The future of whole-cell modeling

Current Opinion in Biotechnology

Volumn 28, Issue , 2014, Pages 111-115

  Macklin, Derek N ^a   Ruggero, Nicholas A ^a   Covert, Markus W ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BUILDING MODEL; COMMUNITY DEVELOPMENT; COMPLEX CELLS; DATA CURATION; MODEL VALIDATION; MYCOPLASMA GENITALIUM; SYSTEMS BIOLOGY; WHOLE CELL;

DATA VISUALIZATION;

COMPUTER SIMULATION;

BIOTECHNOLOGY; CELL AGGREGATION; CELL TYPE; COMPUTER MODEL; DATA ANALYSIS; EXPERIMENTAL DESIGN; MOLECULAR BIOLOGY; MYCOPLASMA GENITALIUM; NONHUMAN; PRIORITY JOURNAL; PROCESS OPTIMIZATION; REVIEW; VALIDATION STUDY; WHOLE CELL;

CELLS; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; MODELS, BIOLOGICAL; MYCOPLASMA GALLISEPTICUM;

EID: 84896719722     PISSN: 09581669     EISSN: 18790429     Source Type: Journal    
DOI: 10.1016/j.copbio.2014.01.012     Document Type: Review

References (55)

1. Shuler M.L., Leung S., Dick C.C. A mathematical model for the growth of a single cell. Ann N Y Acad Sci 1979, 326:35-52.
2. Domach M.M., Shuler M.L. A finite representation model for an asynchronous culture of E. coli. Biotechnol Bioeng 1984, 26:877-884.
3. Tomita M., et al. E-CELL: software environment for whole-cell simulation. Bioinformatics 1999, 15:72-84.
4. Shuler M.L., Foley P., Atlas J. Modeling a minimal cell. Methods Mol Biol 2012, 881:573-610.
5. Savinell J., Palsson B.O. Network analysis of intermediary metabolism using linear optimization. I. Development of mathematical formalism. J Theor Biol 1992, 154:421-454.
6. Varma A., Palsson B.O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol 1994, 60:3724.
7. Davidson E.H., et al. A genomic regulatory network for development. Science 2002, 295:1669-1678.
8. Covert M.W., et al. Integrating metabolic transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 2008, 24:2044-2050.
9. Karr J.R., et al. A whole-cell computational model predicts phenotype from genotype. Cell 2012, 150:389-401.
10. Güell M., et al. Transcriptome complexity in a genome-reduced bacterium. Science 2009, 326:1268-1271.
11. Kühner S., et al. Proteome organization in a genome-reduced bacterium. Science 2009, 326:1235-1240.
12. Yus E., et al. Impact of genome reduction on bacterial metabolism and its regulation. Science 2009, 326:1263-1268.
13. Maier T., et al. Quantification of mRNA and protein and integration with protein turnover in a bacterium. Mol Syst Biol 2011, 7.
14. Ishii N., et al. Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 2007, 316:593-597.
15. Picotti P., et al. A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 2013, 494:266-270.
16. Miller C., et al. Dynamic transcriptome analysis measures rates of mRNA synthesis and decay in yeast. Mol Syst Biol 2011, 7.
17. Hoheisel J.D. Microarray technology: beyond transcript profiling and genotype analysis. Nat Rev Genet 2006, 7:200-210.
18. Wang Z., Gerstein M., Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009, 10:57-63.
19. Mahmood T., Yang P.-C. Western blot: technique theory and trouble shooting. N Am J Med Sci 2012, 4:429-434.
20. Gallagher S.R. Current protocols in molecular biology. Current Protocols in Molecular Biology 2006, (Chapter 10, Unit 10.2A). F.M. Ausubel (Ed.).
21. Furey T.S. ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet 2012, 13:840-852.
22. Roberts E., et al. Noise contributions in an inducible genetic switch: a whole-cell simulation study. PLoS Comput Biol 2011, 7:e1002010.
23. Taniguchi Y., et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 2010, 329:533-538.
24. Lee T.K., et al. A noisy paracrine signal determines the cellular NF-kappaB response to lipopolysaccharide. Sci Signal 2009, 2:ra65.
25. Tang Fuchou, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 2009, 6:377-382.
26. Ibá nez A.J., et al. Mass spectrometry-based metabolomics of single yeast cells. Proc Natl Acad Sci 2013, 110:8790-8794.
27. Caspi R., et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 2010, 38(Database issue):D473-D479.
28. Schomburg I., et al. BRENDA in 2013: integrated reactions kinetic data enzyme function data improved disease classification: new options and contents in BRENDA. Nucleic Acids Res 2013, 41(Database issue):D764-D772.
29. Wittig U., et al. SABIO-RK-database for biochemical reaction kinetics. Nucleic Acids Res 2011, 40:D790-D796.
30. Barrett T., et al. NCBI GEO: archive for functional genomics data sets-update. Nucleic acids research 2012, 41:D991-D995.
31. Karr J.R., et al. WholeCellKB: model organism databases for comprehensive whole-cell models. Nucleic Acids Res 2013, 41:D787-D792.
32. Finkel J., et al. Exploring the boundaries: gene and protein identification in biomedical text. BMC Bioinformatics 2005, 6(Suppl. 1):S5.
33. Thiele I., Palsson B.O. Reconstruction annotation jamborees: a community approach to systems biology. Mol Syst Biol 2010, 6:361.
34. Announcement. Reducing our irreproducibility. Nature 2013, 496:398.
35. Hartwell L.H., et al. From molecular to modular cell biology. Nature 1999, 402:C47-C52.
36. Bonneau R., et al. The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol 2006, 7:R36.
37. Carrera J., Rodrigo G., Jaramillo A. Model-based redesign of global transcription regulation. Nucleic Acids Res 2009, 37:e38.
38. CUDA Toolkit Documentation. http://docs.nvidia.com/cuda/index.html.
39. MPI Documents. http://www.mpi-forum.org/docs/.
40. Gunawardena J. Silicon dreams of cells into symbols. Nat Biotechnol 2012, 30:838-840.
41. Dror R.O., et al. Biomolecular simulation: a computational microscope for molecular biology. Annu Rev Biophys 2012, 41:429-452.
42. Silver R., et al. Neurotech for neuroscience: unifying concepts organizing principles and emerging tools. J Neurosci 2007, 27:11807-11819.
43. Lee R., Karr J.R., Covert M.W. WholeCellViz: data visualization for whole-cell models. BMC Bioinformatics 2013, 14:253.
44. Business Intelligence and Analytics Software. http://www.tableausoftware.com/.
45. Tableau Technology-Tableau Software. http://www.tableausoftware.com/products/technology.
46. Wong P.C., et al. The top 10 challenges in extreme-scale visual analytics. IEEE Comput Graph Appl 2012, 32:63-67.
47. Meyer M., et al. MulteeSum: a tool for comparative spatial and temporal gene expression data. IEEE Trans Visual Comput Graph 2010, 16:908-917.
48. Meyer M., et al. Pathline: a tool for comparative functional genomics. Comput Graph Forum 2010, 29:1043-1052.
49. Meyer M., Munzner T., Pfister H. MizBee: a multiscale synteny browser. IEEE Trans Visual Comput Graph 2009, 15:897-904.
50. Ashby S., et al. The Opportunities and Challenges of Exascale Computing-Summary Report of the Advanced Scientific Computing Advisory Committee (ASCAC) Subcommittee 2010, US Department of Energy Office of Science.
51. Kitano H. Computational systems biology. Am J Gastroenterol 2002, 420:206-210.
52. Sanghvi J.C., et al. Accelerated discovery via a whole-cell model. Nat Methods 2013, 10:1192-1195.
53. The MIT License (MIT)-Open Source Initiative. http://opensource.org/licenses/MIT.
54. Whole-cell parameter estimation DREAM challenge-syn1876068. https://www.synapse.org/#!Synapse:syn1876068.
55. Purcell O., et al. Towards a whole-cell modeling approach for synthetic biology. Chaos 2013, 23:025112.