From systems biology to P4 medicine: applications in respiratory medicine

Abstract

Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, "omics" data and bioinformatic computing power. It paves the way forward towards "P4 medicine" (predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field.

FIGURE 1

Multilevel layers of biological, environmental and social information ideally integrated in systems biomedicine approaches. For further explanations, see text. Reproduced and modified from [2] with permission.

FIGURE 2

Network topology. Nodes are linked by edges. Node size represents a quantifiable node property (e.g. fold-change in two different experimental situations; this allows the inclusion of a dynamic component (i.e. time change) in the graphical representation of the network). Edge width represents the connection strength (e.g. correlation coefficient). Node colours identify different network modules. For further explanations, see text.

FIGURE 3

Summary of bioinformatic methodologies currently available. a) Correlation (e.g. Pearson/Spearman) network constructed from omics data. b) Weighted gene coexpression networks analysis (WGCNA) methodology. c) Bayesian networks approach. d) Gene set enrichment with gene set enrichment analysis (GSEA). e) k-means clustering. For further explanations, see text.