Network Analyses in Systems Pharmacology

Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, One Gustave Levy Place, New York, NY 10029, USA.
Bioinformatics (Impact Factor: 4.98). 08/2009; 25(19):2466-72. DOI: 10.1093/bioinformatics/btp465
Source: PubMed

ABSTRACT Systems pharmacology is an emerging area of pharmacology which utilizes network analysis of drug action as one of its approaches. By considering drug actions and side effects in the context of the regulatory networks within which the drug targets and disease gene products function, network analysis promises to greatly increase our knowledge of the mechanisms underlying the multiple actions of drugs. Systems pharmacology can provide new approaches for drug discovery for complex diseases. The integrated approach used in systems pharmacology can allow for drug action to be considered in the context of the whole genome. Network-based studies are becoming an increasingly important tool in understanding the relationships between drug action and disease susceptibility genes. This review discusses how analysis of biological networks has contributed to the genesis of systems pharmacology and how these studies have improved global understanding of drug targets, suggested new targets and approaches for therapeutics, and provided a deeper understanding of the effects of drugs. Taken together, these types of analyses can lead to new therapeutic options while improving the safety and efficacy of existing medications.

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    • "Reaction networks represent a modeling paradigm that finds applications in many areas of science. Examples include, chemical reaction networks [1], cell signalling networks [2], gene expression networks [3], metabolic networks [4], pharmacological networks [5], epidemiological networks [6] and ecological networks [7]. Traditionally, reaction networks are mathematically analyzed by expressing the dynamics as a set of ordinary differential equations. "
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    ABSTRACT: Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many disciplines, spanning biochemistry, epidemiology, pharmacology, ecology and social networks. It is now well-established that, for small population sizes, stochastic models for reaction networks are necessary to capture randomness in the interactions. The tools for analyzing them, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics, and determining moment bounds for the underlying stochastic process. Theoretical and computational solutions for these problems are obtained by utilizing a blend of ideas and techniques from probability theory, linear algebra, polynomial analysis and optimization theory. We demonstrate that stability properties of a wide class of networks can be assessed from theoretical results that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species, but worst-case quadratic. We illustrate the validity, the efficiency and the universality of our results on several reaction networks arising in fields such as biochemistry, epidemiology and ecology.
    PLoS Computational Biology 06/2014; 10(6). DOI:10.1371/journal.pcbi.1003669 · 4.62 Impact Factor
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    • "Data analysis becomes critical to identify the MOA of drugs. Recently, network models and network analysis of genomic data has been proven useful to translate the microarray information and identify potential targets [11], [12] and enriched pathways involved in the MOA [13]–[15]. Tools such as Cytoscape have often been used to construct and analyze networks [16]. "
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    ABSTRACT: Chinese medicine is a complex system guided by traditional Chinese medicine (TCM) theories, which has proven to be especially effective in treating chronic and complex diseases. However, the underlying modes of action (MOA) are not always systematically investigated. Herein, a systematic study was designed to elucidate the multi-compound, multi-target and multi-pathway MOA of a Chinese medicine, QiShenYiQi (QSYQ), on myocardial infarction. QSYQ is composed of Astragalus membranaceus (Huangqi), Salvia miltiorrhiza (Danshen), Panax notoginseng (Sanqi), and Dalbergia odorifera (Jiangxiang). Male Sprague Dawley rat model of myocardial infarction were administered QSYQ intragastrically for 7 days while the control group was not treated. The differentially expressed genes (DEGs) were identified from myocardial infarction rat model treated with QSYQ, followed by constructing a cardiovascular disease (CVD)-related multilevel compound-target-pathway network connecting main compounds to those DEGs supported by literature evidences and the pathways that are functionally enriched in ArrayTrack. 55 potential targets of QSYQ were identified, of which 14 were confirmed in CVD-related literatures with experimental supporting evidences. Furthermore, three sesquiterpene components of QSYQ, Trans-nerolidol, (3S,6S,7R)-3,7,11-trimethyl-3,6-epoxy-1,10-dodecadien-7-ol and (3S,6R,7R)-3,7,11-trimethyl-3,6-epoxy-1,10-dodecadien-7-ol from Dalbergia odorifera T. Chen, were validated experimentally in this study. Their anti-inflammatory effects and potential targets including extracellular signal-regulated kinase-1/2, peroxisome proliferator-activated receptor-gamma and heme oxygenase-1 were identified. Finally, through a three-level compound-target-pathway network with experimental analysis, our study depicts a complex MOA of QSYQ on myocardial infarction.
    PLoS ONE 05/2014; 9(5):e95004. DOI:10.1371/journal.pone.0095004 · 3.23 Impact Factor
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    • "Third, we explore the role of structural systems pharmacology to enhance the power of pharmacogenomics and GWAS. Thus, this review complements several recent reviews that focus on a network view of systems pharmacology and its connection to phenotype [10]–[15]. Physical-based multiscale modeling will not be covered in detail, since this is presented elsewhere [37]–[40]. Ultimately, we argue, structural systems pharmacology should be incorporated into the modeling and simulation of macromolecular ensembles, tissues, and organisms. "
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    ABSTRACT: Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.
    PLoS Computational Biology 05/2014; 10(5):e1003554. DOI:10.1371/journal.pcbi.1003554 · 4.62 Impact Factor
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