Automating Knowledge-Driven Model Recommendation: Methodology, Evaluation, and Key Challenges

TL;DR

This paper presents a methodology for automating the assembly and extension of biological models from repositories, highlighting key challenges and providing software tools for further research.

Contribution

It introduces an automated, iterative approach to model assembly, discusses inherent difficulties, and releases software to facilitate future exploration.

Findings

Automated assembly can generate models from biological repositories.

Key challenges include contextless assembly difficulties.

Software release enables further research in model automation.

Abstract

There is significant interest in using existing repositories of biological entities, relationships, and models to automate biological model assembly and extension. Current methods aggregate human-curated biological information into executable, simulatable models, but these models do not resemble human curated models and do not recapitulate experimental results. Here, we outline the process of automated model assembly and extension, while demonstrating it on both synthetic models and human-curated models of biological signaling networks. We begin with an iterative, greedy, and combinatoric approach to automated assembly and demonstrate the key difficulties inherent to contextless assembly. We publicly release the software used in this paper to enable further exploration of this problem.