Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation, and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage, (ii) adaptive drug resistance in BRAF-V600E-mutant melanomas, and (iii) the RAS signaling pathway. The use of natural language makes the task of developing a model more efficient and it increases model transparency, thereby promoting collaboration with the broader biology community.

中文翻译：

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从单词模型到使用自动组装的信令网络的可执行模型。

单词模型（分子机制的自然语言描述）是生物医学中口头和书面交流的通行手段，但在预测复杂生物网络的行为方面用途有限。我们提出了一种使用自动组装直接从自然语言构建计算模型的方法。用自然语言处理算法读取用简单英语描述的分子机制，将其转换为中间表示形式，并组装为可执行或网络模型。我们已经在集成网络和动态推理组装器（INDRA）中实现了该方法，该集成器利用了现有的自然语言处理系统以及Pathway Commons和其他在线资源中的路径信息。我们证明了使用INDRA和自然语言来模拟三个范围不断扩大的生物学过程：（i）对DNA损伤作出反应的p53动力学；（ii）BRAF-V600E突变型黑色素瘤中的自适应药物耐药性；以及（iii）RAS信号传导途径。自然语言的使用使开发模型的任务更加有效，并且增加了模型的透明度，从而促进了与更广泛的生物学界的合作。