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From word models to executable models of signaling networks using automated assembly
Benjamin M Gyori1, John A Bachman1, Kartik Subramanian1
1Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.
Molecular Systems Biology
|November 28, 2017
Summary
This study introduces a novel method for building computational biological models directly from natural language descriptions. This approach enhances efficiency and transparency in modeling complex biological networks.
Area of Science:
- Systems Biology
- Computational Biology
- Bioinformatics
Background:
- Biomedical research relies on natural language descriptions of molecular mechanisms.
- Current word models have limitations in predicting complex biological network behavior.
Purpose of the Study:
- To develop a computational approach for building models directly from natural language.
- To increase efficiency and transparency in biological network modeling.
Main Methods:
- Utilizing natural language processing (NLP) algorithms to interpret English descriptions of molecular mechanisms.
- Converting natural language into an intermediate representation for automated assembly into executable or network models.
- Implementing the Integrated Network and Dynamical Reasoning Assembler (INDRA) integrating NLP with pathway databases.
Main Results:
- Demonstrated INDRA's capability to model diverse biological processes, including p53 dynamics, drug resistance in melanoma, and RAS signaling.
- Showcased the efficiency and transparency gains of using natural language for model development.
Conclusions:
- Natural language processing offers a powerful avenue for constructing computational biological models.
- INDRA facilitates the creation of transparent and collaborative biological models, advancing systems biology research.
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