Mass-Dispersed Gravitational Search Algorithm for Gene Regulatory Network Model Parameter Identification

2012 ◽  
pp. 62-72 ◽  
Author(s):  
Mohsen Davarynejad ◽  
Zary Forghany ◽  
Jan van den Berg
Keyword(s):  
Parameter Identification ◽  
Gene Regulatory Network ◽  
Network Model ◽  
Regulatory Network ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Model Parameter ◽  
Gene Regulatory ◽  
Gravitational Search ◽  
Model Parameter Identification

scholarly journals Enhancement of Gravitational Search Algorithm using A Differential Mutation Operator and Its Application on Reconstructing Gene Regulatory Network

2019 ◽  
Vol 12 (2) ◽  
pp. 176-186
Author(s):  
Chukiat Worasucheep
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Parameter Tuning ◽  
Small Scale ◽  
Mutation Operator ◽  
Gene Regulatory ◽  
Gravitational Search ◽  
Different Characteristics

Gravitational Search Algorithm (GSA) is a recent stochastic search algorithm that is inspired from the concepts of gravity rule and law of motion in physics. Despite its success and attractiveness, it has some coefficients and parameters that should be properly tuned to improve its performance. This paper studies the performance of GSA by varying the parameters that controls its gravitational force. Then a new differential mutation operator is proposed to enhance performance of GSA by accelerating its convergence. The proposed algorithm, namely DMGSA, is evaluated using 15 well-known benchmark functions from the special session of CEC2013 with different characteristics including randomly shifted optimum, rotation and non-separability. The results obviously confirms the performance achieved from the proposed mutation operator outperforms that from the attempts of parameter tuning in the original GSA. Lastly, DMGSA is applied for optimizing a small-scale gene regulatory network. The result demonstrates that its performance is highly competitive and clearly surpasses original GSA.

Construction of a Molecular Learning Network

2013 ◽  
Vol 17 (6) ◽  
pp. 913-918
Author(s):  
Tomohiro Shirakawa ◽  
◽  
Hiroshi Sato
Keyword(s):  
Associative Learning ◽  
Gene Regulatory Network ◽  
20Th Century ◽  
Network Model ◽  
Regulatory Network ◽  
Learning Ability ◽  
Learning Network ◽  
Unicellular Organisms ◽  
Gene Regulatory

Learning ability in unicellular organisms has been studied since the first half of the 20th century, but there is still no clear evidence of unicellular learning. Based on results from previous associative learning experiments using thePhysarumplasmodium, a gene regulatory network model of unicellular learning was constructed. The model demonstrates that, in principle, unicellular learning can be achieved through the cooperation of several biomolecules.

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