaches. Some signaling proteins have already been identified as such, e.g. c-Myc, Cyclin D1, Bcl-XL, kinases and some nuclear receptors. This has led to the successful development of a few function-modulatory drugs (Glivec, SERM, Iressa), providing proof-of-principle of the validity of this approach. Further developments are likely to derive from “-omic” approaches, aimed at the understanding of signaling networks and of the mechanism of action of newfound lead molecules. High-throughput screening of small drug-like molecules from combinatorial chemical libraries or from microbial extracts will identify novel, “intelligent” drug candidates. An additional medicinal chemistry strategy (via 40–50 unit rosary-bead chains) has the potential to be much more effective than small molecules in interfering with protein-protein interactions. This may lead to considerably higher selectivity and effectiveness compared with historical approaches in drug discovery.

Phytochrome Signaling Networks

2021 ◽  
Vol 72 (1) ◽  
Author(s):  
Mei-Chun Cheng ◽  
Praveen Kumar Kathare ◽  
Inyup Paik ◽  
Enamul Huq
Keyword(s):  
Protein Interactions ◽  
Translational Regulation ◽  
Regulatory Networks ◽  
Annual Review ◽  
Publication Date ◽  
Protein Protein Interactions ◽  
Conformational Switching ◽  
E3 Ligases ◽  
Almost All

The perception of light signals by the phytochrome family of photoreceptors has a crucial influence on almost all aspects of growth and development throughout a plant's life cycle. The holistic regulatory networks orchestrated by phytochromes, including conformational switching, subcellular localization, direct protein-protein interactions, transcriptional and posttranscriptional regulations, and translational and posttranslational controls to promote photomorphogenesis, are highly coordinated and regulated at multiple levels. During the past decade, advances using innovative approaches have substantially broadened our understanding of the sophisticated mechanisms underlying the phytochrome-mediated light signaling pathways. This review discusses and summarizes these discoveries of the role of the modular structure of phytochromes, phytochrome-interacting proteins, and their functions; the reciprocal modulation of both positive and negative regulators in phytochrome signaling; the regulatory roles of phytochromes in transcriptional activities, alternative splicing, and translational regulation; and the kinases and E3 ligases that modulate PHYTOCHROME INTERACTING FACTORs to optimize photomorphogenesis. Expected final online publication date for the Annual Review of Plant Biology, Volume 72 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Genome-Wide Identification and Analysis of the Polycomb Group Family in Medicago truncatula

2021 ◽  
Vol 22 (14) ◽  
pp. 7537
Author(s):  
Yuanyuan Zhao ◽  
Junchao Zhang ◽  
Zhanmin Sun ◽  
Yixiong Tang ◽  
Yanmin Wu
Keyword(s):  
Medicago Truncatula ◽  
Protein Interactions ◽  
Stress Responses ◽  
Regulatory Networks ◽  
Developmental Stages ◽  
Interaction Network ◽  
Polycomb Group ◽  
Protein Protein Interactions ◽  
Group I ◽  
Gene Structures

Polycomb group (PcG) proteins, which are important epigenetic regulators, play essential roles in the regulatory networks involved in plant growth, development, and environmental stress responses. Currently, as far as we know, no comprehensive and systematic study has been carried out on the PcG family in Medicago truncatula. In the present study, we identified 64 PcG genes with distinct gene structures from the M. truncatula genome. All of the PcG genes were distributed unevenly over eight chromosomes, of which 26 genes underwent gene duplication. The prediction of protein interaction network indicated that 34 M. truncatula PcG proteins exhibited protein–protein interactions, and MtMSI1;4 and MtVRN2 had the largest number of protein–protein interactions. Based on phylogenetic analysis, we divided 375 PcG proteins from 27 species into three groups and nine subgroups. Group I and Group III were composed of five components from the PRC1 complex, and Group II was composed of four components from the PRC2 complex. Additionally, we found that seven PcG proteins in M. truncatula were closely related to the corresponding proteins of Cicer arietinum. Syntenic analysis revealed that PcG proteins had evolved more conservatively in dicots than in monocots. M. truncatula had the most collinearity relationships with Glycine max (36 genes), while collinearity with three monocots was rare (eight genes). The analysis of various types of expression data suggested that PcG genes were involved in the regulation and response process of M. truncatula in multiple developmental stages, in different tissues, and for various environmental stimuli. Meanwhile, many differentially expressed genes (DEGs) were identified in the RNA-seq data, which had potential research value in further studies on gene function verification. These findings provide novel and detailed information on the M. truncatula PcG family, and in the future it would be helpful to carry out related research on the PcG family in other legumes.

scholarly journals Evolution and emergence: higher order information structure in protein interactomes across the tree of life

2021 ◽  
Author(s):  
Brennan Klein ◽  
Erik Hoel ◽  
Anshuman Swain ◽  
Ross Griebenow ◽  
Michael Levin
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Information Structure ◽  
Regulatory Networks ◽  
Biological Systems ◽  
Higher Order ◽  
Protein Protein Interactions ◽  
Black Boxes ◽  
Sensitivity Tests ◽  
Network Simulations

Abstract The internal workings of biological systems are notoriously difficult to understand. Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein–protein interactome networks remain black boxes. One consequence of this black-box nature is that it is unclear at which scale to analyze biological systems to best understand their function. We analyzed the protein interactomes of over 1800 species, containing in total 8 782 166 protein–protein interactions, at different scales. We show the emergence of higher order ‘macroscales’ in these interactomes and that these biological macroscales are associated with lower noise and degeneracy and therefore lower uncertainty. Moreover, the nodes in the interactomes that make up the macroscale are more resilient compared with nodes that do not participate in the macroscale. These effects are more pronounced in interactomes of eukaryota, as compared with prokaryota; these results hold even after sensitivity tests where we recalculate the emergent macroscales under network simulations where we add different edge weights to the interactomes. This points to plausible evolutionary adaptation for macroscales: biological networks evolve informative macroscales to gain benefits of both being uncertain at lower scales to boost their resilience, and also being ‘certain’ at higher scales to increase their effectiveness at information transmission. Our work explains some of the difficulty in understanding the workings of biological networks, since they are often most informative at a hidden higher scale, and demonstrates the tools to make these informative higher scales explicit.

Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarray Gene Expression Measurements

2010 ◽  
pp. 289-309
Author(s):  
Yang Dai ◽  
Eyad Almasri ◽  
Peter Larsen ◽  
Guanrao Chen
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Genetic Regulatory Networks ◽  
Biological Knowledge ◽  
Protein Protein Interactions ◽  
Microarray Gene Expression ◽  
Prior Biological Knowledge ◽  
Comprehensive Survey ◽  
Microarray Gene

The reconstruction of genetic regulatory networks from microarray gene expression measurements has been a challenging problem in bioinformatics. Various methods have been proposed for this problem including the Bayesian Network (BN) approach. In this chapter, we provide a comprehensive survey of the current development of using structure priors derived from high-throughput experimental results such as protein-protein interactions, transcription factor binding location data, evolutionary relationships, and literature database in learning regulatory networks.

scholarly journals Corynebacterium glutamicum regulation beyond transcription: Organizing principles and reconstruction of an extended regulatory network incorporating regulations mediated by small RNA and protein-protein interactions

2021 ◽  
Author(s):  
Juan Miguel Escorcia-Rodríguez ◽  
Andreas Tauch ◽  
Julio Augusto Freyre-González
Keyword(s):  
Corynebacterium Glutamicum ◽  
Protein Interactions ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Models ◽  
Global Scale ◽  
System Level ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Regulatory Structure

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory machinery. The study of such machinery at the global scale has been challenged by the lack of data integration. Here, we report three regulatory network models for C. glutamicum: strong (3040 interactions) constructed solely with regulations previously supported by directed experiments; all evidence (4665 interactions) containing the strong network, regulations previously supported by non-directed experiments, and protein-protein interactions with a direct effect on gene transcription; and sRNA (5222 interactions) containing the all evidence network and sRNA-mediated regulations. Compared to the previous version (2018), the strong and all evidence networks increased by 75 and 1225 interactions, respectively. We analyzed the system-level components of the three networks to identify how they differ and compared their structures against those for the networks of more than 40 species. The inclusion of the sRNAs regulations changed the proportions of the system-level components and increased the number of modules but decreased their size. The C. glutamicum regulatory structure contrasted with other bacterial regulatory networks. Finally, we used the strong networks of three model organisms to provide insights and future directions of the C. glutamicum regulatory network characterization.

scholarly journals Roles of Gremlin 1 and Gremlin 2 in regulating ovarian primordial to primary follicle transition

Reproduction ◽  
2014 ◽  
Vol 147 (6) ◽  
pp. 865-874 ◽  
Author(s):  
Eric E Nilsson ◽  
Ginger Larsen ◽  
Michael K Skinner
Keyword(s):  
Growth Factors ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Primordial Follicle ◽  
Protein Protein Interactions ◽  
Primary Follicle ◽  
Primordial Follicles ◽  
Immunohistochemical Studies ◽  
Gremlin 1

A network of extracellular signaling factors has previously been shown to act in concert to control the ovarian primordial to primary follicle transition. The current study was designed to investigate the roles of the endogenous bone morphogenetic protein (BMP) inhibitors Gremlin 1 (GREM1) and GREM2 in primordial follicle transition in the rat ovary. GREM1 and GREM2 treatments were found to reverse the effects of anti-Müllerian hormone (AMH) to inhibit follicle transition in a whole-ovary culture system. GREM1 reversed the effect of BMP4 to stimulate primordial follicle transition. Immunohistochemical studies showed that GREM2, but not GREM1, was present in primordial follicles suggesting that GREM2 may regulate primordial follicle transition in vivo. Co-immunoprecipitation studies indicated that GREM2 directly binds to AMH, as well as to BMP4. Transcriptome analyses of ovaries treated with GREM2 or GREM1 yielded negligible numbers of differentially expressed genes, suggesting that the immediate effects of GREM2 or GREM1 appear to be at the level of protein–protein interactions, rather than direct actions on the cells. A number of other ovarian growth factors were found to influence the expression of Grem2. Observations suggest that Grem2 is a part of the signaling network of growth factors that regulate the primordial to primary follicle transition. Insights into the regulatory networks affecting the pool of primordial follicles are important to understand the molecular basis for reproductive diseases such as primary ovarian insufficiency.

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