“It was the best of times, it was the worst of times…” This could be the description of modern medicine today. On one hand, the technological advancements in biomedicine enable us to gather overwhelming amounts of information about our bodies, ranging from genomic and molecular information, all the way to images and physiological signals. On the other hand, the large amount of additional information does not seem to help us to curb the escalating health care costs we are facing.
One strategy to bring down the health care costs is by accelerating the translation of new knowledge from research laboratories to clinical practice, the so-called translational medicine. Successful implement of translational medicine may lead to many innovative interventions, and ways for early diagnosis of diseases. A promising, but less recognized direction for this translation is to extract dynamical features hidden in the signals collected by new technologies.
Life is a dynamical process. Thus, it is not surprising that disease processes alter important aspects of healthy dynamics. These changes, therefore, could serve as useful dynamical signatures of the underlying disease states. In recent years, significant progress has been made in decoding these dynamical patterns and using them as biomarkers. The key challenge is that the analytical tools developed for the purpose of measuring dynamical biomarkers have to be physically meaningful, mathematically rigorous, and clinically relevant. To meet this challenge, researchers in multiple disciplines, from mathematics, physics, computer science, engineering, to biomedical and clinical sciences, have to work together. To this end, the National Science Council (NSC) of Taiwan established a new International Research-Intensive Center of Excellence: Center for Dynamical Biomarkers and Translational Medicine, to explore this new research area. For the 2nd annual symposium, we continue the tradition to bring leading interdisciplinary researchers together to discuss innovative approaches to utilize dynamical patterns in health and disease for better clinical care.