Data integration in logic-based models of biological mechanisms. Issue 28 (December 2021) - Hall, Benjamin A.; Niarakis, Anna
Abstract: Discrete, logic-based models are increasingly used to describe biological mechanisms. Initially introduced to study gene regulation, these models evolved to cover various molecular mechanisms, such as signaling, transcription factor cooperativity, and even metabolic processes. The abstract nature and amenability of discrete models to robust mathematical analyses make them appropriate for addressing a wide range of complex biological problems. Recent technological breakthroughs have generated a wealth of high-throughput data. Novel, literature-based representations of biological processes and emerging algorithms offer new opportunities for model construction. Here, we review up-to-date efforts to address challenging biological questions by incorporating omic data into logic-based models and discuss critical difficulties in constructing and analyzing integrative, large-scale, logic-based models of biological mechanisms. Highlights: Logic-based models are powerful tools for deciphering complex biological processes. High-throughput data can be used to enrich, validate, contextualize, and infer logic-based models. Efficient data integration and rigorous formal methods for large-scale analysis are major challenges in systems biology.