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Updated: May 2, 2026

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
Heart failure: a complex clinical process interpreted by systems biology approach and network medicine
George E Louridas1, Katerina G Lourida
1Emeritus Professor of Cardiology, Aristotle University of Thessaloniki; Thessaloniki-Greece 1MSc in Information Security; Thessaloniki-Greece. louridasg@gmail.com.
Abstract:
Systems biology is founded on the principles of integrative computational analysis and on the data from genetic and molecular components. The integration of biological components produces interacting networks, modules and phenotypes with remarkable applications in the field of clinical medicine. The evolving concept of network medicine gives a more precise picture of the intrinsic complexity of failing myocardium and its clinical consequences. The present review is focused on the impact of network cardiology in explaining the progressive nature of the clinical syndrome of heart failure. The failing myocardium and the subsequent clinical syndrome of heart failure disclose a dynamical and non-linear system with a progressive picture of clinical deterioration. The classical description of heart failure is based on tissue pathology and clinical presentation, and lately on specific genetic and molecular modifications. This characterization of heart failure has significant limitations to recognize preclinical disease features and to explain the progressive nature of the syndrome. Systems biology detects and evaluates specific networks from molecular, cellular and tissue elements, and assesses their influence on the appearance of clinical phenotypes. The classical reductive concept of heart failure is inadequate to provide data for molecular dysfunctions or defective coordination of the interconnected network components that are central to the genesis and clinical deterioration of heart failure. In heart failure, the recognition of molecular targets within the complex networks will increase the conceptual basis of pharmacology and the identification of novel biomarkers and at the same time will accelerate the discovery of new drugs.
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