Fuzzy intervention in biological phenomena
Author | Nounou, H.N. |
Author | Nounou, M.N. |
Author | Meskin, Nader |
Author | Datta, A. |
Author | Dougherty, E.R. |
Available date | 2022-04-14T08:45:45Z |
Publication Date | 2012 |
Publication Name | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/TCBB.2012.113 |
Abstract | An 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. |
Sponsor | Qatar Foundation; Qatar National Research Fund |
Language | en |
Publisher | IEEE |
Subject | Biological 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 |
Type | Article |
Pagination | 1819-1825 |
Issue Number | 6 |
Volume Number | 9 |
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