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AuthorNounou, H.N.
AuthorNounou, M.N.
AuthorMeskin, Nader
AuthorDatta, A.
AuthorDougherty, E.R.
Available date2022-04-14T08:45:45Z
Publication Date2012
Publication NameIEEE/ACM Transactions on Computational Biology and Bioinformatics
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/TCBB.2012.113
URIhttp://hdl.handle.net/10576/29830
AbstractAn important objective of modeling biological phenomena is to develop therapeutic intervention strategies to move an undesirable state of a diseased network toward a more desirable one. Such transitions can be achieved by the use of drugs to act on some genes/metabolites that affect the undesirable behavior. Due to the fact that biological phenomena are complex processes with nonlinear dynamics that are impossible to perfectly represent with a mathematical model, the need for model-free nonlinear intervention strategies that are capable of guiding the target variables to their desired values often arises. In many applications, fuzzy systems have been found to be very useful for parameter estimation, model development and control design of nonlinear processes. In this paper, a model-free fuzzy intervention strategy (that does not require a mathematical model of the biological phenomenon) is proposed to guide the target variables of biological systems to their desired values. The proposed fuzzy intervention strategy is applied to three different biological models: a glycolyticglycogenolytic pathway model, a purine metabolism pathway model, and a generic pathway model. The simulation results for all models demonstrate the effectiveness of the proposed scheme. 2013 IEEE.
SponsorQatar Foundation; Qatar National Research Fund
Languageen
PublisherIEEE
SubjectBiological intervention
Biological phenomena
Fuzzy intervention
Intervention strategy
Model development
Model free
Therapeutic intervention
Undesirable state
Complex networks
Fuzzy systems
Mathematical models
Biological systems
purine derivative
article
biological model
biology
chemistry
computer simulation
fuzzy logic
glycogenolysis
glycolysis
metabolism
methodology
Monte Carlo method
nonlinear system
Computational Biology
Computer Simulation
Fuzzy Logic
Glycogenolysis
Glycolysis
Metabolic Networks and Pathways
Models, Biological
Monte Carlo Method
Nonlinear Dynamics
Purines
TitleFuzzy intervention in biological phenomena
TypeArticle
Pagination1819-1825
Issue Number6
Volume Number9
dc.accessType Abstract Only


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