遺伝子調節DNAの進化,進化可能性と工学
Eeshit Dhaval Vaishnav1,2, Carl G de Boer3,4, Jennifer Molinet5,6
1Massachusetts Institute of Technology, Cambridge, MA, USA. edv@mit.edu.
Nature
|March 10, 2022
まとめ
科学者はDNA配列を遺伝子発現にマッピングするために ディープニューラルネットワークモデルを開発し 制御進化の原理を明らかにしました このアプローチは,規制配列を設計し,進化の制約を理解するのに役立ちます.
科学分野:
- ゲノミクス
- 進化生物学
- システム生物学
背景:
- 非コーディングDNAの調節配列の突然変異は遺伝子発現,生物の特徴,体調に大きく影響する.
- DNA配列をフィットネス・ランドスケープにマッピングすることは 進化を理解するために不可欠ですが, 広大な配列空間のために挑戦的です.
研究 の 目的:
- 規則的なフィットネス環境を捉えるためのシーケンス・トゥ・エクスプレッション・モデルを開発する.
- 規制の進化を制御する原理を解読する.
- 表現工学のための規制配列の設計を可能にします.
主な方法:
- ランダムに採取された数百万の酵母プロモーターDNA配列とその表現レベルを測定した.
- DNA配列から遺伝子発現を予測する ディープニューラルネットワークモデルを開発した
- 異なる進化的体制 (ドリフト,選択-変異) の下での表現の逸脱を研究するために適用されたモデル.
主要な成果:
- シーケンスから表現モデルの優れた予測性能を達成し,広大なシーケンス空間での一般化を可能にしました.
- 規制の進化は急速であり,利益の減少に伴い,環境目標の対立によって制約されています.
- 安定する選択は規制の複雑さを緩和し,規制の変異効果の大きさはパワー法則に従う.
結論:
- 開発されたモデルは,規制配列と遺伝子発現の設計のための一般的な枠組みを提供します.
- この研究は,規制の進化の動態,変異の強度,そして進化可能性についての洞察を提供します.
- 選択シグネチャーを検出し,収束する規制の進化を発見するためのアプローチが提示されました.
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