scholarly journals Revisiting Parameter Estimation in Biological Networks: Influence of Symmetries

2019 ◽  
Author(s):  
Jithin K. Sreedharan ◽  
Krzysztof Turowski ◽  
Wojciech Szpankowski
Keyword(s):  
Parameter Estimation ◽  
Protein Interactions ◽  
Biological Networks ◽  
Likelihood Estimation ◽  
Accurate Estimation ◽  
Protein Protein Interactions ◽  
Graph Models ◽  
History Of ◽  
The Right ◽  
Right Order

AbstractGraph models often give us a deeper understanding of real-world networks. In the case of biological networks they help in predicting the evolution and history of biomolecule interactions, provided we map properly real networks into the corresponding graph models. In this paper, we show that for biological graph models many of the existing parameter estimation techniques overlook the critical property of graph symmetry (also known formally as graph automorphisms), thus the estimated parameters give statistically insignificant results concerning the observed network. To demonstrate it and to develop accurate estimation procedures, we focus on the biologically inspired duplication-divergence model, and the up-to-date data of protein-protein interactions of seven species including human and yeast. Using exact recurrence relations of some prominent graph statistics, we devise a parameter estimation technique that provides the right order of symmetries and uses phylogenetically old proteins as the choice of seed graph nodes. We also find that our results are consistent with the ones obtained from maximum likelihood estimation (MLE). However, the MLE approach is significantly slower than our methods in practice.

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.

XGBoost and Network Analysis for Prediction of Proteins Affecting Insulin based on Protein Protein Interactions

2020 ◽  
pp. 253-262
Author(s):  
Mohammad Hamim Zajuli Al Faroby ◽  
Mohammad Isa Irawan ◽  
Ni Nyoman Tri Puspaningsih
Keyword(s):  
Protein Interactions ◽  
Interaction Analysis ◽  
Synthesis Process ◽  
Gradient Boosting ◽  
Protein Protein Interactions ◽  
Central Function ◽  
Extreme Gradient Boosting ◽  
Main Protein ◽  
The Right ◽  
Roc Score

Protein Interaction Analysis (PPI) can be used to identify proteins that have a supporting function on the main protein, especially in the synthesis process. Insulin is synthesized by proteins that have the same molecular function covering different but mutually supportive roles. To identify this function, the translation of Gene Ontology (GO) gives certain characteristics to each protein. This study purpose to predict proteins that interact with insulin using the centrality method as a feature extractor and extreme gradient boosting as a classification algorithm. Characteristics using the centralized method produces  features as a central function of protein. Classification results are measured using measurements, precision, recall and ROC scores. Optimizing the model by finding the right parameters produces an accuracy of  and a ROC score of . The prediction model produced by XGBoost has capabilities above the average of other machine learning methods.

scholarly journals Discovery, development and application of drugs targeting BCL-2 pro-survival proteins in cancer

2021 ◽  
Author(s):  
Erinna F. Lee ◽  
W. Douglas Fairlie
Keyword(s):  
Structural Biology ◽  
Protein Interactions ◽  
Basic Science ◽  
Great Success ◽  
Protein Protein Interactions ◽  
New Class ◽  
Success Stories ◽  
History Of ◽  
Approved Drugs ◽  
Close Relatives

The discovery of a new class of small molecule compounds that target the BCL-2 family of anti-apoptotic proteins is one of the great success stories of basic science leading to translational outcomes in the last 30 years. The eponymous BCL-2 protein was identified over 30 years ago due to its association with cancer. However, it was the unveiling of the biochemistry and structural biology behind it and its close relatives’ mechanism(s)-of-action that provided the inspiration for what are now known as ‘BH3-mimetics’, the first clinically approved drugs designed to specifically inhibit protein–protein interactions. Herein, we chart the history of how these drugs were discovered, their evolution and application in cancer treatment.

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.

scholarly journals The structural evolution of host-pathogen protein interactions: an integrative approach

2019 ◽  
Author(s):  
Anderson F. Brito ◽  
John W. Pinney
Keyword(s):  
Protein Interactions ◽  
Homology Modelling ◽  
Structural Evolution ◽  
Integrative Approach ◽  
Binding Affinities ◽  
Protein Protein Interactions ◽  
Host Pathogen ◽  
Cell Membrane Proteins ◽  
Pathogen Protein ◽  
History Of

ABSTRACTThe evolution of protein-protein interactions (PPIs) is directly influenced by the evolutionary histories of the genes and the species encoding the interacting proteins. When it comes to PPIs of host-pathogen systems, the complexity of their evolution is much higher, as two independent, but biologically associated entities, are involved. In this work, an integrative approach combining phylogenetics, tree reconciliations, ancestral sequence reconstructions, and homology modelling is proposed for studying the evolution of host-pathogen PPIs. As a case study, we analysed the evolution of interactions between herpesviral glycoproteins gD/gG and the cell membrane proteins nectins. By modelling the structures of more than 12,000 ancestral states of these virus-host complexes it was found that in early times of their evolution, these proteins were unable to interact, most probably due to electrostatic incompatibilities between their interfaces. After the event of gene duplication that gave rise to a paralog of gD (known as gG), both protein lineages evolved following distinct functional constraints, with most gD reaching high binding affinities towards nectins, while gG lost such ability, most probably due to a process of neofunctionalization. Based on their favourable interaction energies (negative ΔG), it is possible to hypothesize that apart from nectins 1 and 2, some alphaherpesviruses might also use nectins 3 and 4 as cell receptors. These findings show that the proposed integrative method is suitable for modelling the evolution of host-pathogen protein interactions, and useful for raising new hypotheses that broaden our understanding about the evolutionary history of PPIs, and their molecular functioning.

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.

Empires of the Senses

2019 ◽  
Author(s):  
Andrew J. Rotter
Keyword(s):  
Lived Experience ◽  
Cultural History ◽  
The Philippines ◽  
Self Control ◽  
Sensory History ◽  
History Of ◽  
The Senses ◽  
History Of Empire ◽  
The Right ◽  
Right Order

This book offers a sensory history of the British in India from the formal imposition of their rule to its end and the Americans in the Philippines from annexation to independence. A social and cultural history of empire, it focuses on quotidian life. It analyzes how the senses created mutual impressions of the agents of imperialism and their subjects and highlights connections between apparently disparate items, including the lived experience of empire, the otherwise unremarkable comments (and complaints) found in memoirs and reports, the appearance of lepers, the sound of bells, the odor of excrement, the feel of cloth against skin, the first taste of a mango or meat spiced with cumin. Men and women in imperial India and the Philippines had different ideas from the start about what looked, sounded, smelled, felt, and tasted good or bad. Both the British and the Americans saw themselves as the civilizers of what they judged backward societies and believed that a vital part of the civilizing process was to put the senses in the right order of priority and to ensure them against offense or affront. People without manners who respected the senses lacked self-control; they were uncivilized and thus unfit for self-government. Societies that looked shabby, were noisy and smelly, felt wrong, and consumed unwholesome food in unmannerly ways were not prepared to form independent polities and stand on their own. It was the duty of allegedly more sensorily advanced westerners to put the senses right before withdrawing the most obvious manifestations of their power.

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.

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