抗生物質の作用メカニズムを明らかにするためのホワイトボックスの機械学習アプローチ
Jason H Yang1, Sarah N Wright1, Meagan Hamblin2
1Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Cell
|May 14, 2019
まとめ
生物学的スクリーニング,ネットワークモデリング,機械学習を組み合わせた 新しいアプローチを開発し 生物学的信号と表型の間の因果関係を明らかにし 抗生物質の有効性のメカニズムを明らかにしました
科学分野:
- 生物化学
- システム生物学
- 機械学習
背景:
- 現在の機械学習方法は 生物学的信号と現象型を 関連付けるのに優れているが 因果関係を確立することはできません
- 薬の発見と有効性研究を進めるには 原因メカニズムを理解することが重要です
研究 の 目的:
- 抗生物質の有効性の基礎となる因果メカニズムを特定するための統合的アプローチを開発し,適用する.
- 抗生物質に対する細菌の反応における代謝状態の役割を解明する.
主な方法:
- バイオケミカルスクリーニング,ゲノムスケールの代謝ネットワークモデリング,機械学習を統合した"ホワイトボックス"アプローチを開発した.
- エシェリキア・コリ菌における抗菌剤に対する代謝物の対スクリーニングを行った.
- メタボリックネットワークモデルとシミュレーションに対する回帰スクリーニングデータを用いてシミュレーションされた代謝状態.
主要な成果:
- 抗生物質の致死性の重要な要因として ピューリン生物合成を特定した.
- 抗生物質によるアデニン制限がATPの需要と中央の炭素代謝を高めることを示した.
- 代謝活動が増加すると 抗生物質の殺菌効果が強化されます
結論:
- 統合されたネットワークモデリングと機械学習のアプローチは,複雑な因果メカニズムを特定することができます.
- 代謝障害,特にアデニンの制限は,抗生物質の有効性に大きな影響を与えます.
- この枠組みは 薬物への反応を理解し 予測する道を示しています
キーワード:
ATP についてLC-MS/MS についてNADPH:NADP ((+) の比率についてアデニラートエネルギー抗生物質生化学スクリーニング機械学習メタボリズムネットワークモデリングピューリン生物合成さらに関連する動画
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