Design of a bistable switch to control cellular uptake

Journal of the Royal Society Interface

Volumn 12, Issue 113, 2015, Pages

  Oyarzun, Diego A ^a   Chaves, Madalena ^b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b INRIA   (France)

Author keywords

Bistability; Cellular uptake; Dynamic metabolic engineering; Genetic circuits; Piecewise affine models; Synthetic biology

Indexed keywords

BIOLOGY; CELL CULTURE; CELLS; CYTOLOGY; DESIGN; GENE EXPRESSION; MACHINERY; METABOLIC ENGINEERING; METABOLISM; METABOLITES; OPTICAL BISTABILITY; RECONFIGURABLE HARDWARE;

ANALYSIS AND SIMULATION; CELL-TO-CELL COMMUNICATION; CELL-TO-CELL VARIABILITY; CELLULAR UPTAKE; CIRCUIT ARCHITECTURES; GENETIC CIRCUITS; PIECEWISE AFFINE MODELS; SYNTHETIC BIOLOGY;

ELECTRIC NETWORK ANALYSIS;

CELL COMMUNICATION; DNA STRUCTURE; EXPERIMENTAL MODEL; EXTRACELLULAR SPACE; FEEDBACK SYSTEM; HUMAN; HUMAN CELL; HYSTERESIS; MATHEMATICAL ANALYSIS; METABOLITE; MODEL; PHENOTYPE; PROMOTER REGION; STATISTICAL MODEL; STEM CELL CULTURE; SYNTHETIC BIOLOGY; BIOLOGICAL MODEL; CELL CULTURE TECHNIQUE; GENE EXPRESSION REGULATION; PHYSIOLOGY; PROCEDURES; SIGNAL TRANSDUCTION;

CELL CULTURE TECHNIQUES; GENE EXPRESSION REGULATION; MODELS, BIOLOGICAL; PROMOTER REGIONS, GENETIC; SIGNAL TRANSDUCTION; SYNTHETIC BIOLOGY;

EID: 84954170312     PISSN: 17425689     EISSN: 17425662     Source Type: Journal    
DOI: 10.1098/rsif.2015.0618     Document Type: Article

References (63)

1. Tyson JJ, Chen KC, Novak B. 2003 Sniffers, buzzers, toggles and blinkers: Dynamics of regulatory and signaling pathways in the cell. Curr. Opin. Cell Biol. 15, 221-231. (doi:10.1016/S0955-0674(03)00017-6)
2. Brandman O, Ferrell JE, Li R, Meyer T. 2005 Interlinked fast and slow positive feedback loops drive reliable cell decisions. Science 310, 496-498. (doi:10.1126/science.1113834)
3. Gardner TS, Cantor CR, Collins JJ. 2000 Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339-342. (doi:10.1038/35002131)
4. Atkinson MR, Savageau MA, Myers JT, Ninfa AJ. 2003 Development of genetic circuitry exhibiting toggle switch or oscillatory behavior in Escherichia coli. Cell 113, 597-607. (doi:10.1016/S0092-8674(03)00346-5)
5. Kramer BP, Fussenegger M. 2005 Hysteresis in a synthetic mammalian gene network. Proc. Natl Acad. Sci. USA 102, 9517-9522. (doi:10.1073/pnas. 0500345102)
6. Chen D, Arkin AP. 2012 Sequestration-based bistability enables tuning of the switching boundaries and design of a latch. Mol. Syst. Biol. 8, 620. (doi:10.1038/msb.2012.52)
7. Farmer WR, Liao JC. 2000 Improving lycopene production in Escherichia coli by engineering metabolic control. Nat. Biotechnol. 18, 533-537. (doi:10.1038/75398)
8. Zhang F, Carothers JM, Keasling JD. 2012 Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids. Nat. Biotechnol. 30, 354-359. (doi:10.1038/nbt.2149)
9. Oyarzún DA, Stan G-BV. 2013 Synthetic gene circuits for metabolic control: Design trade-offs and constraints. J. R. Soc. Interface 10, 20120671. (doi:10.1098/rsif.2012.0671)
10. Xu P, Li L, Zhang F, Stephanopoulos G, Koffas M. 2014 Improving fatty acids production by engineering dynamic pathway regulation and metabolic control. Proc. Natl Acad. Sci. USA 111, 11 299-11 304. (doi:10.1073/pnas.1406401111)
11. Fung E, Wong WW, Suen JK, Bulter T, Lee S-G, Liao JC. 2005 A synthetic gene-metabolic oscillator. Nature 435, 118-122. (doi:10.1038/nature03508)
12. Silva-Rocha R, de Lorenzo V. 2014 Engineering multicellular logic in bacteria with metabolic wires. ACS Synth. Biol. 3, 204-209. (doi:10.1021/sb400064y)
13. Brenner K, You L, Arnold FH. 2008 Engineering microbial consortia: A new frontier in synthetic biology. Trends Biotechnol. 26, 483-489. (doi:10. 1016/j.tibtech.2008.05.004)
14. Acar M, Becskei A, van Oudenaarden A. 2005 Enhancement of cellular memory by reducing stochastic transitions. Nature 435, 228-232. (doi:10.1038/nature03524)
15. Solopova A, van Gestel J, Weissing FJ, Bachmann H, Teusink B, Kok J, Kuipers OP. 2014 Bet-hedging during bacterial diauxic shift. Proc. Natl Acad. Sci. USA 111, 7427-7432. (doi:10.1073/pnas. 1320063111)
16. Kashiwagi A, Urabe I, Kaneko K, Yomo T. 2006 Adaptive response of a gene network to environmental changes by fitness-induced attractor selection. PLoS ONE 1, e49. (doi:10.1371/journal. pone.0000049)
17. Goldbeter A, Koshland DE. 1981 An amplified sensitivity arising from covalent modification in biological systems. Proc. Natl Acad. Sci. USA 78, 6840-6844. (doi:10.1073/pnas.78.11.6840)
18. Ciliberto A, Capuani F, Tyson JJ. 2007 Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation. PLoS Comput. Biol. 3, e45. (doi:10.1371/journal.pcbi. 0030045)
19. Ozbudak E, Thattai M, Lim H, Shraiman B, van Oudenaarden A. 2004 Multistability in the lactose utilization network of Escherichia coli. Nature 427, 737-740. (doi:10.1038/nature02298)
20. Cosentino C, Salerno L, Passanti A, Merola A, Bates DG, Amato F. 2012 Structural bistability of the GAL regulatory network and characterization of its domains of attraction. J. Comput. Biol. 19, 148-162. (doi:10.1089/cmb.2011.0251)
21. Oyarzún DA, Chaves M, Hoff-Hoffmeyer-Zlotnik M. 2012 Multistability and oscillations in genetic control of metabolism. J. Theoret. Biol. 295, 139-153. (doi:10.1016/j.jtbi.2011.11.017)
22. Casey R, de Jong H, Gouzé J-L. 2006 Piecewiselinear models of genetic regulatory networks: Equilibria and their stability. J. Math. Biol. 52, 27-56. (doi:10.1007/s00285-005-0338-2)
23. Dayarian A, Chaves M, Sontag ED, Sengupta AM. 2009 Shape, size, and robustness: Feasible regions in the parameter space of biochemical networks. PLoS Comput. Biol. 5, e1000256. (doi:10.1371/journal. pcbi.1000256)
24. Brockman IM, Prather KLJ. 2015 Dynamic metabolic engineering: New strategies for developing responsive cell factories. Biotechnol. J. 10, 1360-1369. (doi:10.1002/biot.201400422)
25. Liu D, Evans T, Zhang F. 2015 Applications and advances of metabolite biosensors for metabolic engineering. Metab. Eng. 31, 35-43. (doi:10.1016/j.ymben.2015.06.008)
26. Alper H, Fischer C, Nevoigt E, Stephanopoulos G. 2005 Tuning genetic control through promoter engineering. Proc. Natl Acad. Sci. USA 102, 12 678-12 683. (doi:10.1073/pnas.0504604102)
27. Salis HM, Mirsky EA, Voigt CA. 2009 Automated design of synthetic ribosome binding sites to control protein expression. Nat. Biotechnol. 27, 946-950. (doi:10.1038/nbt.1568)
28. Kaufman M, Soulé C, Thomas R. 2007 A new necessary condition on interaction graphs for multistationarity. J. Theoret. Biol. 248, 675-685. (doi:10.1016/j.jtbi.2007.06.016)
29. de Jong H, Gouzé J-L, Hernandez C, Page M, Sari T, Geiselmann J. 2004 Qualitative simulation of genetic regulatory networks using piecewise-linear models. Bull. Math. Biol. 2, 301-340. (doi:10.1016/j.bulm. 2003.08.010)
30. Ropers D, Baldazzi V, de Jong H. 2011 Model reduction using piecewise-linear approximations preserves dynamic properties of the carbon starvation response in Escherichia coli. IEEE/ACM Trans. Comput. Biol. Bioinform. 8, 166-181. (doi:10.1109/TCBB.2009.49)
31. Alon U. 2006 An introduction to systems biology: Design principles of biological circuits. London, UK: Chapman & Hall.
32. Sriram K, Soliman S, Fages F. 2009 Dynamics of the interlocked positive feedback loops explaining the robust epigenetic switching in Candida albicans. J. Theoret. Biol. 258, 71-88. (doi:10.1016/j.jtbi. 2009.01.008)
33. Domingo-Sananes MR, Novak B. 2010 Different effects of redundant feedback loops on a bistable switch. Chaos 20, 045120. (doi:10.1063/1.3526967)
34. Tiwari A, Igoshin OA. 2012 Coupling between feedback loops in autoregulatory networks affects bistability range, open-loop gain and switching times. Phys. Biol. 9, 055003. (doi:10.1088/1478-3975/9/5/055003)
35. Yagil G, Yagil E. 1971 On the relation between effector concentration and the rate of induced enzyme synthesis. Biophys. J. 11, 11-27. (doi:10. 1016/S0006-3495(71)86192-1)
36. Raj A, van Oudenaarden A. 2009 Single-molecule approaches to stochastic gene expression. Annu. Rev. Biophys. 38, 255-270. (doi:10.1146/annurev. biophys.37.032807.125928)
37. Labhsetwar P, Cole JA, Roberts E, Price ND, Luthey-Schulten ZA. 2013 Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population. Proc. Natl Acad. Sci. USA 110, 14 006-14 011. (doi:10.1073/pnas. 1222569110)
38. Oyarzún DA, Lugagne J-B, Stan G-B. 2015 Noise propagation in synthetic gene circuits for metabolic control. ACS Synth. Biol. 4, 116-125. (doi:10.1021/sb400126a)
39. Shahrezaei V, Swain PS. 2008 Analytical distributions for stochastic gene expression. Proc. Natl Acad. Sci. USA 105, 17 256-17 261. (doi:10. 1073/pnas.0803850105)
40. Taniguchi Y, Choi PJ, Li G-W, Chen H, Babu M, Hearn J, Emili A, Xie XS. 2010 Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533-538.
41. Kremling A, Geiselmann J, Ropers D, de Jong H. 2015 Understanding carbon catabolite repression in Escherichia coli using quantitative models. Trends Microbiol. 23, 99-109. (doi:10.1016/j.tim.2014. 11.002)
42. Kotte O, Volkmer B, Radzikowski JL, Heinemann M. 2014 Phenotypic bistability in Escherichia coli's central carbon metabolism. Mol. Syst. Biol. 10, 736. (doi:10.15252/msb.20135022)
43. van Heerden JH et al.2014 Lost in transition: Startup of glycolysis yields subpopulations of nongrowing cells. Science 343, 1245114. (doi:10. 1126/science.1245114)
44. Ford TJ, Silver PA. 2015 Synthetic biology expands chemical control of microorganisms. Curr. Opin. Chem. Biol. 28, 20-28. (doi:10.1016/j.cbpa. 2015.05.012)
45. Jagmann N, Philipp B. 2014 Reprint of design of synthetic microbial communities for biotechnological production processes. J. Biotechnol. 192, 293-301. (doi:10.1016/j.jbiotec.2014.11.005)
46. Shou W, Ram S, Vilar JMG. 2007 Synthetic cooperation in engineered yeast populations. Proc. Natl Acad. Sci. USA 104, 1877-1882. (doi:10.1073/pnas.0610575104)
47. Youk H, Lim WA. 2014 Secreting and sensing the same molecule allows cells to achieve versatile social behaviors. Science 343, 1242782. (doi:10. 1126/science.1242782)
48. Conradi C, Flockerzi D. 2012 Switching in mass action networks based on linear inequalities. SIAM J. Appl. Dyn. Syst. 11, 110-134. (doi:10.1137/10081722X)
49. Siegal-Gaskins D, Mejia-Guerra MK, Smith GD, Grotewold E. 2011 Emergence of switch-like behavior in a large family of simple biochemical networks. PLoS Comput. Biol. 7, e1002039. (doi:10. 1371/journal.pcbi.1002039)
50. Feliu E, Wiuf C. 2013 A computational method to preclude multistationarity in networks of interacting species. Bioinformatics 29, 2327-2334. (doi:10. 1093/bioinformatics/btt400)
51. Otero-Muras I, Banga JR, Alonso AA. 2012 Characterizing multistationarity regimes in biochemical reaction networks. PLoS ONE 7, e39194. (doi:10.1371/journal.pone.0039194)
52. Ang J, Harris E, Hussey BJ, Kil R, McMillen DR. 2013 Tuning response curves for synthetic biology. ACS Synth. Biol. 2, 547-567. (doi:10.1021/sb4000564)
53. Arpino JAJ, Hancock EJ, Anderson J, Barahona M, Stan G-BV, Papachristodoulou A, Polizzi K. 2013 Tuning the dials of synthetic biology. Microbiology 159(Pt 7), 1236-1253. (doi:10.1099/mic.0.067975-0)
54. Brewster RC, Jones DL, Phillips R. 2012 Tuning promoter strength through RNA polymerase binding site design in Escherichia coli. PLoS Comput. Biol. 8, e1002811. (doi:10.1371/journal.pcbi.1002811)
55. Egbert RG, Klavins E. 2012 Fine-tuning gene networks using simple sequence repeats. Proc. Natl Acad. Sci. USA 109, 16 817-16 822. (doi:10.1073/pnas.1205693109)
56. McGinness KE, Baker TA, Sauer RT. 2006 Engineering controllable protein degradation. Mol. Cell 22, 701-707. (doi:10.1016/j.molcel.2006.04.027)
57. Cardinale S, Arkin AP. 2012 Contextualizing context for synthetic biology-identifying causes of failure of synthetic biological systems. Biotechnol. J. 7, 856-866. (doi:10.1002/biot.201200085)
58. Weiße AY, Oyarzún DA, Danos V, Swain PS. 2015 Mechanistic links between cellular trade-offs, gene expression, growth. Proc. Natl Acad. Sci. USA 112, E1038-E1047. (doi:10.1073/pnas.1416533112)
59. Chubukov V, Gerosa L, Kochanowski K. 2014 Coordination of microbial metabolism. Nat. Rev. Microbiol. 12U, 327-340. (doi:10.1038/nrmicro3238)
60. Kotte O, Zaugg JB, Heinemann M. 2010 Bacterial adaptation through distributed sensing of metabolic fluxes. Mol. Syst. Biol. 6, 355. (doi:10.1038/msb. 2010.10)
61. Xu P, Wang W, Li L, Bhan N, Zhang F, Koffas MAG. 2014 Design and kinetic analysis of a hybrid promoter-regulator system for malonyl-CoA sensing in Escherichia coli. ACS Chem. Biol. 9, 451-458. (doi:10.1021/cb400623m)
62. Kuntz J, Oyarzún D, Stan G-B. 2014 Model reduction of genetic-metabolic networks via time scale separation. In A systems theoretic approach to systems and synthetic biology I: Models and system characterizations (eds VV Kulkarni, G-B Stan, K Raman), pp. 181-210. Berlin, Germany: Springer.
63. Dhooge A, Govaerts W, Kuznetsov YA. 2003 MATCONT: A MATLAB package for numerical bifurcation analysis of ODEs. ACM Trans. Math. Softw. 29, 141-164. (doi:10.1145/779359.779362)