共查询到20条相似文献,搜索用时 15 毫秒
1.
Boris Sobolev Dmitry Filimonov Alexey Lagunin Alexey Zakharov Olga Koborova Alexander Kel Vladimir Poroikov 《BMC bioinformatics》2010,11(1):313
Background
The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published in silico method PAAS was applied for prediction of interactions between protein kinases and their substrates. 相似文献2.
Rafal Zielinski Pawel F Przytycki Jie Zheng David Zhang Teresa M Przytycka Jacek Capala 《BMC systems biology》2009,3(1):88-10
Background
Cellular response to external stimuli requires propagation of corresponding signals through molecular signaling pathways. However, signaling pathways are not isolated information highways, but rather interact in a number of ways forming sophisticated signaling networks. Since defects in signaling pathways are associated with many serious diseases, understanding of the crosstalk between them is fundamental for designing molecularly targeted therapy. Unfortunately, we still lack technology that would allow high throughput detailed measurement of activity of individual signaling molecules and their interactions. This necessitates developing methods to prioritize selection of the molecules such that measuring their activity would be most informative for understanding the crosstalk. Furthermore, absence of the reaction coefficients necessary for detailed modeling of signal propagation raises the question whether simple parameter-free models could provide useful information about such pathways. 相似文献3.
Background
New approaches are needed for large-scale predictive modeling of cellular signaling networks. While mass action and enzyme kinetic approaches require extensive biochemical data, current logic-based approaches are used primarily for qualitative predictions and have lacked direct quantitative comparison with biochemical models. 相似文献4.
Avi Ma'ayan Sherry L Jenkins Ryan L Webb Seth I Berger Sudarshan P Purushothaman Noura S Abul-Husn Jeremy M Posner Tony Flores Ravi Iyengar 《BMC systems biology》2009,3(1):10-11
Background
Studies of cellular signaling indicate that signal transduction pathways combine to form large networks of interactions. Viewing protein-protein and ligand-protein interactions as graphs (networks), where biomolecules are represented as nodes and their interactions are represented as links, is a promising approach for integrating experimental results from different sources to achieve a systematic understanding of the molecular mechanisms driving cell phenotype. The emergence of large-scale signaling networks provides an opportunity for topological statistical analysis while visualization of such networks represents a challenge. 相似文献5.
Background
Mathematical modeling of biological networks is an essential part of Systems Biology. Developing and using such models in order to understand gene regulatory networks is a major challenge. 相似文献6.
Background
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. 相似文献7.
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Dennis YQ Wang Luca Cardelli Andrew Phillips Nir Piterman Jasmin Fisher 《BMC systems biology》2009,3(1):118-17
Background
The epidermal growth factor receptor (EGFR) signaling pathway plays a key role in regulation of cellular growth and development. While highly studied, it is still not fully understood how the signal is orchestrated. One of the reasons for the complexity of this pathway is the extensive network of inter-connected components involved in the signaling. In the aim of identifying critical mechanisms controlling signal transduction we have performed extensive analysis of an executable model of the EGFR pathway using the stochastic pi-calculus as a modeling language. 相似文献11.
Background
The architectural structure of cellular networks provides a framework for innovations as well as constraints for protein evolution. This issue has previously been studied extensively by analyzing protein interaction networks. However, it is unclear how signaling networks influence and constrain protein evolution and conversely, how protein evolution modifies and shapes the functional consequences of signaling networks. In this study, we constructed a human signaling network containing more than 1,600 nodes and 5,000 links through manual curation of signaling pathways, and analyzed the d N/d S values of human-mouse orthologues on the network. 相似文献12.
Background
Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. 相似文献13.
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Background
Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. 相似文献15.
Background
Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. 相似文献16.
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Background
Recent years have seen a dramatic increase in the use of mathematical modeling to gain insight into gene regulatory network behavior across many different organisms. In particular, there has been considerable interest in using mathematical tools to understand how multistable regulatory networks may contribute to developmental processes such as cell fate determination. Indeed, such a network may subserve the formation of unicellular leaf hairs (trichomes) in the model plant Arabidopsis thaliana. 相似文献18.
Laurent Messer Ghada Alsaleh Jean-Marie Freyssinet Fatiha Zobairi Isabelle Leray Jacques-Eric Gottenberg Jean Sibilia Florence Toti-Orfanoudakis Dominique Wachsmann 《Arthritis research & therapy》2009,11(2):R40
Introduction
In the present study, we investigated the ability of microparticles isolated from synovial fluids from patients with rheumatoid arthritis or osteoarthritis to induce the synthesis and release of key cytokines of B-lymphocyte modulation such as B cell-activating factor, thymic stroma lymphopoietin, and secretory leukocyte protease inhibitor by rheumatoid fibroblast-like synoviocytes. 相似文献19.
Background
Protein-protein interaction (PPI) is fundamental to many biological processes. In the course of evolution, biological networks such as protein-protein interaction networks have developed. Biological networks of different species can be aligned by finding instances (e.g. proteins) with the same common ancestor in the evolutionary process, so-called orthologs. For a better understanding of the evolution of biological networks, such aligned networks have to be explored. Visualization can play a key role in making the various relationships transparent. 相似文献20.
Dominik M Wittmann Jan Krumsiek Julio Saez-Rodriguez Douglas A Lauffenburger Steffen Klamt Fabian J Theis 《BMC systems biology》2009,3(1):98-21