Abstract
Biological networks describe the relationships among molecular elements and help in the deep understanding of the biological mechanisms and functions. One of the common problems is to identify the set of biomolecules that could be targeted by drugs to drive the state transition of the cells from disease states to health states called desired states as the realization of the therapy of complex diseases. Most previous studies based on the output control determine the set of steering nodes without considering available biological information. In this study, we propose a strategy by using the additionally available information like the FDA-approved drug targets to restrict the range for choosing steering nodes in output control instead, where we call it the Set Preference Output Control (SPOC) problem. A graphic-theoretic algorithm is proposed to approximately tackle it by using the Maximum Weighted Complete Matching (MWCM). The computation experiment results from two biological networks illustrate that our proposed SPOC strategy outperforms the full control and output control strategies to identify drug targets. Finally, the case studies further demonstrate the role of the combination therapy in two biological networks, which reveals that our proposed SPOC strategy is potentially applicable for more complicated cases.
This work was supported by Natural Sciences and Engineering Research Council of Canada (NSERC), the National Natural Science Foundation of China [61772552], and the Program of Independent Exploration Innovation in Central South University (2019zzts959), the Fundamental Research Funds for the Central Universities, CSU (2282019SYLB004).
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Gao, H., Li, M., Wu, FX. (2020). SPOC: Identification of Drug Targets in Biological Networks via Set Preference Output Control. In: Cai, Z., Mandoiu, I., Narasimhan, G., Skums, P., Guo, X. (eds) Bioinformatics Research and Applications. ISBRA 2020. Lecture Notes in Computer Science(), vol 12304. Springer, Cham. https://doi.org/10.1007/978-3-030-57821-3_3
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