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.

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 for modeling makes routine tasks more efficient for modeling practitioners and increases the accessibility and transparency of models for the broader biology community. Keywords: computational modeling, natural language processing, signaling pathwaysRunning title: From word models to executable models Standfirst text: INDRA uses natural language processing systems to read descriptions of molecular mechanisms and assembles them into executable models. Highlights:• INDRA decouples the curation of knowledge as word models from model implementation • INDRA is connected to multiple natural language processing systems and can draw on information from curated databases • INDRA can assemble dynamical models in rule-based and reaction network formalisms, as well as Boolean networks and visualization formats • We used INDRA to build models of p53 dynamics, resistance to targeted inhibitors of BRAF in melanoma, and the Ras signaling pathway from natural language . CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under a

Abstract-We describe CARDIAC, a prototype for an intelligent conversational assistant that provides health monitoring for chronic heart failure patients. CARDIAC supports user initiative through its ability to understand natural language and connect it to intention recognition. The spoken language interface allows patients to interact with CARDIAC without special training. We present speech recognition results obtained during an evaluation with fourteen chronic heart failure patients.

Complex mechanisms, such as cell-signaling pathways, consist of many highly interconnected components, yet they are often described in disconnected fragmentary ways. The goal of DRUM (Deep Reader for Understanding Mechanisms) is to develop a system that can read papers and combine results of individual studies into a comprehensive explanatory model. A first step is to automatically extract relevant events and event relationships from the literature. This paper describes initial steps in extending an existing general deep language understanding system, TRIPS, to read biomedical papers. In a preliminary evaluation, our system was the best performing system among the participants, achieving results close to human expert performance. These results suggested that our system is viable for complex event extraction and, ultimately, understanding complex systems and mechanisms.

We describe design and prototyping efforts for a Personal Health Management Assistant for heart failure patients as part of Project HealthDesign. An assistant is more than simply an application. An assistant understands what its users need to do, interacts naturally with them, reacts to what they say and do, and is proactive in helping them manage their health. In this project, we focused on heart failure, which is not only a prevalent and economically significant disease, but also one that is very amenable to self-care. Working with patients, and building on our prior experience with conversational assistants, we designed and developed a prototype system that helps heart failure patients record objective and subjective observations using spoken natural language conversation. Our experience suggests that it is feasible to build such systems and that patients would use them. The system is designed to support rapid application to other self-care settings.