Bridging the gap between systems biology and medicine

TLDR

Systems biology has advanced, yet gaps between research and clinical application are becoming evident, prompting the emergence of systems medicine as a translational extension. The study seeks to define systems medicine, identify relevant ontologies, outline key theoretical and methodological challenges, and assess how to integrate incomplete data and network analysis for clinical insight. The authors discuss the difficulties of translating the rapidly expanding data landscape into bedside knowledge. They conclude that specialized groups should be guided by a roadmap for systems medicine developed by a multidisciplinary coalition.

Abstract

Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.