Detecting and removing inconsistencies between experimental data and signaling network topologies using integer linear programming on interaction graphs.
Detecting and removing inconsistencies between experimental data and signaling network topologies using integer linear programming on interaction graphs.
复制标题
在交互图上使用整数线性编程来检测和消除实验数据和信号网络拓扑之间的不一致。
DOI：
10.1371/journal.pcbi.1003204
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发表时间：
2013
影响因子：
4.3
通讯作者：
Klamt S
中科院分区：
文献类型：
--
作者：
Melas IN;Samaga R;Alexopoulos LG;Klamt S
交叉引用实验数据与我们目前的知识信号网络拓扑结构是细胞信号转导网络的数学建模的一个中心目标。我们提出了一种新的方法，用于信令网络的数据驱动的询问和训练。虽然大多数已发表的信号网络推理方法都是在贝叶斯、布尔或ODE模型上进行的，但我们的方法在交互图上使用整数线性规划（ILP）来编码对节点定性行为的约束。这些约束是由网络拓扑结构构成的，它们作为ILP的公式使我们能够预测给定刺激下节点激活水平的可能定性变化（向上、向下、无影响）。我们提供了四个基本操作来检测和消除测量和预测行为之间的不一致：（i）为刺激-反应实验中测量的信号节点的反应找到拓扑一致的解释（ii）确定需要被校正以使不一致的场景一致的最小节点集合;（iii）确定给定网络拓扑的最佳子图，其可以最好地反映来自一组实验场景的测量;（iv）找到可能丢失的边缘，其将最大地改善图相对于一组实验场景的一致性。我们证明了所提出的方法的适用性，通过询问一个手动策划的互动图模型的EGFR/ErbB信号对高通量磷酸化蛋白质组学数据在原代肝细胞中测量的库。我们的方法检测可能在肝细胞中无活性的相互作用，并为新的相互作用提供建议，如果包括在内，将显着提高拟合优度。我们的框架是高度灵活的，基础模型只需要容易获得的生物学知识。所有相关算法都在免费提供的工具箱SigNetTrainer中实现，使其成为各种应用程序的吸引力方法。细胞信号转导是由信号蛋白的通信网络协调的，通常描绘在信号通路图上。然而，每种细胞类型可能具有不同的信号通路变体，并且在疾病状态下接线图通常会改变。因此，基于实验数据识别真正活跃的信号传导拓扑结构是细胞信号传导系统生物学中的一个关键挑战。我们提出了一个新的框架，用于训练信令网络的基础上交互图（IG）。与复杂的建模形式相反，IG仅捕获组件之间的已知正边缘和负边缘。然而，当扰动网络时，这些基本信息已经对节点的可能定性行为设置了严格的约束。我们的方法使用线性规划来编码这些约束条件，并预测可能的变化（下，中性，上）的激活水平的参与者为一个给定的实验。基于这个公式，我们开发了几种算法，用于检测和消除测量和网络拓扑结构之间的不一致。通过肝细胞中的EGFR/ErbB信号传导证明，我们的方法提供了关于边缘的直接结论，这些边缘相对于经典途径图可能是无活性的或缺失的。这些信息推动了在正常和病理表型下信号网络拓扑结构的进一步阐明。
Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsistencies between measurements and predicted behavior: (i) find a topology-consistent explanation for responses of signaling nodes measured in a stimulus-response experiment (if none exists, find the closest explanation); (ii) determine a minimal set of nodes that need to be corrected to make an inconsistent scenario consistent; (iii) determine the optimal subgraph of the given network topology which can best reflect measurements from a set of experimental scenarios; (iv) find possibly missing edges that would improve the consistency of the graph with respect to a set of experimental scenarios the most. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary hepatocytes. Our methods detect interactions that are likely to be inactive in hepatocytes and provide suggestions for new interactions that, if included, would significantly improve the goodness of fit. Our framework is highly flexible and the underlying model requires only easily accessible biological knowledge. All related algorithms were implemented in a freely available toolbox SigNetTrainer making it an appealing approach for various applications. Cellular signal transduction is orchestrated by communication networks of signaling proteins commonly depicted on signaling pathway maps. However, each cell type may have distinct variants of signaling pathways, and wiring diagrams are often altered in disease states. The identification of truly active signaling topologies based on experimental data is therefore one key challenge in systems biology of cellular signaling. We present a new framework for training signaling networks based on interaction graphs (IG). In contrast to complex modeling formalisms, IG capture merely the known positive and negative edges between the components. This basic information, however, already sets hard constraints on the possible qualitative behaviors of the nodes when perturbing the network. Our approach uses Integer Linear Programming to encode these constraints and to predict the possible changes (down, neutral, up) of the activation levels of the involved players for a given experiment. Based on this formulation we developed several algorithms for detecting and removing inconsistencies between measurements and network topology. Demonstrated by EGFR/ErbB signaling in hepatocytes, our approach delivers direct conclusions on edges that are likely inactive or missing relative to canonical pathway maps. Such information drives the further elucidation of signaling network topologies under normal and pathological phenotypes.
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影响因子：
4.3
作者：
Aldridge BB;Saez-Rodriguez J;Muhlich JL;Sorger PK;Lauffenburger DA
通讯作者：
Lauffenburger DA
影响因子：
1.4
作者：
Gebser, Martin;Schaub, Torsten;Veber, Philippe
通讯作者：
Veber, Philippe
影响因子：
4.6
作者：
Qiu, Peng;Plevritis, Sylvia K.
通讯作者：
Plevritis, Sylvia K.
影响因子：
--
作者：
Melas, Ioannis N.;Mitsos, Alexander;Alexopoulos, Leonidas G.
通讯作者：
Alexopoulos, Leonidas G.
影响因子：
4.3
作者：
Samaga R;Saez-Rodriguez J;Alexopoulos LG;Sorger PK;Klamt S
通讯作者：
Klamt S