{"publist_id":"325","type":"journal_article","status":"public","month":"01","publication":"BMC Systems Biology","year":"2007","main_file_link":[{"url":"http://www.biomedcentral.com/1752-0509/1/4","open_access":"0"}],"volume":1,"citation":{"apa":"Schaub, M., Henzinger, T. A., &#38; Fisher, J. (2007). Qualitative networks: A symbolic approach to analyze biological signaling networks. <i>BMC Systems Biology</i>. BioMed Central. <a href=\"https://doi.org/10.1186/1752-0509-1-4\">https://doi.org/10.1186/1752-0509-1-4</a>","short":"M. Schaub, T.A. Henzinger, J. Fisher, BMC Systems Biology 1 (2007).","chicago":"Schaub, Marc, Thomas A Henzinger, and Jasmin Fisher. “Qualitative Networks: A Symbolic Approach to Analyze Biological Signaling Networks.” <i>BMC Systems Biology</i>. BioMed Central, 2007. <a href=\"https://doi.org/10.1186/1752-0509-1-4\">https://doi.org/10.1186/1752-0509-1-4</a>.","ieee":"M. Schaub, T. A. Henzinger, and J. Fisher, “Qualitative networks: A symbolic approach to analyze biological signaling networks,” <i>BMC Systems Biology</i>, vol. 1, no. 4. BioMed Central, 2007.","mla":"Schaub, Marc, et al. “Qualitative Networks: A Symbolic Approach to Analyze Biological Signaling Networks.” <i>BMC Systems Biology</i>, vol. 1, no. 4, BioMed Central, 2007, doi:<a href=\"https://doi.org/10.1186/1752-0509-1-4\">10.1186/1752-0509-1-4</a>.","ama":"Schaub M, Henzinger TA, Fisher J. Qualitative networks: A symbolic approach to analyze biological signaling networks. <i>BMC Systems Biology</i>. 2007;1(4). doi:<a href=\"https://doi.org/10.1186/1752-0509-1-4\">10.1186/1752-0509-1-4</a>","ista":"Schaub M, Henzinger TA, Fisher J. 2007. Qualitative networks: A symbolic approach to analyze biological signaling networks. BMC Systems Biology. 1(4)."},"tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"issue":"4","date_created":"2018-12-11T12:08:41Z","date_published":"2007-01-08T00:00:00Z","author":[{"first_name":"Marc","full_name":"Schaub, Marc A","last_name":"Schaub"},{"full_name":"Thomas Henzinger","first_name":"Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","last_name":"Henzinger","orcid":"0000−0002−2985−7724"},{"last_name":"Fisher","full_name":"Fisher, Jasmin","first_name":"Jasmin"}],"day":"08","publication_status":"published","publisher":"BioMed Central","intvolume":"         1","title":"Qualitative networks: A symbolic approach to analyze biological signaling networks","doi":"10.1186/1752-0509-1-4","license":"https://creativecommons.org/licenses/by/4.0/","extern":1,"_id":"4405","abstract":[{"text":"Background\nA central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico.\n\nResults\nWe consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis.\n\nConclusion\nWe introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology.","lang":"eng"}],"quality_controlled":0,"date_updated":"2021-01-12T07:56:44Z"}