8,986 research outputs found
Analysis of feedback loops and robustness in network evolution based on Boolean models
<p>Abstract</p> <p>Background</p> <p>Many biological networks such as protein-protein interaction networks, signaling networks, and metabolic networks have topological characteristics of a scale-free degree distribution. Preferential attachment has been considered as the most plausible evolutionary growth model to explain this topological property. Although various studies have been undertaken to investigate the structural characteristics of a network obtained using this growth model, its dynamical characteristics have received relatively less attention.</p> <p>Results</p> <p>In this paper, we focus on the robustness of a network that is acquired during its evolutionary process. Through simulations using Boolean network models, we found that preferential attachment increases the number of coupled feedback loops in the course of network evolution. Whereas, if networks evolve to have more coupled feedback loops rather than following preferential attachment, the resulting networks are more robust than those obtained through preferential attachment, although both of them have similar degree distributions.</p> <p>Conclusion</p> <p>The presented analysis demonstrates that coupled feedback loops may play an important role in network evolution to acquire robustness. The result also provides a hint as to why various biological networks have evolved to contain a number of coupled feedback loops.</p
Key protein identification by integrating protein complex information and multi-biological features
Identifying key proteins based on protein-protein interaction networks has emerged as a prominent area of research in bioinformatics. However, current methods exhibit certain limitations, such as the omission of subcellular localization information and the disregard for the impact of topological structure noise on the reliability of key protein identification. Moreover, the influence of proteins outside a complex but interacting with proteins inside the complex on complex participation tends to be overlooked. Addressing these shortcomings, this paper presents a novel method for key protein identification that integrates protein complex information with multiple biological features. This approach offers a comprehensive evaluation of protein importance by considering subcellular localization centrality, topological centrality weighted by gene ontology (GO) similarity and complex participation centrality. Experimental results, including traditional statistical metrics, jackknife methodology metric and key protein overlap or difference, demonstrate that the proposed method not only achieves higher accuracy in identifying key proteins compared to nine classical methods but also exhibits robustness across diverse protein-protein interaction networks
Lethality and entropy of protein interaction networks
We characterize protein interaction networks in terms of network entropy. This approach suggests a ranking principle, which strongly correlates with elements of functional importance, such as lethal proteins. Our combined analysis of protein interaction networks and functional profiles in single cellular yeast and multi-cellular worm shows that proteins with large contribution to network entropy are preferentially lethal. While entropy is inherently a dynamical concept, the present analysis incorporates only structural information. Our result therefore highlights the importance of topological features, which appear as correlates of an underlying dynamical property, and which in turn determine functional traits. We argue that network entropy is a natural extension of previously studied observables, such as pathway multiplicity and centrality. It is also applicable to networks in which the processes can be quantified and therefore serves as a link to study questions of structural and dynamical robustness in a unified way
Algebraic and Topological Indices of Molecular Pathway Networks in Human Cancers
Protein-protein interaction networks associated with diseases have gained
prominence as an area of research. We investigate algebraic and topological
indices for protein-protein interaction networks of 11 human cancers derived
from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We find a
strong correlation between relative automorphism group sizes and topological
network complexities on the one hand and five year survival probabilities on
the other hand. Moreover, we identify several protein families (e.g. PIK, ITG,
AKT families) that are repeated motifs in many of the cancer pathways.
Interestingly, these sources of symmetry are often central rather than
peripheral. Our results can aide in identification of promising targets for
anti-cancer drugs. Beyond that, we provide a unifying framework to study
protein-protein interaction networks of families of related diseases (e.g.
neurodegenerative diseases, viral diseases, substance abuse disorders).Comment: 15 pages, 4 figure
Hierarchy and assortativity as new tools for affinity investigation: the case of the TBA aptamer-ligand complex
Aptamers are single stranded DNA, RNA or peptide sequences having the ability
to bind a variety of specific targets (proteins, molecules as well as ions).
Therefore, aptamer production and selection for therapeutic and diagnostic
applications is very challenging. Usually they are in vitro generated, but,
recently, computational approaches have been developed for the in silico
selection, with a higher affinity for the specific target. Anyway, the
mechanism of aptamer-ligand formation is not completely clear, and not obvious
to predict. This paper aims to develop a computational model able to describe
aptamer-ligand affinity performance by using the topological structure of the
corresponding graphs, assessed by means of numerical tools such as the
conventional degree distribution, but also the rank-degree distribution
(hierarchy) and the node assortativity. Calculations are applied to the
thrombin binding aptamer (TBA), and the TBA-thrombin complex, produced in the
presence of Na+ or K+. The topological analysis reveals different affinity
performances between the macromolecules in the presence of the two cations, as
expected by previous investigations in literature. These results nominate the
graph topological analysis as a novel theoretical tool for testing affinity.
Otherwise, starting from the graphs, an electrical network can be obtained by
using the specific electrical properties of amino acids and nucleobases.
Therefore, a further analysis concerns with the electrical response, which
reveals that the resistance sensitively depends on the presence of sodium or
potassium thus posing resistance as a crucial physical parameter for testing
affinity.Comment: 12 pages, 5 figure
Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening
This work introduces a number of algebraic topology approaches, such as
multicomponent persistent homology, multi-level persistent homology and
electrostatic persistence for the representation, characterization, and
description of small molecules and biomolecular complexes. Multicomponent
persistent homology retains critical chemical and biological information during
the topological simplification of biomolecular geometric complexity.
Multi-level persistent homology enables a tailored topological description of
inter- and/or intra-molecular interactions of interest. Electrostatic
persistence incorporates partial charge information into topological
invariants. These topological methods are paired with Wasserstein distance to
characterize similarities between molecules and are further integrated with a
variety of machine learning algorithms, including k-nearest neighbors, ensemble
of trees, and deep convolutional neural networks, to manifest their descriptive
and predictive powers for chemical and biological problems. Extensive numerical
experiments involving more than 4,000 protein-ligand complexes from the PDBBind
database and near 100,000 ligands and decoys in the DUD database are performed
to test respectively the scoring power and the virtual screening power of the
proposed topological approaches. It is demonstrated that the present approaches
outperform the modern machine learning based methods in protein-ligand binding
affinity predictions and ligand-decoy discrimination
Increased signaling entropy in cancer requires the scale-free property of protein interaction networks
One of the key characteristics of cancer cells is an increased phenotypic
plasticity, driven by underlying genetic and epigenetic perturbations. However,
at a systems-level it is unclear how these perturbations give rise to the
observed increased plasticity. Elucidating such systems-level principles is key
for an improved understanding of cancer. Recently, it has been shown that
signaling entropy, an overall measure of signaling pathway promiscuity, and
computable from integrating a sample's gene expression profile with a protein
interaction network, correlates with phenotypic plasticity and is increased in
cancer compared to normal tissue. Here we develop a computational framework for
studying the effects of network perturbations on signaling entropy. We
demonstrate that the increased signaling entropy of cancer is driven by two
factors: (i) the scale-free (or near scale-free) topology of the interaction
network, and (ii) a subtle positive correlation between differential gene
expression and node connectivity. Indeed, we show that if protein interaction
networks were random graphs, described by Poisson degree distributions, that
cancer would generally not exhibit an increased signaling entropy. In summary,
this work exposes a deep connection between cancer, signaling entropy and
interaction network topology.Comment: 20 pages, 5 figures. In Press in Sci Rep 201
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