CellDesigner: A Graphical Biological Network Editor and Workbench Interfacing Simulator

2008 ◽  
pp. 422-434 ◽  
Author(s):  
Akira Funahashi ◽  
Mineo Morohashi ◽  
Yukiko Matsuoka ◽  
Akiya Jouraku ◽  
Hiroaki Kitano
Keyword(s):  
Biological Network

Differential Drug Target Selection in Blood Coagulation: What can we get from Computational Systems Biology Models?

2020 ◽  
Vol 26 (18) ◽  
pp. 2109-2115 ◽  
Author(s):  
Mikhail A. Panteleev ◽  
Anna A. Andreeva ◽  
Alexey I. Lobanov
Keyword(s):  
Blood Coagulation ◽  
Biological Network ◽  
Target Selection ◽  
Anticoagulation Therapy ◽  
Differential Regulation ◽  
Coagulation Cascade ◽  
Computational Systems Biology ◽  
Analysis Strategies ◽  
Optimal Target ◽  
Computational Systems

Discovery and selection of the potential targets are some of the important issues in pharmacology. Even when all the reactions and the proteins in a biological network are known, how does one choose the optimal target? Here, we review and discuss the application of the computational methods to address this problem using the blood coagulation cascade as an example. The problem of correct antithrombotic targeting is critical for this system because, although several anticoagulants are currently available, all of them are associated with bleeding risks. The advantages and the drawbacks of different sensitivity analysis strategies are considered, focusing on the approaches that emphasize: 1) the functional modularity and the multi-tasking nature of this biological network; and 2) the need to normalize hemostasis during the anticoagulation therapy rather than completely suppress it. To illustrate this effect, we show the possibility of the differential regulation of lag time and endogenous thrombin potential in the thrombin generation. These methods allow to identify the elements in the blood coagulation cascade that may serve as the targets for the differential regulation of this system.

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

2018 ◽  
Vol 14 (1) ◽  
pp. 4-10
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Gene Ontology ◽  
Topological Structure ◽  
Metabolic Networks ◽  
Biological Network ◽  
Genomic Sequence ◽  
Network Alignment ◽  
Future Directions ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

scholarly journals Data-driven biological network alignment that uses topological, sequence, and functional information

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shawn Gu ◽  
Tijana Milenković
Keyword(s):  
Prediction Accuracy ◽  
Biological Network ◽  
Network Alignment ◽  
Data Driven ◽  
Sequence Information ◽  
Functional Prediction ◽  
Research Knowledge ◽  
Topological Information ◽  
Functional Knowledge ◽  
Related Proteins

Abstract Background Network alignment (NA) can transfer functional knowledge between species’ conserved biological network regions. Traditional NA assumes that it is topological similarity (isomorphic-like matching) between network regions that corresponds to the regions’ functional relatedness. However, we recently found that functionally unrelated proteins are as topologically similar as functionally related proteins. So, we redefined NA as a data-driven method called TARA, which learns from network and protein functional data what kind of topological relatedness (rather than similarity) between proteins corresponds to their functional relatedness. TARA used topological information (within each network) but not sequence information (between proteins across networks). Yet, TARA yielded higher protein functional prediction accuracy than existing NA methods, even those that used both topological and sequence information. Results Here, we propose TARA++ that is also data-driven, like TARA and unlike other existing methods, but that uses across-network sequence information on top of within-network topological information, unlike TARA. To deal with the within-and-across-network analysis, we adapt social network embedding to the problem of biological NA. TARA++ outperforms protein functional prediction accuracy of existing methods. Conclusions As such, combining research knowledge from different domains is promising. Overall, improvements in protein functional prediction have biomedical implications, for example allowing researchers to better understand how cancer progresses or how humans age.

CNVs inform the biological network of Autism spectrum disorder

2021 ◽  
Vol 297 ◽  
pp. 113729
Author(s):  
Silvana Briuglia ◽  
Marco Calabrò ◽  
Anna Paola Capra ◽  
Maria Angela La Rosa ◽  
Concetta Crisafulli
Keyword(s):  
Autism Spectrum Disorder ◽  
Biological Network ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases

2013 ◽  
Vol 14 (1) ◽  
Author(s):  
Panagiotis Moulos ◽  
Julie Klein ◽  
Simon Jupp ◽  
Robert Stevens ◽  
Jean-Loup Bascands ◽  
...  
Keyword(s):  
Biological Network ◽  
Kidney Diseases

Biological network inference for drug discovery

2013 ◽  
Vol 18 (5-6) ◽  
pp. 256-264 ◽  
Author(s):  
Paola Lecca ◽  
Corrado Priami
Keyword(s):  
Drug Discovery ◽  
Network Inference ◽  
Biological Network

Eigenvalue Sensitivity-based Analysis for Evaluation of Biological Network Stability versus disturbances

2021 ◽  
pp. 110941
Author(s):  
Maryam Gholampour ◽  
Ali Khaki Sedigh ◽  
Mohammad Ghassem Mahjani ◽  
Abdorasoul Ghasemi
Keyword(s):  
Biological Network ◽  
Network Stability ◽  
Eigenvalue Sensitivity
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