Abstract
Currently, research in physiology focuses on molecular mechanisms underlying the functioning of living organisms. Reductionist strategies are used to decompose systems into their components and to measure changes of physiological variables between experimental conditions. However, how these isolated physiological variables translate into the emergence -and collapse- of biological functions of the organism as a whole is often a less tractable question. To generate a useful representation of physiology as a system, known and unknown interactions between heterogeneous physiological components must be taken into account. In this work we use a Complex Inference Networks approach to build physiological networks from biomarkers. We employ two unrelated databases to generate Spearman correlation matrices of 81 and 54 physiological variables, respectively, including endocrine, mechanic, biochemical, anthropometric, physiological, and cellular variables. From these correlation matrices we generated physiological networks by selecting a p-value threshold indicating statistically significant links. We compared the networks from both samples to show which features are robust and representative for physiology in health. We found that although network topology is sensitive to the p-value threshold, an optimal value may be defined by combining criteria of stability of topological features and network connectedness. Unsupervised community detection algorithms allowed to obtain functional clusters that correlate well with current medical knowledge. Finally, we describe the topology of the physiological networks, which lie between random and ordered structural features, and may reflect system robustness and adaptability. Modularity of physiological networks allows to explore functional clusters that are consistent even when considering different physiological variables. Altogether Complex Inference Networks from biomarkers provide an efficient implementation of a systems biology approach that is visually understandable and robust. We hypothesize that physiological networks allow to translate concepts such as homeostasis into quantifiable properties of biological systems useful for determination and quantification of health and disease.
Highlights
Communication and interaction between physiological systems and organs are the essence of physiology (Ganong, 1969; Bashan et al, 2012; Bartsch et al, 2015; Ivanov et al, 2016)
The best compromise between these needs was p < 0.001. This was remarkable because the density of the network continued to decrease exponentially whereas the strength of the nodes did not decrease at the same rate
This co-occurrence may be observed in time, as in networks constructed from time series for dynamical understanding of physiology (Liu et al, 2015), within populations through point measurements as is the case of our networks (Barajas-Martínez et al, 2020), and between individuals as shared characteristics to generate phenotypic clusters (Mihaicuta et al, 2017)
Summary
Communication and interaction between physiological systems and organs are the essence of physiology (Ganong, 1969; Bashan et al, 2012; Bartsch et al, 2015; Ivanov et al, 2016). An emerging paradigm for this problem is the systems biology perspective, where the organism is visualized as an open system composed of interacting components (Von Bertalanffy, 1968) The integration of these body components generates physiological states that can be studied in health and disease through complexity approaches (Ivanov et al, 2016). A network approach provides a new level of study where global properties of the system, that are not apparent at the local level, emerge from the interactions of the multiple components These interactions are revealed by changes in topology and connectivity (Ivanov and Bartsch, 2014)
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