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.

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.

scholarly journals Evolution leads to emergence: An analysis of protein interactomes across the tree of life

2020 ◽  
Author(s):  
Erik Hoel ◽  
Brennan Klein ◽  
Anshuman Swain ◽  
Ross Grebenow ◽  
Michael Levin
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Biological Systems ◽  
Protein Protein Interactions ◽  
Black Boxes ◽  
Protein Interactome ◽  
Gene Regulatory ◽  
Lower Uncertainty ◽  
Lower Noise

AbstractThe 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 demonstrate 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 to nodes that do not participate in the macroscale. These effects are more pronounced in interactomes of Eukaryota, as compared to Prokaryota. 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.

Protein Interactions for Functional Genomics

2012 ◽  
Vol 3 (4) ◽  
pp. 15-30
Author(s):  
Pablo Minguez ◽  
Joaquin Dopazo
Keyword(s):  
Graph Theory ◽  
Protein Interactions ◽  
Biological Networks ◽  
State Of The Art ◽  
Protein Protein Interactions ◽  
Pairwise Interactions ◽  
Novel Approach ◽  
Functional Profiling ◽  
Available Resources

Here the authors review the state of the art in the use of protein-protein interactions (ppis) within the context of the interpretation of genomic experiments. They report the available resources and methodologies used to create a curated compilation of ppis introducing a novel approach to filter interactions. Special attention is paid in the complexity of the topology of the networks formed by proteins (nodes) and pairwise interactions (edges). These networks can be studied using graph theory and a brief introduction to the characterization of biological networks and definitions of the more used network parameters is also given. Also a report on the available resources to perform different modes of functional profiling using ppi data is provided along with a discussion on the approaches that have typically been applied into this context. They also introduce a novel methodology for the evaluation of networks and some examples of its application.

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.

scholarly journals Handling Noise in Protein Interaction Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Fernanda B. Correia ◽  
Edgar D. Coelho ◽  
José L. Oliveira ◽  
Joel P. Arrais
Keyword(s):  
Network Topology ◽  
Protein Interactions ◽  
Biological Networks ◽  
Homo Sapiens ◽  
Random Networks ◽  
Protein Protein Interactions ◽  
Ppi Networks ◽  
Reference Set ◽  
Human Networks ◽  
Random Removal

Protein-protein interactions (PPIs) can be conveniently represented as networks, allowing the use of graph theory for their study. Network topology studies may reveal patterns associated with specific organisms. Here, we propose a new methodology to denoise PPI networks and predict missing links solely based on the network topology, the organization measurement (OM) method. The OM methodology was applied in the denoising of the PPI networks of two Saccharomyces cerevisiae datasets (Yeast and CS2007) and one Homo sapiens dataset (Human). To evaluate the denoising capabilities of the OM methodology, two strategies were applied. The first strategy compared its application in random networks and in the reference set networks, while the second strategy perturbed the networks with the gradual random addition and removal of edges. The application of the OM methodology to the Yeast and Human reference sets achieved an AUC of 0.95 and 0.87, in Yeast and Human networks, respectively. The random removal of 80% of the Yeast and Human reference set interactions resulted in an AUC of 0.71 and 0.62, whereas the random addition of 80% interactions resulted in an AUC of 0.75 and 0.72, respectively. Applying the OM methodology to the CS2007 dataset yields an AUC of 0.99. We also perturbed the network of the CS2007 dataset by randomly inserting and removing edges in the same proportions previously described. The false positives identified and removed from the network varied from 97%, when inserting 20% more edges, to 89%, when 80% more edges were inserted. The true positives identified and inserted in the network varied from 95%, when removing 20% of the edges, to 40%, after the random deletion of 80% edges. The OM methodology is sensitive to the topological structure of the biological networks. The obtained results suggest that the present approach can efficiently be used to denoise PPI networks.

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.

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