遺伝子制御ネットワークからの可逆的および不可逆的単一細胞状態遷移の解剖
Daniel A Ramirez1,2, Mingyang Lu3,4
1Center for Theoretical Biological Physics, Northeastern University, Boston, MA, 02115, USA.
Molecular systems biology
|February 9, 2026
まとめ
遺伝子発現データを使用して細胞状態遷移を予測するための新しい計算方法、STICCC(State Transition Inference using Cross-cell Correlations)を開発しました。STICCCは、遺伝子制御ネットワークがこれらの動的な細胞変化をどのように駆動するかを明らかにします。
科学分野:
- 計算生物学
- システム生物学
- ゲノミクス
背景:
- 細胞状態遷移の理解は生物学において重要です。
- 遺伝子制御メカニズムはこれらの動的なプロセスを支配します。
- 擬時間やRNA速度などの既存の方法は限られた洞察しか提供しません。
研究 の 目的:
- 細胞状態遷移を予測するための新しい計算方法を開発すること。
- 単一細胞解像度で可逆的および不可逆的な細胞運命の決定を推論すること。
- 遺伝子制御相互作用とシステムダイナミクスを接続すること。
主な方法:
- クロスセル相関を用いた状態遷移推論(STICCC)法。
- 単一細胞遺伝子発現データと遺伝子制御相互作用を利用します。
- 遺伝子発現の時間遅延を利用して過去と未来の状態を推論します。
主要な成果:
- STICCCは細胞状態遷移を正確に予測します。
- 推論されたベクトル場は、アトラクター領域と不可逆的なフラックスを捉えます。
- ネットワーク相互作用が細胞運命の決定に及ぼす影響を明らかにします。
結論:
- STICCCは、擬時間やRNA速度を超えた補完的な洞察を提供します。
- この方法は、細胞状態遷移における遺伝子制御の理解を深めます。
- STICCCは、動的な生物学的システムを研究するための貴重なツールです。
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