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: 4.62). 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.

Download full-text


Available from: Seth Berger, Nov 19, 2014
  • Source
    [Show abstract] [Hide abstract]
    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 · 4.83 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Cancer is increasingly described as a systems-level, network phenomenon. Genetic methods, such as next generation sequencing and RNA interference uncovered the complexity tumor-specific mutation-induced effects and the identification of multiple target sets. Network analysis of cancer-specific metabolic and signaling pathways highlighted the structural features of cancer-related proteins and their complexes to develop next-generation protein kinase inhibitors, as well as the modulation of inflammatory and autophagic pathways in anti-cancer therapies. Importantly, malignant transformation can be described as a two-phase process, where an initial increase of system plasticity is followed by a decrease of plasticity at late stages of tumor development. Late-stage tumors should be attacked by an indirect network influence strategy. On the contrary, the attack of early-stage tumors may target central network nodes. Cancer stem cells need special diagnosis and targeting, since they potentially have an extremely high ability to change the rigidity/plasticity of their networks. The early warning signals of the activation of fast growing tumor cell clones are important in personalized diagnosis and therapy. Multi-target attacks are needed to perturb cancer-specific networks to exit from cancer attractors and re-enter a normal attractor. However, the dynamic non-genetic heterogeneity of cancer cell population induces the replenishment of the cancer attractor with surviving, non-responsive cells from neighboring abnormal attractors. The development of drug resistance is further complicated by interactions of tumor clones and their microenvironment. Network analysis of intercellular cooperation using game theory approaches may open new areas of understanding tumor complexity. In conclusion, the above applications of the network approach open up new, and highly promising avenues in anti-cancer drug design.
    Seminars in Cancer Biology 07/2013; 23(4). DOI:10.1016/j.semcancer.2013.06.011 · 9.33 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The study of protein-protein interactions (PPIs) has been growing for some years now, mainly as a result of easy access to high-throughput experimental data. Several computational approaches have been presented throughout the years as means to infer PPIs not only within the same species, but also between different species (e.g., host-pathogen interactions). The importance of unveiling the human protein interaction network is undeniable, particularly in the biological, biomedical and pharmacological research areas. Even though protein interaction networks evolve over time and can suffer spontaneous alterations, occasional shifts are often associated with disease conditions. These disorders may be caused by external pathogens, such as bacteria and viruses, or by intrinsic factors, such as auto-immune disorders and neurological impairment. Therefore, having the knowledge of how proteins interact with each other will provide a great opportunity to understand pathogenesis mechanisms, and subsequently support the development of drugs focused on very specific disease pathways and re-targeting already commercialized drugs to new gene products. Computational methods for PPI prediction have been highlighted as an interesting option for interactome mapping. In this paper we review the techniques and strategies used for both experimental identification and computational inference of PPIs. We will then discuss how this knowledge can be used to create protein interaction networks (PINs) and the various methodologies applied to characterize and predict the so-called "disease genes" and "disease networks". This will be followed by an overview of the strategies employed to predict drug targets.
    Current topics in medicinal chemistry 03/2013; DOI:10.2174/1568026611313050005 · 3.45 Impact Factor