Please wait a minute...
Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

邮发代号 80-971

Quantitative Biology  2015, Vol. 3 Issue (1): 46-53   https://doi.org/10.1007/s40484-015-0039-9
  本期目录
SupraBiology 2014: Promoting UK-China collaboration on Systems Biology and High Performance Computing
Ettore Murabito1, Riccardo Colombo2,3, Chengkun Wu4, Malkhey Verma4, Samrina Rehman4, Jacky Snoep4, Shao-Liang Peng5, Naiyang Guan5, Xiangke Liao5(), Hans V. Westerhoff4()
1. Manchester Institute of Biotechnology, School of Computer Science, Faculty of Engineering and Physical Sciences, Manchester Centre for Integrative Systems Biology, The University of Manchester, Manchester, M139PL, United Kingdom.
2. Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, 20126, Italy.
3. SYSBIO – Centre of Systems Biology, Milan, 20126, Italy.
4. Manchester Institute of Biotechnology, School of Chemical Engineering and Analytical Sciences, Manchester Centre for Integrative Systems Biology, The University of Manchester, Manchester, M139PL, United Kingdom.
5. School of Computer Science, National University of Defence Technology, Changsha 410073, China
 全文: PDF(115 KB)   HTML
收稿日期: 2015-01-27      出版日期: 2015-05-06
Corresponding Author(s): Xiangke Liao,Hans V. Westerhoff   
 引用本文:   
. [J]. Quantitative Biology, 2015, 3(1): 46-53.
Ettore Murabito, Riccardo Colombo, Chengkun Wu, Malkhey Verma, Samrina Rehman, Jacky Snoep, Shao-Liang Peng, Naiyang Guan, Xiangke Liao, Hans V. Westerhoff. SupraBiology 2014: Promoting UK-China collaboration on Systems Biology and High Performance Computing. Quant. Biol., 2015, 3(1): 46-53.
 链接本文:  
https://academic.hep.com.cn/qb/CN/10.1007/s40484-015-0039-9
https://academic.hep.com.cn/qb/CN/Y2015/V3/I1/46
1 H. V. Westerhoff, and B. O. Palsson, (2004) The evolution of molecular biology into systems biology. Nat. Biotechnol., 22, 1249−1252
https://doi.org/10.1038/nbt1020. pmid: 15470464
2 W.-L. Liao, and F.-J. Tsai, (2013) Personalized medicine: A paradigm shift in healthcare. Biomedicine, 3, 66−72
https://doi.org/10.1016/j.biomed.2012.12.005.
3 A. Kolodkin,, F. C. Boogerd,, N. Plant,, F. J. Bruggeman,, V. Goncharuk,, J. Lunshof,, R. Moreno-Sanchez,, N. Yilmaz,, B. M. Bakker,, J. L. Snoep,, et al. (2012) Emergence of the silicon human and network targeting drugs. Eur. J. Pharm. Sci., 46, 190−197
https://doi.org/10.1016/j.ejps.2011.06.006. pmid: 21704158
4 P. V. Lawford,, et al. (1921) Virtual physiological human: training challenges. Philoso. T. Roy. Soc., 2010, 368.
5 M. Viceconti,, G. Clapworthy, and S.V.S. Jan, (2008) The Virtual Physiological Human — A European Initiative for in silico Human Modelling —. J. Physiol., 58, 441−446
6 B. G. Olivier, and J. L. Snoep, (2004) Web-based kinetic modelling using JWS Online. Bioinformatics, 20, 2143−2144
https://doi.org/10.1093/bioinformatics/bth200. pmid: 15072998
7 I. Stoica,, S.K. Sadiq, and P.V. Coveney, (2008) Rapid and accurate prediction of binding free energies for Saquinavir-Bound HIV-1 proteases. J. Am. Chem. Soc., 130 2639−2648
8 T. Hou,, J. Wang,, Y. Li, and W. Wang, (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model., 51, 69−82
https://doi.org/10.1021/ci100275a. pmid: 21117705
9 M. D. Mazzeo, and P. V. Coveney, (2008) HemeLB: A high performance parallel lattice-Boltzmann code for large scale fluid flow in complex geometries. Comput. Phys. Commun., 178, 894−914
https://doi.org/10.1016/j.cpc.2008.02.013.
10 S. Hoops,, S. Sahle,, R. Gauges,, C. Lee,, J. Pahle,, N. Simus,, M. Singhal,, L. Xu,, P. Mendes, and U. Kummer, (2006) COPASI—a COmplex PAthway SImulator. Bioinformatics, 22, 3067−3074
https://doi.org/10.1093/bioinformatics/btl485. pmid: 17032683
11 P. Cazzaniga,, C. Damiani,, D. Besozzi,, R. Colombo,, M.S. Nobile,, D. Gaglio,, D. Pescini,, S. Molinari,, G. Mauri,, L. Alberghina, et al. (2014) Computational strategies for a system-level understanding of metabolism. Metabolites, 4, 1034−1087
12 P.D. Karp,, S. Paley, and P. Romero, (2002) The pathway tools software. Bioinformatics, 18, suppl. 1: S225−S232
13 J. W. Pinney,, M. W. Shirley,, G. A. McConkey, and D. R. Westhead, (2005) metaSHARK: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella. Nucleic Acids Res., 33, 1399−1409
https://doi.org/10.1093/nar/gki285. pmid: 15745999
14 F. Büchel,, N. Rodriguez,, N. Swainston,, C. Wrzodek,, T. Czauderna,, R. Keller,, F. Mittag,, M. Schubert,, M. Glont,, M. Golebiewski,, et al. (2013) Path2Models: large-scale generation of computational models from biochemical pathway maps. BMC Syst. Biol., 7, 116
https://doi.org/10.1186/1752-0509-7-116. pmid: 24180668
15 C. P. Fisher,, N. J. Plant,, J. B. Moore, and A. M. Kierzek, (2013) QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells. Bioinformatics, 29, 3181−3190
https://doi.org/10.1093/bioinformatics/btt552. pmid: 24064420
16 C. Wu,, J.-M. Schwartz,and G. Nenadic, (2013) PathNER: a tool for systematic identification of biological pathway mentions in the literature. BMC Syst. Biol., 7, Suppl. 3:S2
17 M. Chetty, R.H. Rose,, K. Abduljalil,, N. Patel,, G. Lu,, T. Cain,, M. Jamei, and A. Rostami-Hodjegan, (2014) Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability. Front. Pharmacol., 5, 258
18 L. G. Jr Valerio,. (2011) In silico toxicology models and databases as FDA Critical Path Initiative toolkits. Hum. Genomics, 5, 200−207
https://doi.org/10.1186/1479-7364-5-3-200. pmid: 21504870
19 P. D. Dobson, and D. B. Kell, (2008) Carrier-mediated cellular uptake of pharmaceutical drugs: an exception or the rule? Nat. Rev. Drug Discov., 7, 205−220
https://doi.org/10.1038/nrd2438. pmid: 18309312
20 D. B. Kell,, P. D. Dobson,, E. Bilsland, and S. G. Oliver, (2013) The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so. Drug Discov. Today, 18, 218−239
https://doi.org/10.1016/j.drudis.2012.11.008. pmid: 23207804
21 K. Lanthaler,, E. Bilsland,, P. D. Dobson,, H. J. Moss,, P. Pir,, D.B. Kell, and S.G. Oliver,(2011) Genome-wide assessment of the carriers involved in the cellular uptake of drugs: a model system in yeast. BMC Biol., 9, 70
22 X. Yang,, X. Liao,, W. Xu,, J. Song,, Q. Hu,, J. Su,, L. Xiao,, K. Lu,, Q. Dou,, J. Jiang,, et al. (2010) TH-1: China's first petaflop supercomputer. Front. Comput. Sci. China, 4, 445−455
https://doi.org/10.1007/s11704-010-0383-x.
23 X. Yang,, X.-K. Liao,, K. Lu,, Q.-F. Hu,, J.-Q. Song, and J.-S. Su, (2011) The TianHe-1. A supercomputer: Its hardware and software. J. Comput. Sci. Technol., 26, 344−351
https://doi.org/10.1007/s02011-011-1137-8.
24 Tianhe-2 (MilkyWay-2) Supercomputer
25 R. B. Luo,, B.H. Liu,, Y. L. Xie,, Z.Y. Li,, W.H. Huang,, J.Y. Yuan,, G. Z. He,, Y. X. Chen,, Q. Pan,, Y. J. Liu,et al., (2012) SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience, 1, 18.
26 R. Luo,, T. Wong,, J. Zhu,, C. M. Liu,, X. Zhu,, E. Wu,, L. K. Lee,, H. Lin,, W. Zhu,, D. W. Cheung,, et al. (2013) SOAP3-dp: fast, accurate and sensitive GPU-based short read aligner. PLOS ONE, 8, e65632
pmid: 23741504.
27 W. Jia,, K. Qiu,, M. He,, P. Song,, Q. Zhou,, F. Zhou,, Y. Yu,, D. Zhu,, M. L. Nickerson,, S. Wan,, et al. (2013) SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data. Genome Biol., 14, R12
https://doi.org/10.1186/gb-2013-14-2-r12. pmid: 23409703
28 Y. Cui,, X.-K. Liao,, X. Q. Zhu, and B. Q. Wang, (2014) mBWA: A massively parallel sequence reads aligner, in 8th international conference on practical applications of computational biology & bioinformatics (PACBB 2014). Springer International Publishing, 113−120
29 N.Y. Guan,, D. C. Tao,, Z. G. Luo, and B. Yuan, (2012) NeNMF: An optimal gradient method for Nonnegative Matrix Factorization. IEEE Trans. Signal Process., 60, 2882−2898
30 E. Murabito,, K. Smallbone,, J. Swinton,, H. V. Westerhoff, and R. Steuer, (2011) A probabilistic approach to identify putative drug targets in biochemical networks. J. R. Soc. Interface, 8, 880−895
https://doi.org/10.1098/rsif.2010.0540. pmid: 21123256
31 E. Murabito,, (2013) Targeting breast cancer metabolism: A metabolic control analysis approach. Curr. Synthetic Sys. Biol., 1, 104
32 E. Murabito,, M. Verma,, M. Bekker,, D. Bellomo,, H. V. Westerhoff,, B. Teusink, and R. Steuer, (2014) Monte-Carlo modeling of the central carbon metabolism of Lactococcus lactis: insights into metabolic regulation. PLOS ONE, 9, e106453
https://doi.org/10.1371/journal.pone.0106453. pmid: 25268481
33 I. Thiele,, N. Swainston,, R. M. Fleming,, A. Hoppe,, S. Sahoo,, M. K. Aurich,, H. Haraldsdottir,, M. L. Mo,, O. Rolfsson,, M. D. Stobbe,, et al. (2013) A community-driven global reconstruction of human metabolism. Nat. Biotechnol., 31, 419−425
https://doi.org/10.1038/nbt.2488. pmid: 23455439
34 E. Kent,, S. Hoops, and P. Mendes, (2012) Condor-COPASI: high-throughput computing for biochemical networks. BMC Syst. Biol., 6, 91
https://doi.org/10.1186/1752-0509-6-91. pmid: 22834945
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed