OP-16 Automated and systematic verification and validation increases quality and long-term reuse of models
Presenting Author: Natasa Miskov-Zivanov, University of Pittsburgh
Abstract: Although modeling is an important component of a research pipeline in biology, most often there is no systematic or standardized approach for quality assessment and annotation of models, reducing their trustworthiness and reuse potential. Moreover, most of the model design and documenting steps are still done manually. Creating useful and reliable models of cellular signaling requires thorough and careful information extraction, knowledge assembly, comprehensive model verification and validation, which can take months, sometimes even years. The verification step, assessing whether the model structure is correct by finding support for all its elements and interactions, and the validation step, evaluation of model behavior against experimental observations and data, usually occur iteratively with model expansion before the model can be used to make predictions or explanations. The objective of our work is to develop an architecture that will allow researchers to automatically verify, assess the quality, annotate and expand their models, utilizing available literature and model databases. We have developed several methods and tools to automatically verify models using the information from literature and databases, and to test for contradictions between new knowledge and existing models. Our tools are able to process large amounts of information from literature and compare with models within seconds, a task that would take days to complete manually, and would likely be prone to errors. Outcomes of this work will contribute to increasing the long-term reuse of models and aid computational and systems biology researchers in assembling or selecting models with trusted quality.
