Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data.
Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data.
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DOI：
10.1186/s12918-018-0551-4
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发表时间：
2018-03-21
影响因子：
--
通讯作者：
Guziolowski C
中科院分区：
文献类型：
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作者：
Miannay B;Minvielle S;Magrangeas F;Guziolowski C
基因表达谱（GEPs）和大规模生物网络的整合是一个正在广泛探索的课题。现有的方法是基于网络之间的距离措施显着测量的物种。其中只有一小部分包括生物网络中存在的方向性和潜在逻辑。在这项研究中，我们通过考虑网络逻辑来处理GEP网络整合问题，但是我们的方法不需要根据基因表达水平进行事先的物种选择。我们首先使用逻辑编程来建模生物网络，表示其底层逻辑。该模型指向可达到的网络离散状态，其最大化分子物种活性或非活性可能状态与根据其激活剂或抑制剂控制作用的途径反应的方向性之间的和谐概念。只有这样，我们才能用GEP来对抗这些网络状态。从这种对抗独立的图形组件派生，他们每个人都涉及到一个固定的和最佳的分配活动或非活动状态。这些组件使我们能够将一个大规模的网络分解成子图，它们的分子物种状态分配与相同的GEP相比具有不同程度的相似性。我们应用我们的方法来研究来自NCI-PID路径交互数据库的子图的可能状态集。该图将多发性骨髓瘤（MM）基因与这种血癌的已知受体联系起来。我们发现NCI-PID MM图有15个独立的分量，当面对611个MM GEP时，我们发现1个分量更具体地代表癌症和健康概况之间的差异。
The integration of gene expression profiles (GEPs) and large-scale biological networks derived from pathways databases is a subject which is being widely explored. Existing methods are based on network distance measures among significantly measured species. Only a small number of them include the directionality and underlying logic existing in biological networks. In this study we approach the GEP-networks integration problem by considering the network logic, however our approach does not require a prior species selection according to their gene expression level. We start by modeling the biological network representing its underlying logic using Logic Programming. This model points to reachable network discrete states that maximize a notion of harmony between the molecular species active or inactive possible states and the directionality of the pathways reactions according to their activator or inhibitor control role. Only then, we confront these network states with the GEP. From this confrontation independent graph components are derived, each of them related to a fixed and optimal assignment of active or inactive states. These components allow us to decompose a large-scale network into subgraphs and their molecular species state assignments have different degrees of similarity when compared to the same GEP. We apply our method to study the set of possible states derived from a subgraph from the NCI-PID Pathway Interaction Database. This graph links Multiple Myeloma (MM) genes to known receptors for this blood cancer. We discover that the NCI-PID MM graph had 15 independent components, and when confronted to 611 MM GEPs, we find 1 component as being more specific to represent the difference between cancer and healthy profiles.
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DOI：
10.1093/bioinformatics/btv305
发表时间：
2015-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Nicolle R;Radvanyi F;Elati M
通讯作者：
Elati M
影响因子：
14.9
作者：
Schaefer, Carl F.;Anthony, Kira;Buetow, Kenneth H.
通讯作者：
Buetow, Kenneth H.
影响因子：
5.7
作者：
Ates O
通讯作者：
Ates O
影响因子：
3
作者：
Thiele, Sven;Cerone, Luca;Klamt, Steffen
通讯作者：
Klamt, Steffen
DOI：
10.1038/nrg3552
发表时间：
2013-10
期刊：
Nature reviews. Genetics
影响因子：
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作者：
通讯作者：
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