1887
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
f
Towards a whole-cell modeling approach for synthetic biology
Rent:
Rent this article for
Access full text Article
/content/aip/journal/chaos/23/2/10.1063/1.4811182
1.
1. A. A. Cheng and T. K. Lu, “Synthetic biology: An emerging engineering discipline,” Annu. Rev. Biomed. Eng. 14, 155178 (2012).
http://dx.doi.org/10.1146/annurev-bioeng-071811-150118
2.
2. A. S. Khalil and J. J. Collins, “Synthetic biology: Applications come of age,” Nat. Rev. Genet. 11, 367379 (2010).
http://dx.doi.org/10.1038/nrg2775
3.
3. C. M. Agapakis and P. A. Silver, “Synthetic biology: Exploring and exploiting genetic modularity through the design of novel biological networks,” Mol. Biosyst. 5, 704713 (2009).
http://dx.doi.org/10.1039/b901484e
4.
4. A. Arkin, “Setting the standard in synthetic biology,” Nat. Biotechnol. 26, 771774 (2008).
http://dx.doi.org/10.1038/nbt0708-771
5.
5. D. Endy, “Foundations for engineering biology,” Nature 438, 449453 (2005).
http://dx.doi.org/10.1038/nature04342
6.
6. Y. Y. Chen, K. E. Galloway, and C. D. Smolke, “Synthetic biology: Advancing biological frontiers by building synthetic systems,” Genome Biol. 13, 240 (2012).
http://dx.doi.org/10.1186/gb-2012-13-2-240
7.
7. N. J. Guido et al., “A bottom-up approach to gene regulation,” Nature 439, 856860 (2006).
http://dx.doi.org/10.1038/nature04473
8.
8. M. Tigges, T. T. Marquez-Lago, J. Stelling, and M. Fussenegger, “A tunable synthetic mammalian oscillator,” Nature 457, 309312 (2009).
http://dx.doi.org/10.1038/nature07616
9.
9. J. Stricker et al., “A fast, robust and tunable synthetic gene oscillator,” Nature 456, 516U39 (2008).
http://dx.doi.org/10.1038/nature07389
10.
10. M. B. Elowitz and S. Leibler, “A synthetic oscillatory network of transcriptional regulators,” Nature 403, 335338 (2000).
http://dx.doi.org/10.1038/35002125
11.
11. M. Kaern, T. C. Elston, W. J. Blake, and J. J. Collins, “Stochasticity in gene expression: From theories to phenotypes,” Nat. Rev. Genet. 6, 451464 (2005).
http://dx.doi.org/10.1038/nrg1615
12.
12. M. Yoda, T. Ushikubo, W. Inoue, and M. Sasai, “Roles of noise in single and coupled multiple genetic oscillators,” J. Chem. Phys. 126, 115101 (2007).
http://dx.doi.org/10.1063/1.2539037
13.
13. P. Marguet, Y. Tanouchi, E. Spitz, C. Smith, and L. You, “Oscillations by minimal bacterial suicide circuits reveal hidden facets of host-circuit physiology,” PLoS ONE 5, e11909 (2010).
http://dx.doi.org/10.1371/journal.pone.0011909
14.
14. Y. Tanouchi, A. Pai, N. E. Buchler, and L. You, “Programming stress-induced altruistic death in engineered bacteria,” Mol. Syst. Biol. 8, 626 (2012).
http://dx.doi.org/10.1038/msb.2012.57
15.
15. A. Pai, Y. Tanouchi, C. H. Collins, and L. You, “Engineering multicellular systems by cell-cell communication,” Curr. Opin. Biotechnol. 20, 461470 (2009).
http://dx.doi.org/10.1016/j.copbio.2009.08.006
16.
16. D. Nevozhay, T. Zal, and G. Balázsi, “Transferring a synthetic gene circuit from yeast to mammalian cells,” Nat. Commun. 4, 1451 (2013).
http://dx.doi.org/10.1038/ncomms2471
17.
17. J. R. Karr et al., “A whole-cell computational model predicts phenotype from genotype,” Cell 150, 389401 (2012).
http://dx.doi.org/10.1016/j.cell.2012.05.044
18.
18. C. M. Fraser et al., “The minimal gene complement of Mycoplasma genitalium,” Science (N.Y.) 270, 397403 (1995).
http://dx.doi.org/10.1126/science.270.5235.397
19.
19. J. D. Orth, I. Thiele, and B. Ø. Palsson, “What is flux balance analysis?Nat. Biotechnol. 28, 245248 (2010).
http://dx.doi.org/10.1038/nbt.1614
20.
20. G. Kudla, A. W. Murray, D. Tollervey, and J. B. Plotkin, “Coding-sequence determinants of gene expression in Escherichia coli,” Science (N.Y.) 324, 255258 (2009).
http://dx.doi.org/10.1126/science.1170160
21.
21. M. Lewis, “The lac repressor,” C. R. Biol. 328, 521548 (2005).
http://dx.doi.org/10.1016/j.crvi.2005.04.004
22.
22. T. S. Gardner, C. R. Cantor, and J. J. Collins, “Construction of a genetic toggle switch in Escherichia coli,” Nature 403, 339342 (2000).
http://dx.doi.org/10.1038/35002131
23.
23. M. R. Atkinson, M. A. Savageau, J. T. Myers, and A. J. Ninfa, “Development of genetic circuitry exhibiting toggle switch or oscillatory behavior in Escherichia coli,” Cell 113, 597607 (2003).
http://dx.doi.org/10.1016/S0092-8674(03)00346-5
24.
24.See supplementary material at http://dx.doi.org/10.1063/1.4811182 for lacI sequences used and additional Goodwin oscillator simulations. [Supplementary Material]
25.
25. C. Berens and D. Porschke, “Recognition of operator DNA by Tet repressor,” J. Phys. Chem. B 117, 18801885 (2013).
http://dx.doi.org/10.1021/jp311877t
26.
26. S. J. Remington, “Green fluorescent protein: A perspective,” Protein Sci. 20, 15091519 (2011).
http://dx.doi.org/10.1002/pro.684
27.
27. R. Schleif, “AraC protein, regulation of the l-arabinose operon in Escherichia coli, and the light switch mechanism of AraC action,” FEMS Microbiol. Rev. 34, 779796 (2010).
http://dx.doi.org/10.1111/j.1574-6976.2010.00226.x
28.
28. A. Grote et al., “JCat: A novel tool to adapt codon usage of a target gene to its potential expression host,” Nucleic Acids Res. 33, W526W531 (2005).
http://dx.doi.org/10.1093/nar/gki376
29.
29. M. Welch et al., “Design parameters to control synthetic gene expression in Escherichia coli,” PLoS ONE 4, e7002 (2009).
http://dx.doi.org/10.1371/journal.pone.0007002
30.
30. B. C. Goodwin, Temporal Organization in Cells. A Dynamic Theory of Cellular Control Processes (Academic Press, London, 1963).
31.
31. O. Purcell, N. J. Savery, C. S. Grierson, and M. Di Bernardo, “A comparative analysis of synthetic genetic oscillators,” J. R. Soc., Interface 7, 15031524 (2010).
http://dx.doi.org/10.1098/rsif.2010.0183
32.
32. T. Makino, G. Skretas, and G. Georgiou, “Strain engineering for improved expression of recombinant proteins in bacteria,” Microb. Cell Factories 10, 32 (2011).
http://dx.doi.org/10.1186/1475-2859-10-32
33.
33. L. Ma, G. Zhang, and M. P. Doyle, “Green fluorescent protein labeling of listeria, salmonella, and Escherichia coli O157:H7 for safety-related studies,” PLoS ONE 6, e18083 (2011).
http://dx.doi.org/10.1371/journal.pone.0018083
34.
34. H. Dong, L. Nilsson, and C. G. Kurland, “Gratuitous overexpression of genes in Escherichia coli leads to growth inhibition and ribosome destruction,” J. Bacteriol. 177, 14971504 (1995).
35.
35. R. L. Gourse, T. Gaal, M. S. Bartlett, J. A. Appleman, and W. Ross, “rRNA transcription and growth rate-dependent regulation of ribosome synthesis in Escherichia coli,” Annu. Rev. Microbiol. 50, 645677 (1996).
http://dx.doi.org/10.1146/annurev.micro.50.1.645
36.
36. P. Jorgensen et al., “A dynamic transcriptional network communicates growth potential to ribosome synthesis and critical cell size,” Genes Develop. 18, 24912505 (2004).
http://dx.doi.org/10.1101/gad.1228804
37.
37. S. Klumpp and T. Hwa, “Growth-rate-dependent partitioning of RNA polymerases in bacteria,” Proc. Natl. Acad. Sci. U.S.A. 105, 2024520250 (2008).
http://dx.doi.org/10.1073/pnas.0804953105
38.
38. T. Moss, “At the crossroads of growth control; making ribosomal RNA,” Curr. Opin. Genet. Develop. 14, 210217 (2004).
http://dx.doi.org/10.1016/j.gde.2004.02.005
39.
39. C. Lou et al., “Synthesizing a novel genetic sequential logic circuit: a push-on push-off switch,” Mol. Syst. Biol. 6, 350 (2010).
http://dx.doi.org/10.1038/msb.2010.2
40.
40. M. F. Balish, R. T. Santurri, A. M. Ricci, K. K. Lee, and D. C. Krause, “Localization of Mycoplasma pneumoniae cytadherence-associated protein HMW2 by fusion with green fluorescent protein: Implications for attachment organelle structure,” Mol. Microbiol. 47(1), 4960 (2003).
http://dx.doi.org/10.1046/j.1365-2958.2003.03282.x
41.
41. M. Breton et al., “First report of a tetracycline-inducible gene expression system for mollicutes,” Microbiology 156, 198205 (2010).
http://dx.doi.org/10.1099/mic.0.034074-0
42.
42. D. G. Gibson et al., “Creation of a bacterial cell controlled by a chemically synthesized genome,” Science (N.Y.) 329, 5256 (2010).
http://dx.doi.org/10.1126/science.1190719
43.
43. A. Burger, A. M. Walczak, and P. G. Wolynes, “Abduction and asylum in the lives of transcription factors,” Proc. Natl. Acad. Sci. U.S.A. 107, 40164021 (2010).
http://dx.doi.org/10.1073/pnas.0915138107
44.
44. T.-H. Lee and N. Maheshri, “A regulatory role for repeated decoy transcription factor binding sites in target gene expression,” Mol. Syst. Biol. 8, 576 (2012).
http://dx.doi.org/10.1038/msb.2012.7
45.
45. B. Xia et al., “Developer's and user's guide to Clotho v2.0 A software platform for the creation of synthetic biological systems,” Methods Enzymol. 498, 97135 (2011).
http://dx.doi.org/10.1016/B978-0-12-385120-8.00005-X
46.
46. M. J. Czar, Y. Cai, and J. Peccoud, “Writing DNA with GenoCAD™,” Nucl. Acids Res. 37, W40W47 (2009).
http://dx.doi.org/10.1093/nar/gkp361
47.
47. Y. Benenson, “Biomolecular computing systems: Principles, progress and potential,” Nat. Rev. Genet. 13, 455468 (2012).
http://dx.doi.org/10.1038/nrg3197
48.
48. R. Daniel, J. R. Rubens, R. Sarpeshkar, and T. K. Lu, “Synthetic analog computation in living cells,” Nature 497(7451), 619623 (2013).
http://dx.doi.org/10.1038/nature12148
49.
49. Z. Xie, L. Wroblewska, L. Prochazka, R. Weiss, and Y. Benenson, “Multi-input RNAi-based logic circuit for identification of specific cancer cells,” Science (N.Y.) 333, 13071311 (2011).
http://dx.doi.org/10.1126/science.1205527
50.
50. F. Isaacs, D. Dwyer, and J. Collins, “RNA synthetic biology,” Nat. Biotechnol. 24(5), 545554 (2006).
http://dx.doi.org/10.1038/nbt1208
51.
51. P. Siuti, J. Yazbek, and T. K. Lu, “Synthetic circuits integrating logic and memory in living cells,” Nat. Biotechnol 31(5), 448452 (2013).
http://dx.doi.org/10.1038/nbt.2510
52.
52. E. Gur and R. T. Sauer, “Evolution of the ssrA degradation tag in Mycoplasma: Specificity switch to a different protease,” Proc. Natl. Acad. Sci. U.S.A. 105, 1611316118 (2008).
http://aip.metastore.ingenta.com/content/aip/journal/chaos/23/2/10.1063/1.4811182
Loading
/content/aip/journal/chaos/23/2/10.1063/1.4811182
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/aip/journal/chaos/23/2/10.1063/1.4811182
2013-06-13
2014-09-30

Abstract

Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of , we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.

Loading

Full text loading...

/deliver/fulltext/aip/journal/chaos/23/2/1.4811182.html;jsessionid=o1ai4gchl5e3.x-aip-live-06?itemId=/content/aip/journal/chaos/23/2/10.1063/1.4811182&mimeType=html&fmt=ahah&containerItemId=content/aip/journal/chaos
true
true
This is a required field
Please enter a valid email address
This feature is disabled while Scitation upgrades its access control system.
This feature is disabled while Scitation upgrades its access control system.
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Towards a whole-cell modeling approach for synthetic biology
http://aip.metastore.ingenta.com/content/aip/journal/chaos/23/2/10.1063/1.4811182
10.1063/1.4811182
SEARCH_EXPAND_ITEM