Figure 3.

The relationship between genome medicine and systems pharmacology. The diagram summarizes various aspects of genome medicine (in blue) and systems pharmacology (in yellow). Overlapping aspects of analyses and practice are in green (intersection of circles). The positioning of the circles indicates the operational classification of 'genome medicine to systems pharmacology' as top-down and 'systems pharmacology to genome medicine' as bottom-up. The key analyses and practices are in the circle for the field that uses them. Approaches and practices that are used in both fields are in the overlapping region. Genome medicine starts with genetic and genomic testing. Experimental data are computationally processed using statistical genetics tools to yield information that is used in personalized medicine for therapeutic-index targeting (such as dosage of warfarin) and combination therapy. Network analysis is a common approach that integrates genome medicine and systems pharmacology. Systems pharmacology starts from cataloguing the characteristics of individual drugs and targets from biochemistry and cell-physiology experiments. Computational methods and genomic and proteomic data together enable the use of this catalog of information to make predictions regarding drug discovery, drug action and adverse events. Such predictions can be experimentally and clinically tested. Approaches common to both genome medicine and systems pharmacology are based on network analyses that underlie systems pathophysiology, whereby the origins of disease are understood in the context of multi-scale systems. Such understanding enables network-based drug screening and whole genome-based predictions of adverse events and drug resistance. Thus, ultimately, therapeutics intervention will be guided by integrating genome medicine and systems pharmacology.