FIDDLE: Efficient Assembly of Networks That Satisfy Desired Behavior

Background: Due to the complexity and redundancy of biological systems, computational models are difficult and laborious to create and update. Therefore, machine reading and automated model assembly are of great interest to computational and systems biologists. Here, we describe FIDDLE (Finding Interactions using Diagram Driven modeL Extension), the tool that we built with the goal to automatically assemble or extend models with the knowledge extracted from published literature. The two main methods developed as part of FIDDLE are called Breadth First Addition (BFA) and Depth First Addition (DFA), and they are based on network search algorithms. To assess the advantages and limitations of BFA and DFA, we applied them on Erdös-Rényi random networks (ER) and Barabási-Albert scale-free networks (BA) as models of biological networks. Starting with several different baseline models and different sets of candidate edges, we conducted a comprehensive evaluation of the BFA and DFA algorithms and their ability to either reconstruct a specified network structure or assemble models that reproduce the same behavior as defined golden models.Results: When BFA and DFA are applied to random networks, we are able to show that model extension with BFA is more specific and DFA is a more sensitive approach. When applied to scale-free networks, both BFA and DFA demonstrated very limited success. Conclusion: Results suggest that BFA is a better method for assembling abundant but mixed true and false information, while DFA will better assemble fewer and more truthful interactions. The limited success with scale-free networks suggests a fundamental difficulty with automatically creating biological models due to the inherent redundancy and complexity of biological networks. These methods represent an early attempt and a novel approach to truly autonomous model creation or extension given different types of knowledge. The source code is available at: https://bitbucket.org/biodesignlab/fiddle/.