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Fuzzy Intervention in Biological Phenomena
Nov.-Dec. 2012 (vol. 9 no. 6)
pp. 1819-1825
H. N. Nounou, Electr. & Comput. Eng. Program, Texas A&M Univ. at Qatar, Doha, Qatar
M. N. Nounou, Chem. Eng. Program, Texas A&M Univ. at Qatar, Doha, Qatar
N. Meskin, Dept. of Electr. Eng., Qatar Univ., Doha, Qatar
A. Datta, Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
E. R. Dougherty, Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
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 glycolytic-glycogenolytic 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.
Index Terms:
parameter estimation,biochemistry,biology computing,drugs,enzymes,fuzzy systems,genetics,genomics,molecular biophysics,generic pathway model,biological phenomena modeling,therapeutic intervention strategy,diseased network,genes,drugs,metabolites,complex processes,nonlinear dynamics,mathematical model,model-free nonlinear intervention strategy,fuzzy systems,parameter estimation,nonlinear process control design,model-free fuzzy intervention strategy,biological systems,glycolytic-glycogenolytic pathway model,purine metabolism pathway model,Mathematical model,Biological system modeling,Analytical models,Fuzzy models,Computational modeling,Bioinformatics,biological intervention,Fuzzy intervention,model-free intervention,fuzzy systems
Citation:
H. N. Nounou, M. N. Nounou, N. Meskin, A. Datta, E. R. Dougherty, "Fuzzy Intervention in Biological Phenomena," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 6, pp. 1819-1825, Nov.-Dec. 2012, doi:10.1109/TCBB.2012.113
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