Abstract
Inferring novel findings from known biological knowledge is one of the ultimate goals in systems biology. However, the observation of system-level responses to a given perturbation has not been thoroughly explored due to the lack of proper large-scale inference models. We developed a novel expert system that can be applied to conventional biological networks based on the production rule system which works by transforming networks into a knowledgebase. Testing on large-scale multi-level biomedical networks confirmed the applicability of our system and revealed that hundreds of molecules are affected by the cascades of given signals, thereby activating or repressing key pathways in a cell.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Friedman-Hill, E.J.: Jess, the Expert System Shell for the Java Platform (2002)
Forgy, C.L.: Rete: a fast algorithm for the many pattern/many object pattern match problem. Artif. Intell. 19, 17–37 (1982)
Rawlings, J.S., Rosler, K.M., Harrison, D.A.: The JAK/STAT signaling pathway. J. Cell Sci. 117, 1281–1283 (2004)
Bethune, G., Bethune, D., Ridgway, N., Xu, Z.: Epidermal growth factor receptor (EGFR) in lung cancer: an overview and update. J. Thorac. Dis. 2, 48–51 (2010)
Bhargava, R., Gerald, W.L., Li, A.R., Pan, Q., Lal, P., Ladanyi, M., Chen, B.: EGFR gene amplification in breast cancer: correlation with epidermal growth factor receptor mRNA and protein expression and HER-2 status and absence of EGFR-activating mutations. Mod. Pathol. 18, 1027–1033 (2005)
Gysin, S., Salt, M., Young, A., McCormick, F.: Therapeutic Strategies for Targeting Ras Proteins. Genes Cancer 2, 359–372 (2011)
Acknowledgement
This work was supported by the Bio-Synergy Research Project (NRF-2014M3A9C4066449) of the Ministry of Science, ICT and Future Planning through the National Research Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Kim, S., Nam, H. (2017). In Silico Simulation of Signal Cascades in Biomedical Networks Based on the Production Rule System. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-59575-7_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59574-0
Online ISBN: 978-3-319-59575-7
eBook Packages: Computer ScienceComputer Science (R0)