# Neural modeling of Gene Regulatory Network using Firefly algorithm

Title | Neural modeling of Gene Regulatory Network using Firefly algorithm |

Publication Type | Conference Paper |

Year of Publication | 2015 |

Authors | Santra, N., Biswas, S., Acharyya, S. |

Conference Name | 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON) |

Date Published | dec |

Keywords | Algorithm, biological tissues, biology computing, cellular environment, computational biologists, computational reconstruction, disease motifs, diseased tissues, diseases, firefly algorithm, Gene expression, gene regulatory network, genetic algorithms, Genetics, GRN reconstruction, Mathematical model, meta-heuristic algorithm, metabolites, microarray gene expression, microarray gene expression data, molecular biophysics, neural modeling, normal metabolic pathway, optimisation, Optimization, parameter learning, parameter optimization, proteins, pubcrawl170110, Recurrent neural networks, search problems, structure optimization, synthetic GRN data set, Time series analysis, virtual network |

Abstract | Genes, proteins and other metabolites present in cellular environment exhibit a virtual network that represents the regulatory relationship among its constituents. This network is called Gene Regulatory Network (GRN). Computational reconstruction of GRN reveals the normal metabolic pathway as well as disease motifs. Availability of microarray gene expression data from normal and diseased tissues makes the job easier for computational biologists. Reconstruction of GRN is based on neural modeling. Here we have used discrete and continuous versions of a meta-heuristic algorithm named Firefly algorithm for structure and parameter learning of GRNs respectively. The discrete version for this problem is proposed by us and it has been applied to explore the discrete search space of GRN structure. To evaluate performance of the algorithm, we have used a widely used synthetic GRN data set. The algorithm shows an accuracy rate above 50% in finding GRN. The accuracy level of the performance of Firefly algorithm in structure and parameter optimization of GRN is promising. |

DOI | 10.1109/UPCON.2015.7456720 |

Citation Key | santra_neural_2015 |

- parameter optimization
- microarray gene expression
- microarray gene expression data
- molecular biophysics
- neural modeling
- normal metabolic pathway
- optimisation
- optimization
- parameter learning
- metabolites
- proteins
- pubcrawl170110
- Recurrent neural networks
- search problems
- structure optimization
- synthetic GRN data set
- Time series analysis
- virtual network
- firefly algorithm
- biological tissues
- biology computing
- cellular environment
- computational biologists
- computational reconstruction
- disease motifs
- diseased tissues
- diseases
- Algorithm
- Gene expression
- gene regulatory network
- genetic algorithms
- Genetics
- GRN reconstruction
- Mathematical model
- meta-heuristic algorithm