Cause-effect relationships in medicine: a protein network perspective.
ABSTRACT Current target-based drug discovery platforms are not able to predict drug efficacy and the full spectrum of drug effects in organisms. Hence, many experimental drugs do not survive the lengthy and costly process of drug development. Understanding how drugs affect cellular network structures and how the resulting signals are translated into drug effects is extremely important for the discovery of new medicines. This requires a greater understanding of cause-effect relationships at the organism, organ, tissue, cellular, and molecular level. There is a growing recognition that this information must be integrated into discovery paradigms, but a 'road map' for obtaining and integrating information about heterogeneous networks into drug-discovery platforms currently does not exist. This review explores recent network-centered approaches developed to investigate the genesis of medicine and disease effects, specifically highlighting protein-protein interaction network models and their use in cause-effect analyses in medicine.
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ABSTRACT: The scientific understanding of traditional Chinese medicine (TCM) has been hindered by the lack of methods that can explore the complex nature and combinatorial rules of herbal formulae. On the assumption that herbal ingredients mainly target a molecular network to adjust the imbalance of human body, here we present a-self-developed TCM network pharmacology platform for discovering herbal formulae in a systematic manner. This platform integrates a set of network-based methods that we established previously to catch the network regulation mechanism and to identify active ingredients as well as synergistic combinations for a given herbal formula. We then provided a case study on an antirheumatoid arthritis (RA) formula, Qing-Luo-Yin (QLY), to demonstrate the usability of the platform. We revealed the target network of QLY against RA-related key processes including angiogenesis, inflammatory response, and immune response, based on which we not only predicted active and synergistic ingredients from QLY but also interpreted the combinatorial rule of this formula. These findings are either verified by the literature evidence or have the potential to guide further experiments. Therefore, such a network pharmacology strategy and platform is expected to make the systematical study of herbal formulae achievable and to make the TCM drug discovery predictable.Evidence-based Complementary and Alternative Medicine 01/2013; 2013:456747. · 1.72 Impact Factor
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ABSTRACT: Biomolecules in subnetworks are the focus of a new strategy to develop drugs that halt complex diseases. In this article, the authors use genome-wide association study and linkage data derived from Parkinson's disease studies to illustrate how algorithms that use gene and protein interaction databases reveal subnetworks in biological systems that suggest mechanisms for disease progression. Network modeling may help develop testable hypotheses for neurodegenerative diseases and open up new avenues for therapeutic development.Expert Review of Neurotherapeutics 06/2013; 13(6):685-93. · 2.96 Impact Factor
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ABSTRACT: Transient receptor potential (TRP) channels are a large family of non-selective cation channels that mediate numerous physiological and pathophysiological processes; however, still largely unknown are the underlying molecular mechanisms. With data generated on an unprecedented scale, network-based approaches have been revolutionizing the way in which we understand biology and disease, discover disease genes, and develop therapeutic strategies. These circumstances have created opportunities to encounter TRP channel research to data-intensive science. In this review, we provide an introduction of network-based approaches in biomedical science, describe the current state of TRP channel network biology, and discuss the future direction of TRP channel research. Network perspective will facilitate the discovery of latent roles and underlying mechanisms of TRP channels in biology and disease.Pflügers Archiv - European Journal of Physiology 05/2013; · 4.87 Impact Factor