Causal interactions from proteomic profiles: Molecular data meet pathway knowledge.
Causal interactions from proteomic profiles: Molecular data meet pathway knowledge.
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DOI：
10.1016/j.patter.2021.100257
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发表时间：
2021-06-11
期刊：
影响因子：
--
通讯作者：
Demir E
中科院分区：
文献类型：
--
作者：
Babur Ö;Luna A;Korkut A;Durupinar F;Siper MC;Dogrusoz U;Vaca Jacome AS;Peckner R;Christianson KE;Jaffe JD;Spellman PT;Aslan JE;Sander C;Demir E
我们提出了一种计算方法来推断因果机制，在细胞生物学中的生化反应知识库中捕获的先验知识的背景下，通过分析高通量蛋白质组学谱的变化。该方法模仿了生物学家使用先验知识解释数据变化的传统方法，但在数十万个反应的规模下进行。这是如何自动化科学推理过程的一个具体例子，并说明了通过逻辑编程将实验数据映射到先验知识的能力。所确定的机制可以解释实验和生理扰动如何在反应网络中传播，影响细胞反应及其表型后果。因果通路分析是一种强大而灵活的发现工具，适用于各种细胞分析数据类型和生物学问题。自动因果推理工具，以及源代码，可以在http://causalpath.org免费获得。CausePath从蛋白质组学图谱构建机制模型它将生物途径模型与分子测量相结合它支持翻译后修饰的逻辑推理网络服务器，免费软件和源代码可用生物有机体的分子图谱为我们提供了大量关于细胞差异的信息，但将其转换为机制见解仍然是一项非常具有挑战性的任务。一个突出的方法是将新的测量与科学文献中描述的机械知识相结合，并建立一个由两者支持的模型。虽然这可以通过多种方式来实现，但熟练的方法将详细使用文献知识，并遵循高标准的逻辑推理，同时整合已知和新的。本文描述了一种方法，利用人体生物学途径的细节，以确定可能的因果关系内的变化对。该方法自动将比较蛋白质组学和其他分子谱转换为解释谱如何形成的差异活性机制关系的假设。CausalPath将详细的生物学途径与蛋白质组学和其他分子谱整合在一起，以生成解释观察到的变化如何相关的机制模型。它适用于广泛的背景和各种实验类型。该方法可以在caitypath.org上访问。
We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org. CausalPath builds mechanistic models from proteomic profiles It integrates biological pathway models with molecular measurements It supports logical reasoning with post-translational modifications A web server, free software, and a source code are available Molecular profiling of biological organisms provides us with a great amount of information on cellular differences, but converting it to mechanistic insights is still a very challenging task. A prominent approach is to integrate new measurements with the mechanistic knowledge described in the scientific literature and build a model that is supported by both. Although this can be done in many ways, an adept approach will use the literature knowledge in detail and follow high standards of logical reasoning while integrating the known and the new. This article describes an approach that utilizes the details in human biological pathways to identify pairs of changes with a likely cause-effect relation within. The approach automatically converts comparative proteomic and other molecular profiles into hypotheses of differentially active mechanistic relations that explain how the profiles came to be. CausalPath integrates detailed biological pathways with proteomic and other molecular profiles to generate mechanistic models explaining how the observed changes are related. It is applicable to a wide range of contexts and a variety of experiment types. The method can be accessed at causalpath.org.
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