Building a model from natural language with INDRA

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Presentation transcript:

Building a model from natural language with INDRA Building a model from natural language with INDRA The architecture of INDRA consists of three layers of modules (1–3). In layer (1), interfaces collect mechanisms from natural language processing systems (e.g., TRIPS Interface) and pathway databases (e.g., Pathway Commons Interface) and Processors (e.g., TRIPS Processor, BioPAX Processor) extract INDRA Statements from their outputs. Statements, the internal representation in INDRA, constitute layer (2). In layer (3), INDRA Statements are assembled into various model formats by Assembler modules (e.g., PySB Assembler, Graph Assembler).A Python script is used to assemble and simulate a model from the text “MEK1 phosphorylates ERK2 at threonine 185 and tyrosine 187”. The process_text method of INDRA's TRIPS Processor is called to send the text to the TRIPS NLP system (1) and then process the output of TRIPS to construct INDRA Statements (2). Then, a PySB Assembler is constructed, the Statements are added to it, and an executable model is assembled using the PySB Assembler's make_model method with a “two‐step” policy (3). Finally, the model is simulated for 300 s using PySB's odesolve function.User input, INDRA modules, and external tools form a sequence of events to turn a natural language sentence into a model and simulation. The natural language description from the user is passed to INDRA's TRIPS Interface, which sends the text to TRIPS (1). The TRIPS system processes the text and creates an Extraction Knowledge Base graph (Results column; yellow box). INDRA receives the results from TRIPS and constructs two INDRA Statements from it, one for each phosphorylation event (Results column), which are returned to the user (2). The user then instantiates a PySB Assembler and instructs it to assemble an executable model (3) from the given INDRA Statements (a schematic biochemical reaction network shown in Results column). Finally, the user calls an ODE solver via PySB's odesolve function to simulate the model for 300 s (simulation output shown in Results column). Benjamin M Gyori et al. Mol Syst Biol 2017;13:954 © as stated in the article, figure or figure legend