JP5064656B2 - 実施反応経路を同定するための方法およびシステム - Google Patents
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Description
本発明は、一般的に、コンピューター内でのモデル生物体の構築、より具体的には、実施反応経路を特定する、および実際の生物体の最適のコンピューター内モデルの創出のための方法ならびにシステムに関する。
本発明は、生物系の実施反応経路を同定する方法を提供する。方法は以下の段階からなる:(a)その生物系を表す反応ネットワークを通しての1セットの体系的反応経路を提供する段階;(b)その生物系の1セットの現象論的反応経路を提供する段階、および(c)そのセットの体系的反応経路をそのセットの現象論的反応経路と比較する段階であって、それらのセットに共通した経路がその生物系の実施反応経路である、段階。
本発明は、生物系の1セットの構成要素の相互作用、統合および協調を決定するための方法ならびにシステムを提供する。本発明は、このように、ゲノム尺度において再構築された生化学反応ネットワークを迅速かつ体系的に特定するため、ならびに構成要素の活性およびそれらの相互作用を特定の表現型または生理学的状態へ関連づけるために用いられうる。どの構成要素が特定の条件下において操作可能であるかを理解することは、生きている細胞へ望ましい機能を操作する、うまく機能していない回路を修復する、および細胞の環境の適切な操作により内因性回路を調節する改良された方法を可能にする。さらになお、生化学的ネットワークを特徴付けるための迅速な方法は、最小限の実験的努力で事実上特徴付けられていない生物系の特徴付けを可能にする。
実施極度経路を同定するための大腸菌コア代謝ネットワークについての1セットの現象論的流束分布の分解
この実施例は、1セットの現象論的経路(流束分布)がどのようにして基本モードへ分解されうるかを示し、これらのモードは、代謝反応ネットワーク(大腸菌コア代謝)の実施反応経路を同定するために1セットの体系的経路(極度経路)と比較されうる。
A=UεVT
生理学的に関連した流束分布に対応するヒト赤血球極度経路の同定
この実施例は、代謝反応ネットワーク(ヒト赤血球代謝)の基本的制御モードを同定するために、動力学的モデルにより生じた1セットの現象論的経路(流束分布)がどのようにして、1セットの体系的経路(極度経路)のモード分解と比較されうるかを示している。
赤血球一塩基多型の表現型結果のコンピューター内での評価
以下の実施例は、病理学的データにより定義された現象論的経路の解析への記載された方法の適用を例証する。
既知の制御ネットワーク構造とトランスクリプトミクスデータの間の整合性
以下の実施例は、既知の制御ネットワーク構造を、これらの構造を大規模遺伝子発現データセットと調和させることにより、確証かつ展開するための記載された方法の使用を例証する。
制御ネットワークモデルの繰り返し改善
この実施例の目的は、制御ネットワーク同定、向上、および制御または組み合わされた制御/代謝のモデルにおける制御状態の同定のために、記載された方法がどのように用いられうるかを例証することである。
体系的モデル向上アルゴリズムによる制御ネットワークモデルの繰り返し改善
この実施例の目的は、生物学的過程の最良のモデルに迅速に収束させるための、上で記載され、図2Bで描かれた体系的アプローチの重要性を例証することである。仮説の制御ネットワークが、ここでは例として用いられているが、この過程は、代謝ネットワーク、シグナル伝達経路、タンパク質相互作用ネットワークおよび任意の他の生物学的過程に等しく適用できる。
アルファ円錐方法を用いる極度経路への定常状態流束分布の分解
この実施例は、生物系において実施経路を同定するために、どのようにして、任意の定常状態現象論的流束分布が、体系的経路(ここでは、極度経路)へと理にかなった様式で分解されうるかを示している。アルファ円錐分解方法は、所定の流束分布について重みづけする体系的経路の範囲を同定すること、および必要とされる最小限セットの体系的経路を定義することが、現象論的経路を記述することを可能にする。これらの経路の可能な重みづけの範囲と共のこの最小限セットの体系的経路は、生物系の実施経路を定義する。
βは、経路使用に対応するブール変数のベクトルであり、αは、経路重みづけのベクトルである。解は、望ましい現象論的流束分布の分解を得るために最小数の経路が用いられるような1セットのアルファ重みづけである。
Claims (7)
- 現実の生物系のコンピューター内モデルの実施反応経路を同定する方法であって、該モデルは、代謝産物の関係を規定する複数の代謝反応を含み、前記方法の段階は、入力手段、処理装置、および出力手段を含むコンピューター上で行われ、前記方法は、
(a)前記入力手段を介して、前記コンピューターに、前記生物系のコンピューター内モデルにおける代謝反応を表す反応ネットワークを通して体系的反応経路のセットを含むデータを提供する段階であって、該データが、現実の生物系のコンピューター内モデルの計算された代謝産物の関係を表す体系的反応ベクトルのセットを含み、ここで、該体系的反応ベクトルのセットは反応ネットワークの極度経路のセットを表すベクトルを含み、該ベクトルは、反応ネットワークにより達成可能なすべての可能性のある定常状態流束分布の限界を定める段階と、
(b)前記入力手段を介して、前記コンピューターに、前記現実の生物系における現実の代謝反応の現象論的反応経路のセットを含むデータを提供する段階であって、該データが、現実の生物系における代謝産物の測定された流束を表す現象論的反応ベクトルのセットを含み、ここで、該現象論的反応ベクトルのセットは前記現象論的反応経路のセットから特異値分解(SVD)解析により分解された基本モードを表すベクトルのセットを含む段階と、
(c)前記処理装置を介して、段階(a)において前記入力手段を介して供された前記体系的反応ベクトルのセットと、段階(b)において前記入力手段を介して供された前記現象論的反応ベクトルのセットと、の比較を行う段階であって、前記処理装置が、該比較に基づき、現象論的反応ベクトルと一致する体系的反応ベクトルを同定し、ここで、該同定されたベクトルは、前記体系的反応経路のセットおよび前記現象論的反応経路のセットに共通した経路を表し、共通した経路が前記現実の生物系の実施反応経路である段階と、
(d)前記出力手段を介して、前記同定されたベクトルを表し、これにより段階(c)で同定した前記実施反応経路を表す前記処理装置からの出力をユーザーに提供し、それにより前記生物系のコンピューター内モデルの実施反応経路を同定する段階と、
を含む方法。 - 現実の生物系が原核細胞またはその生物学的経路である、請求項1記載の方法。
- 原核生物が大腸菌(E. coli)、枯草菌(B. subtilis)、インフルエンザ菌(H. influenzae)およびH.ピロリ (H. pylori)からなる群より選択される、請求項2記載の方法。
- 生物学的経路が代謝である、請求項2記載の方法。
- 現実の生物系が真核細胞またはその生物学的経路である、請求項1記載の方法。
- 真核生物がS. セレビシエ(S. cerevisiae)およびヒト(H. sapiens)からなる群より選択される、請求項5記載の方法。
- 生物学的経路が代謝である、請求項5記載の方法。
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