Motivation: Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets. Results: In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets. Availability and Implementation: The source code is available at https://github.com/SPIDDOR/SPIDDOR . Contact: itzirurzun@alumni.unav.es , itroconiz@unav.es. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets. Results: In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets. Availability and Implementation: The source code is available at https://github.com/SPIDDOR/SPIDDOR . Contact: itzirurzun@alumni.unav.es , itroconiz@unav.es. Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: Feiyan Liu; Linda B S Aulin; Sebastiaan S A Kossen; Julius Cathalina; Marlotte Bremmer; Amanda C Foks; Piet H van der Graaf; Matthijs Moerland; Johan G C van Hasselt Journal: J Pharmacokinet Pharmacodyn Date: 2022-10-19 Impact factor: 2.410
Authors: Eirini Tsirvouli; Vasundra Touré; Barbara Niederdorfer; Miguel Vázquez; Åsmund Flobak; Martin Kuiper Journal: Front Mol Biosci Date: 2020-10-09
Authors: Violeta Balbas-Martinez; Leire Ruiz-Cerdá; Itziar Irurzun-Arana; Ignacio González-García; An Vermeulen; José David Gómez-Mantilla; Iñaki F Trocóniz Journal: PLoS One Date: 2018-03-07 Impact factor: 3.240