ディープ・ジェネラティブ・アンド・ダイナミック・エボリューション・アルゴリズムに基づくデ・ノボ・プロモーター設計方法

¹Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, 111 Jiulong Road, Hefei 230601 Anhui, China.

|August 28, 2025

まとめ

調整可能な遺伝子発現レベルを持つ合成プロモーターを設計するための新しい方法である PromoDGDEを開発しましたこのアプローチは,E. coliとS. cerevisiaeの機能的プロモーターを作成することによって,代謝工学と遺伝子治療を強化します.

科学分野:

合成生物学
計算生物学
分子生物学

背景:

プロモーターは遺伝子発現の制御に不可欠であり,代謝工学と遺伝子治療に不可欠です.
現在のプロモーター設計方法は,しばしば高い表現を優先し,調整可能な規制強度の必要性を無視します.

研究の目的:

特定の表現レベルを持つ合成プロモーターを設計するための新しい深層学習フレームワークであるPromoDGDEを導入する.
この方法がEscherichia coliとSaccharomyces cerevisiaeに有効であることを実証する.

主な方法:

自然プロモーター配列の特徴を学習し,新しい候補を生成するためにDiffusion-GANを使用します.
合成プロモーター配列のダイナミック最適化のための強化学習と進化アルゴリズムを使用する.

主要な成果:

PromoDGDEはシリコンで既存の方法を上回り,望ましい機能を持つ生物学的に重要なプロモーターを生成しました.
合成プロモーターの発現活性が確認され,予想される規制効果が60%以上達成された.

結論:

PromoDGDEは合成プロモーターの設計に有効で柔軟なソリューションを提供しており厳格な規制規制が適用されます
この方法は,代謝経路の構築と遺伝子治療を含む合成生物学におけるアプリケーションの進歩に重要な意味を持っています.

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