Multiscale analysis of complex biological systemsOne of the great challenges of 21st century biomedical science is to understand more fully the dynamics of living systems in health and disease. The importance is highlighted by headlines announcing unexpected, life-threatening side effects of once-promising drugs, as well as serendipitous therapeutic discoveries deriving from “outside the box” approaches to major public health problems, for example, in heart disease and cancer biology. The basis of such unexpected findings, both negative and positive, is the extraordinary complexity of physiologic systems, which exceeds that of the most challenging systems in the physical world. These living systems defy understanding based on conventional, typically linear analyses.
The overall goal of my proposal is to develop a deeper understanding of the dynamics underlying
healthy biological systems and what occurs when these systems lose their robustness due to disease or aging. However, because of the nonlinear and multiscale complexity of these systems, it is unrealistic to achieve this goal purely by a traditional reductionist approach in which one “dissects” the system into its constituent pieces, studies each component in detail, and finally puts them back together, in an attempt to recreate the original entity. Even in rare cases where this type of reductionist program can be accomplished, the integrative system's behavior typically surprises expectations based solely on the information gathered by analyzing each component or module in isolation.
In everyday parlance, this well-known effect is referred to as
the whole being greater than the sum of the parts. In the language of complex systems, it is known by the term “emergent properties.” In nonlinear systems, the composite or group behavior (of molecules, cells, organs and even societies) cannot be fully understood by simply “adding up” the components. Instead, one needs rigorous, new approaches to measuring and modeling a system's multiscale, integrative behavior.
Central to this enterprise are computational tools and mathematical models that usefully represent the behavior of the integrative system across many scales of time and space. These system-level measurements and models need to capture certain generic and robust properties of complex biological systems, such that they have a wide range of applications across many disciplines. To this end, I shall focus on studying the output signals generated by a variety of complex physiological systems. The dynamical fluctuations of these signals in health and disease provide a unique window into the free-running behavior of the integrative systems.
On a practical level, this work may lead to new ways to monitor health status, measure the aging process, detect drug toxicity and forecast catastrophic events such as seizures, sudden cardiac death, and falls in the elderly. These multiscale, computational tools also promise to provide new ways to rapidly screen and test new therapeutic interventions designed to enhance system complexity and adaptiveness.
