From word models to executable models of signaling networks using automated assembly.
Journal:
Molecular systems biology
Published Date:
Nov 24, 2017
Abstract
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.
Authors
Keywords
Antineoplastic Agents
Cell Line, Tumor
Computer Simulation
DNA Damage
Drug Resistance, Neoplasm
Enzyme Inhibitors
Gene Expression Regulation, Neoplastic
Humans
Indoles
Language
Melanoma
Models, Genetic
Natural Language Processing
Neural Networks, Computer
Proto-Oncogene Proteins B-raf
Proto-Oncogene Proteins p21(ras)
Signal Transduction
Skin Neoplasms
Sulfonamides
Tumor Suppressor Protein p53
Vemurafenib