A systems-theoretical framework for health and disease: inflammation and preconditioning from an abstract modeling point of view

Abstract

Modern advances in molecular biology have produced enormous amounts of data characterizing physiological and disease states in cells and organisms. While bioinformatics has facilitated the organizing and mining of these data, it is the task of systems biology to merge the available information into dynamic, explanatory and predictive models. This article takes a step into this direction. It proposes a conceptual approach toward formalizing health and disease and illustrates it in the context of inflammation and preconditioning. Instead of defining health and disease states, the emphasis is on simplexes in a high-dimensional biomarker space. These simplexes are bounded by physiological constraints and permit the quantitative characterization of personalized health trajectories, health risk profiles that change with age, and the efficacy of different treatment options. The article mainly focuses on concepts but also briefly describes how the proposed concepts might be formulated rigorously within a mathematical framework.

Fig. 1

Normal “biomarker” values are replaced by ranges, possibly in logarithmic coordinates.

Fig. 2

Constrained normal biomarker ranges become health simplexes in two or more dimensions.

Fig. 3

Constrained normal biomarker ranges become nested health and disease simplexes in a high-dimensional space; illustrations are given here in two and three dimensions, respectively.

Fig. 4

Two individuals P₁ and P₂ have exactly the same score (x₁ = x₂ > ϑ) with respect to biomarker x, which exceeds the alleged disease threshold ϑ. Nonetheless, P₁ resides (with coordinates x₁ > ϑ, y₁, z₁) within the health simplex, while P₂ is diseased, because P₂'s coordinates (x₂ > ϑ, y₂, z₂) fall outside the health simplex.