Amstein Leonie, Ackermann Jörg, Scheidel Jennifer, Fulda Simone, Dikic Ivan, Koch Ina
Molecular Bioinformatics, Institute of Computer Science, Goethe-University Frankfurt am Main, Robert-Mayer-Straße 11-15, Frankfurt am Main, 60325, Germany.
Institute for Experimental Cancer Research in Pediatrics, Goethe-University Frankfurt am Main, Komturstraße 3a, Frankfurt am Main, 60528, Germany.
BMC Syst Biol. 2017 Jul 28;11(1):72. doi: 10.1186/s12918-017-0448-7.
Signal transduction pathways are important cellular processes to maintain the cell's integrity. Their imbalance can cause severe pathologies. As signal transduction pathways feature complex regulations, they form intertwined networks. Mathematical models aim to capture their regulatory logic and allow an unbiased analysis of robustness and vulnerability of the signaling network. Pathway detection is yet a challenge for the analysis of signaling networks in the field of systems biology. A rigorous mathematical formalism is lacking to identify all possible signal flows in a network model.
In this paper, we introduce the concept of Manatee invariants for the analysis of signal transduction networks. We present an algorithm for the characterization of the combinatorial diversity of signal flows, e.g., from signal reception to cellular response. We demonstrate the concept for a small model of the TNFR1-mediated NF- κB signaling pathway. Manatee invariants reveal all possible signal flows in the network. Further, we show the application of Manatee invariants for in silico knockout experiments. Here, we illustrate the biological relevance of the concept.
The proposed mathematical framework reveals the entire variety of signal flows in models of signaling systems, including cyclic regulations. Thereby, Manatee invariants allow for the analysis of robustness and vulnerability of signaling networks. The application to further analyses such as for in silico knockout was shown. The new framework of Manatee invariants contributes to an advanced examination of signaling systems.
信号转导通路是维持细胞完整性的重要细胞过程。它们的失衡会导致严重的病理状况。由于信号转导通路具有复杂的调控机制，它们形成了相互交织的网络。数学模型旨在捕捉其调控逻辑，并允许对信号网络的稳健性和脆弱性进行无偏分析。通路检测仍然是系统生物学领域信号网络分析的一个挑战。目前缺乏一种严格的数学形式来识别网络模型中所有可能的信号流。
在本文中，我们引入了海牛不变量的概念来分析信号转导网络。我们提出了一种算法来表征信号流的组合多样性，例如从信号接收到细胞反应。我们在TNFR1介导的NF-κB信号通路的一个小模型中演示了这个概念。海牛不变量揭示了网络中所有可能的信号流。此外，我们展示了海牛不变量在计算机敲除实验中的应用。在此，我们说明了该概念的生物学相关性。
所提出的数学框架揭示了信号系统模型中所有类型的信号流，包括循环调控。因此，海牛不变量允许对信号网络的稳健性和脆弱性进行分析。展示了其在进一步分析如计算机敲除中的应用。海牛不变量的新框架有助于对信号系统进行深入研究。
