PRO-Simat: Protein network simulation and design tool

Please always quote using this URN: urn:nbn:de:bvb:20-opus-350034
  • PRO-Simat is a simulation tool for analysing protein interaction networks, their dynamic change and pathway engineering. It provides GO enrichment, KEGG pathway analyses, and network visualisation from an integrated database of more than 8 million protein-protein interactions across 32 model organisms and the human proteome. We integrated dynamical network simulation using the Jimena framework, which quickly and efficiently simulates Boolean genetic regulatory networks. It enables simulation outputs with in-depth analysis of the type, strength,PRO-Simat is a simulation tool for analysing protein interaction networks, their dynamic change and pathway engineering. It provides GO enrichment, KEGG pathway analyses, and network visualisation from an integrated database of more than 8 million protein-protein interactions across 32 model organisms and the human proteome. We integrated dynamical network simulation using the Jimena framework, which quickly and efficiently simulates Boolean genetic regulatory networks. It enables simulation outputs with in-depth analysis of the type, strength, duration and pathway of the protein interactions on the website. Furthermore, the user can efficiently edit and analyse the effect of network modifications and engineering experiments. In case studies, applications of PRO-Simat are demonstrated: (i) understanding mutually exclusive differentiation pathways in Bacillus subtilis, (ii) making Vaccinia virus oncolytic by switching on its viral replication mainly in cancer cells and triggering cancer cell apoptosis and (iii) optogenetic control of nucleotide processing protein networks to operate DNA storage. Multilevel communication between components is critical for efficient network switching, as demonstrated by a general census on prokaryotic and eukaryotic networks and comparing design with synthetic networks using PRO-Simat. The tool is available at https://prosimat.heinzelab.de/ as a web-based query server.show moreshow less

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Metadaten
Author: Rana Salihoglu, Mugdha Srivastava, Chunguang Liang, Klaus Schilling, Aladar Szalay, Elena Bencurova, Thomas Dandekar
URN:urn:nbn:de:bvb:20-opus-350034
Document Type:Journal article
Faculties:Fakultät für Mathematik und Informatik / Institut für Informatik
Medizinische Fakultät / Theodor-Boveri-Institut für Biowissenschaften
Language:English
Parent Title (English):Computational and Structural Biotechnology Journal
ISSN:2001-0370
Year of Completion:2023
Volume:21
Pagenumber:2767-2779
Source:Computational and Structural Biotechnology Journal (2023) 21:2767-2779. DOI: 10.1016/j.csbj.2023.04.023
DOI:https://doi.org/10.1016/j.csbj.2023.04.023
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Tag:DNA storage; dynamic protein-protein interactions; network simulation; oncolytic virus; optogenetics; protein analysis; signalling pathways
Release Date:2024/03/28
Licence (German):License LogoCC BY-NC-ND: Creative-Commons-Lizenz: Namensnennung, Nicht kommerziell, Keine Bearbeitungen 4.0 International