酵母菌與爪蟾屬青蛙蛋之基因調控網路：改良型基因演算法

Abstract

利用改良型基因演算法，針對酵母菌與爪蟾屬青蛙蛋分別做基因調控網路modified power-law model與S-system建模。藉由酵母菌實驗和爪蟾屬 Michaelis-Menten model所得到的時間點資料集做訓練，以獲取最佳化的參數，因為modified power-law model和S-system可以清楚地描述基因在生成或消耗反應時是催化作用還是抑制作用，鑒於兩者的特色，對酵母菌的細胞週期與在爪蟾屬青蛙蛋細胞週期中的有絲分裂控制，能明瞭其基因的反應情形。具有遷移作用的改良型基因演算法不但可以做到全域搜尋，還可以精英主義的概念取得最佳的個體。最終得到的基因調控網路可以提供給生物學家在酵母菌和爪蟾屬青蛙蛋細胞週期方面做更深入的研究。

An improved genetic algorithm is proposed to achieve gene regulatory network modeling of Xenopus frog egg in S-system and yeast in modified power-law model respectively. Via the time-course datasets from experiment of yeast and Michaelis-Menten model of Xenopus, the optimal parameters are learned. The modified power-law model and S-system can clearly describe activative and inhibitory interaction between genes as generating and consuming process. We concern cell cycle of yeast and the mitotic control in cell cycle of Xenopus frog egg to realize gene reactions. The proposed improved genetic algorithm can achieve global search with migration and keep the best individual with elitism operation. The generated gene regulatory networks can provide biological researchers for further experiments in yeast and Xenopus frog egg cell cycle.