Task Force on Strategic Research Direction Clinical

I. Complex Systems Analysis for Clinical Application of Scientific Data Introduction, Definition, Content The scope of new biomedical information is rapidly evolving, both in breadth and depth, from multiple research strategies. These sources of data include: (1) major population studies on the epidemiology and results of intervention for disease prevention from clinical trials and epidemiological surveys; (2) new approaches to understanding the pathophysiology of disease, with particular regard to predictors of acute events; and (3) the promise of a major impact on prediction, prevention, and treatment of disease, as well as a much better understanding of the basic biology of cardiovascular disease as a result of genetic insights. Each of these disciplines offers individual analytical challenges in order to understand their meaning and applications, but equally important, the three avenues of study must be integrated to maximize the derived benefits. It will be necessary to construct new methods for complex systems analyses applicable to these biomedical questions. Although this requirement will apply to all phases of large database analysis, it is particularly important for deriving benefits that can be applied to human disease states from molecular genetics. Completion of the draft sequence of the human genome has provided a broad horizon of opportunity for application of genetically based information to our knowledge of pathophysiology and to the diagnosis, prevention, and treatment of clinical states. Technological advances in high-throughput sequencing and various gene expression methods are anticipated to provide ever-increasing volumes of information, with the potential to generate novel approaches to clinical medicine. It was apparent, even before the completion of the draft sequence of the human genome, that clinical expressions of genetic variations are not isolated deterministic phenomena. Rather, an abundance of information suggests that expression of genetic variations is modulated by other factors. Downstream expression of genetic alterations may be modified by other genes (gene-gene interaction), by nongenetic endogenous physiological influences such as autonomic fluctuations or metabolic states, and by exogenous substances such as drugs and environmental exposures. These relationships are extraordinarily complex and will require complex mathematical analyses, which are new systems for the application of information theory to systemic biology. Such endeavors will lead to understanding of the complex interactions between multiple genes and nongenetic biological factors, as well as to the growth of a relatively new discipline, genetic epidemiology. The latter offers the promise of the ability to predict disease expression in specific subgroups and individuals that might influence application of rational therapy and prevention strategies. These promises will be fulfilled only with the development of a discipline of complex systems analyses specific to the biomedical field. The methods on which this discipline will rest are currently in their infancy.