Literature DB >> 17395089

Computational systems biology in drug discovery and development: methods and applications.

Wayne Materi1, David S Wishart.   

Abstract

Computational systems biology is an emerging field in biological simulation that attempts to model or simulate intra- and intercellular events using data gathered from genomic, proteomic or metabolomic experiments. The need to model complex temporal and spatiotemporal processes at many different scales has led to the emergence of numerous techniques, including systems of differential equations, Petri nets, cellular automata simulators, agent-based models and pi calculus. This review provides a brief summary and an assessment of most of these approaches. It also provides examples of how these methods are being used to facilitate drug discovery and development.

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Year:  2007        PMID: 17395089     DOI: 10.1016/j.drudis.2007.02.013

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  25 in total

1.  Coarse-grained molecular simulation of diffusion and reaction kinetics in a crowded virtual cytoplasm.

Authors:  Douglas Ridgway; Gordon Broderick; Ana Lopez-Campistrous; Melania Ru'aini; Philip Winter; Matthew Hamilton; Pierre Boulanger; Andriy Kovalenko; Michael J Ellison
Journal:  Biophys J       Date:  2008-01-30       Impact factor: 4.033

2.  Discovering Molecular Targets in Cancer with Multiscale Modeling.

Authors:  Zhihui Wang; Veronika Bordas; Thomas S Deisboeck
Journal:  Drug Dev Res       Date:  2011-02-01       Impact factor: 4.360

3.  Predicting antiprotozoal activity of benzyl phenyl ether diamine derivatives through QSAR multi-target and molecular topology.

Authors:  Ramon Garcia-Domenech; Riccardo Zanni; Maria Galvez-Llompart; Jorge Galvez
Journal:  Mol Divers       Date:  2015-03-10       Impact factor: 2.943

4.  Fuzzy optimization for detecting enzyme targets of human uric acid metabolism.

Authors:  Kai-Cheng Hsu; Feng-Sheng Wang
Journal:  Bioinformatics       Date:  2013-09-26       Impact factor: 6.937

Review 5.  Proteomics and metabolomics in renal transplantation-quo vadis?

Authors:  Rahul Bohra; Jacek Klepacki; Jelena Klawitter; Jost Klawitter; Joshua M Thurman; Uwe Christians
Journal:  Transpl Int       Date:  2012-11-21       Impact factor: 3.782

6.  Strategies for efficient numerical implementation of hybrid multi-scale agent-based models to describe biological systems.

Authors:  Nicholas A Cilfone; Denise E Kirschner; Jennifer J Linderman
Journal:  Cell Mol Bioeng       Date:  2015-03       Impact factor: 2.321

7.  Systems biology modeling reveals a possible mechanism of the tumor cell death upon oncogene inactivation in EGFR addicted cancers.

Authors:  Jian-Ping Zhou; Xin Chen; Shan Feng; Shi-Dong Luo; You-Li Pan; Lei Zhong; Pan Ji; Ze-Rong Wang; Shuang Ma; Lin-Li Li; Yu-Quan Wei; Sheng-Yong Yang
Journal:  PLoS One       Date:  2011-12-14       Impact factor: 3.240

8.  Modeling formalisms in Systems Biology.

Authors:  Daniel Machado; Rafael S Costa; Miguel Rocha; Eugénio C Ferreira; Bruce Tidor; Isabel Rocha
Journal:  AMB Express       Date:  2011-12-05       Impact factor: 3.298

Review 9.  Bioinformatics for cancer immunology and immunotherapy.

Authors:  Pornpimol Charoentong; Mihaela Angelova; Mirjana Efremova; Ralf Gallasch; Hubert Hackl; Jerome Galon; Zlatko Trajanoski
Journal:  Cancer Immunol Immunother       Date:  2012-09-18       Impact factor: 6.968

10.  targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis.

Authors:  Karthik Raman; Kalidas Yeturu; Nagasuma Chandra
Journal:  BMC Syst Biol       Date:  2008-12-19
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