The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges

Abstract

The field of Network Physiology aims to advance our understanding of how physiological systems and sub-systems interact to generate a variety of behaviors and distinct physiological states, to optimize the organism's functioning, and to maintain health. Within this framework, which considers the human organism as an integrated network, vertices are associated with organs while edges represent time-varying interactions between vertices. Likewise, vertices may represent networks on smaller spatial scales leading to a complex mixture of interacting homogeneous and inhomogeneous networks of networks. Lacking adequate analytic tools and a theoretical framework to probe interactions within and among diverse physiological systems, current approaches focus on inferring properties of time-varying interactions-namely strength, direction, and functional form-from time-locked recordings of physiological observables. To this end, a variety of bivariate or, in general, multivariate time-series-analysis techniques, which are derived from diverse mathematical and physical concepts, are employed and the resulting time-dependent networks can then be further characterized with methods from network theory. Despite the many promising new developments, there are still problems that evade from a satisfactory solution. Here we address several important challenges that could aid in finding new perspectives and inspire the development of theoretic and analytical concepts to deal with these challenges and in studying the complex interactions between physiological systems.

Keywords: complex networks; inverse problem; network physiology; organ communications; physiological systems; surrogate concepts; time-series-analysis techniques.

Figure 1

Schematic of the human organism as an evolving complex network of dynamical interactions between organ systems. The dynamics of different organs exhibit a broad range of timescales, and physiological observables are typically based on different physical and/or chemical quantities. Time-dependent organ-organ interaction matrices are derived from a time-resolved time-series-analysis-based characterization of interactions from all pairs of observables. These matrices represent a network that evolves in time, with nodes representing organs and edges representing time-varying interactions between them.