Dynamic system explanation

Abstract

The large amount of knowledge contained in the scientific literature can be mined using natural language processing and utilized to automatically construct models of complex networks in order to obtain a greater understanding of complex systems. In this paper, we describe the Dynamic System Explanation (DySE) framework, which configures hybrid models and executes simulations over time, relying on a granular computing approach and a range of different element update functions. A standardized tabular format assembles the collected knowledge into networks for parameterization. The Discrete Stochastic Heterogeneous (DiSH) simulator outputs trajectories of state changes for all model elements, thus providing a means for running thousands of in silico scenarios in seconds. Trajectories are also analyzed using statistical model checking to verify against known or desired system properties, determined from text or numerical data. DySE can automatically extend models when additional knowledge is available, and model extension is integrated with model checking to test the validity of additional interactions and dynamics, and

FLUTE: Fast and reliable knowledge retrieval from biomedical literature

“…Through manual curation of machine reading output, we identified four major types of errors in the extracted interactions, namely ambiguous or misconstrued sentences (Omission error), interactions where one or both elements are incorrectly grounded (Grounding error) and interactions that have opposite directionality (Direction error) or opposite effect (Sign error) ( 7 ). In the case of Omission error, the reader denotes a relationship between two elements that does not exist in the evidence statement, while in the Grounding error, the reader was unable to match the elements in the interaction to the correct IDs.…”

“…While the direction and the sign of interaction are both output by INDRA, there is no indication of whether an interaction is direct or indirect. On the other hand, the Dynamic System Explanation (DySE) framework ( 7 ) uses both direct and indirect interactions obtained by machine reading to automatically assemble executable models at different levels of abstraction. DySE conducts automated model testing before using models to explain systems, predict system behavior, or guide interventions; however, the accuracy or confidence in its output depends on the correctness or confidence that we have in the automatically extracted interactions.…”