Network analyses in systems pharmacology

Berger, Seth; Iyengar, Ravi

doi:10.1093/bioinformatics/btp465

Cited by 423 publications

(298 citation statements)

References 52 publications

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“…Systems biology approaches are naturally suited to capture the complexity of drug activity in cells (4)(5)(6). Prediction of drug MoA has been attempted by using gene expression profiles following drug treatment (7)(8)(9)(10)(11)(12)(13), by comparing side-effect similarities (14), by text-mining literature (15), or by applying chemoinformatic tools to search for small molecules similarities (16,17).…”

mentioning

confidence: 99%

Discovery of drug mode of action and drug repositioning from transcriptional responses

Iorio

Bosotti²,

Scacheri³

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

761

728

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A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compoundtargeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).computational drug discovery | drug repurposing | systems biology | chemotherapy

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mentioning

confidence: 99%

Discovery of drug mode of action and drug repositioning from transcriptional responses

Iorio

Bosotti²,

Scacheri³

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

761

728

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“…As discussed in the previous chapters the network generated is based on the concept of Petri net based approach generated on One particularly valuable approach is the use of network analysis of cellular systems." (Berger and Iyengar, 2009) With the availability and ever increasing volume of molecular data enhances our ability to understand the capacity of study cell behaviour. Further to explore and exploit molecular data, one must also investigate the various links between genes and proteins; the relationship between protein structure and function and the united effects These studies put forth various hypotheses and the research results enhance the knowledge of drug design, disease and networks at a larger scale.…”

Section: Discussionmentioning

confidence: 99%

BioSilicoSystems - A Multipronged Approach Towards Analysis and Representation of Biological Data (PhD Thesis)

Hariharaputran

2010

Nat Prec

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The rising field of integrative bioinformatics provides the vital methods to integrate, manage and also to analyze the diverse data and allows gaining new and deeper insights and a clear understanding of the intricate biological systems. The difficulty is not only to facilitate the study of heterogeneous data within the biological context, but it also more fundamental, how to represent and make the available knowledge accessible. Moreover, adding valuable information and functions that persuade the user to discover the interesting relations hidden within the data is, in itself, a great challenge. Also, the cumulative information can provide greater biological insight than is possible with individual information sources. Furthermore, the rapidly growing number of databases and data types poses the challenge of integrating the heterogeneous data types, especially in biology. This rapid increase in the volume and number of data resources drive for providing polymorphic views of the same data and often overlap in multiple resources.In this thesis a multi-pronged approach is proposed that deals with various methods for the analysis and representation of the diverse biological data which are present in different data sources. This is an effort to explain and emphasize on different concepts which are developed for the analysis of molecular data and also to explain its biological significance. The hypotheses proposed are in context with various other results and findings published in the past. The approach demonstrated also explains different ways to integrate the molecular data from various sources along with the need for a comprehensive understanding and clear projection of the concept or the algorithm and its results, but with simple means and methods. The multifarious approach proposed in this work comprises of different tools or methods spanning significant areas of bioinformatics research such as data integration, data visualization, biological network construction / reconstruction and alignment of biological pathways. Each tool deals with a unique approach to utilize the molecular data for different areas of biological research and is built based on the kernel of the thesis. Furthermore these methods are combined with graphical representation that make things simple and comprehensible and also helps to understand with ease the underlying biological complexity. Moreover the human eye is often used to and it is more comfortable with the visual representation of the facts.

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“…Recently, cell biologists are involved in pharmacology and drug discovery after recognition of the important role that networks have in cellular and tissue function. The networks that operate within and between cells are liable to changes induced by therapeutic agents (10). Cell biologists are rethinking the conceptual basis of pharmacology and drug discovery and are appreciating the quantitative behaviors of networks in cells, tissues and organisms (11).…”

Section: Systems Biology Concept Of Human Diseasementioning

confidence: 99%

Heart failure: a complex clinical process interpreted by systems biology approach and network medicine

Louridas¹,

Lourida²

2014

Anadolu Kardiyol Derg

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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 nonlinear 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. (Anadolu Kardiyol Derg 2014; 14: 178-85)

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Network analyses in systems pharmacology

Cited by 423 publications

References 52 publications

Discovery of drug mode of action and drug repositioning from transcriptional responses

Discovery of drug mode of action and drug repositioning from transcriptional responses

BioSilicoSystems - A Multipronged Approach Towards Analysis and Representation of Biological Data (PhD Thesis)

Heart failure: a complex clinical process interpreted by systems biology approach and network medicine

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