As the genome wide interrogation of multiple molecular layers is becoming a common practice in basic and translational research, and as the methods for making sense of genome-wide datasets are becoming more efficient at generating knowledge while however getting in complexity, it is important to provide the community with validated concepts and working models that foster actionable knowledge in HD research. systems biology may provide unique insights into questions including i) understanding how the disease might work at the system level and how experimental models might compare at the biological level and ii) what might be the most promising genes to consider for target prosecution, disease modifier/marker discovery and drug profiling/repositioning in HD. Graph theory and network-based analysis provide powerful ways to address these questions thanks to their ability to capture gene essentiality across multiple contexts. Although the variety of questions of interest across a large community of end-users makes it virtually impossible to develop an all-in-one solution for data analysis and knowledge exploitation, interdisciplinarity and research work proximity may ensure coherence between mathematics and biology in answering specific questions on HD. I will provide an overview of network-based approaches using examples showing how rules, patterns and signatures can be identified and exploited for precise hypothesis generation in basic research as well as decision making in translational and discovery research.