Advertisement

Modeling and Analyzing the Flow of Molecular Machines in Gene Expression

  • Yoram Zarai
  • Michael Margaliot
  • Tamir Tuller
Chapter
Part of the RNA Technologies book series (RNATECHN)

Abstract

Gene expression is a fundamental cellular process by which proteins are synthesized based on the information encoded in the genetic material. During this process, macromolecules such as ribosomes or RNA polymerases scan the genetic material in a sequential manner. We review several deterministic, continuous-time models for the flow of such macromolecules. These models are both easy to simulate and amenable to rigorous mathematical analysis. We demonstrate how these models can be used to predict the expression levels of genes and to study important biological phenomena such as competition for finite resources, sensitivity of gene expression to various biophysical factors, and optimization of the protein production rate.

Keywords

Ribosome flow model Stability Entrainment Biotechnology Gene expression Synthetic biology 

References

  1. Adleman LM (1994) Molecular computation of solutions to combinatorial problems. Science 266:1021–1024PubMedCrossRefPubMedCentralGoogle Scholar
  2. Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P (2007) Molecular biology of the cell, 5th edn. Garland Science, New YorkCrossRefGoogle Scholar
  3. Aminzare Z, Sontag ED (2014) Contraction methods for nonlinear systems: a brief introduction and some open problems. In: Proceedings of 53rd IEEE conference on decision and control. Los Angeles, CA, pp 3835–3847Google Scholar
  4. Ben-Yehezkel T, Atar S, Zur H, Diament A, Goz E, Marx T, Cohen R, Dana A, Feldman A, Shapiro E, Tuller T (2015) Rationally designed, heterologous S. cerevisiae transcripts expose novel expression determinants. RNA Biol 12:972–984PubMedPubMedCentralCrossRefGoogle Scholar
  5. Binnie C, Cossar J, Stewart D (1997) Heterologous biopharmaceutical protein expression in streptomyces. Trends Biotechnol 15(8):315–320PubMedCrossRefPubMedCentralGoogle Scholar
  6. Blythe RA, Evans MR (2007) Nonequilibrium steady states of matrix-product form: a solver’s guide. J Phys A Math Theor 40(46):R333–R441CrossRefGoogle Scholar
  7. Bonnin P, Kern N, Young NT, Stansfield I, Romano MC (2017) Novel mRNA-specific effects of ribosome drop-off on translation rate and polysome profile. PLoS Comput Biol 13(5):e1005555PubMedPubMedCentralCrossRefGoogle Scholar
  8. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  9. Brackley CA, Romano MC, Thiel M (2011) The dynamics of supply and demand in mRNA translation. PLoS Comput Biol 7(10):e1002203PubMedPubMedCentralCrossRefGoogle Scholar
  10. Brauer F (2008) Compartmental models in epidemiology. In: Brauer F, van den Driessche P, Wu J (eds) Mathematical epidemiology. Lecture notes in mathematics, vol 1945. Springer, Berlin, pp 19–79CrossRefGoogle Scholar
  11. Ceroni F, Algar R, Stan GB, Ellis T (2015) Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat Methods 12:415–418PubMedCrossRefGoogle Scholar
  12. Chadani Y, Ono K, Ozawa S, Takahashy Y, Takay K, Nanamiya H, Tozawa Y, Kutsukake K, Abo T (2010) Ribosome rescue by Escherichia coli ArfA (YhdL) in the absence of trans-translation systems. Mol Microbiol 78:796–808PubMedCrossRefGoogle Scholar
  13. Chandar N, Viselli S (2012) Cell and molecular biology. Wolters Kluwer Health, PhiladelphiaGoogle Scholar
  14. Cheung ACM, Cramer P (2011) Structural basis of RNA polymerase II backtracking, arrest and reactivation. Nature 471(7337):249–253PubMedCrossRefGoogle Scholar
  15. Chou T, Lakatos G (2004) Clustered bottlenecks in mRNA translation and protein synthesis. Phys Rev Lett 93(19):198101PubMedCrossRefGoogle Scholar
  16. Chou T, Mallick K, Zia RKP (2011) Non-equilibrium statistical mechanics: from a paradigmatic model to biological transport. Rep Prog Phys 74:116601CrossRefGoogle Scholar
  17. Churchman LS, Weissman JS (2011) Nascent transcript sequencing visualizes transcription at nucleotide resolution. Nature 469(7330):368–373PubMedCrossRefGoogle Scholar
  18. Ciandrini L, Stansfield I, Romano M (2013) Ribosome traffic on mRNAs maps to gene ontology: genome-wide quantification of translation initiation rates and polysome size regulation. PLoS Comput Biol 9(1):e1002866PubMedPubMedCentralCrossRefGoogle Scholar
  19. Cohen E, Zafrir Z, Tuller T (2017) A code for transcription elongation speed. RNA Biol 1–14. https://doi.org/10.1080/15476286.2017.1384118
  20. Coleman J, Papamichail D, Skiena S, Futcher B, Wimmer E, Mueller S (2008) Virus attenuation by genome-scale changes in codon pair bias. Science 320:1784–1787PubMedPubMedCentralCrossRefGoogle Scholar
  21. Crick F (1970) Central dogma of molecular biology. Nature 227(5258):561–563PubMedCrossRefGoogle Scholar
  22. Dana A, Tuller T (2012) Efficient manipulations of synonymous mutations for controlling translation rate–an analytical approach. J Comput Biol 19:200–231PubMedCrossRefGoogle Scholar
  23. Dana A, Tuller T (2014a) The effect of tRNA levels on decoding times of mRNA codons. Nucleic Acids Res 42(14):9171–9181PubMedPubMedCentralCrossRefGoogle Scholar
  24. Dana A, Tuller T (2014b) Mean of the typical decoding rates: a new translation efficiency index based on the analysis of ribosome profiling data. G3 5(1):73–80PubMedCrossRefGoogle Scholar
  25. Derrida B (1998) An exactly soluble non-equilibrium system: the asymmetric simple exclusion process. Phys Rep 301(1):65–83CrossRefGoogle Scholar
  26. Derrida B, Domany E, Mukamel D (1992) An exact solution of a one-dimensional asymmetric exclusion model with open boundaries. J Stat Phys 69(3–4):667–687CrossRefGoogle Scholar
  27. Derrida B, Evans MR, Hakim V, Pasquier V (1993) Exact solution of a 1D asymmetric exclusion model using a matrix formulation. J Phys A Math Gen 26(7):1493CrossRefGoogle Scholar
  28. Devi G (2006) siRNA-based approaches in cancer therapy. Cancer Gene Ther 13(9):819–829PubMedCrossRefGoogle Scholar
  29. Dong J, Schmittmann B, Zia RK (2007a) Towards a model for protein production rates. J Stat Phys 128(1–2):21–34CrossRefGoogle Scholar
  30. Dong JJ, Schmittmann B, Zia RKP (2007b) Inhomogeneous exclusion processes with extended objects: the effect of defect locations. Phys Rev E 76:051113CrossRefGoogle Scholar
  31. Edri S, Gazit E, Cohen E, Tuller T (2014) The RNA polymerase flow model of gene transcription. IEEE Trans Biomed Circuits Syst 8(1):54–64PubMedCrossRefPubMedCentralGoogle Scholar
  32. Evans M, Blythe R (2002) Nonequilibrium dynamics in low-dimensional systems. Physica A 313(1):110–152CrossRefGoogle Scholar
  33. Fabian M, Sonenberg N, Filipowicz W (2010) Regulation of mRNA translation and stability by microRNAs. Annu Rev Biochem 79:351–379PubMedCrossRefPubMedCentralGoogle Scholar
  34. Filipowicz W, Bhattacharyya S, Sonenberg N (2008) Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet 9(2):102–114PubMedCrossRefPubMedCentralGoogle Scholar
  35. Ghildiyal M, Zamore P (2009) Small silencing RNAs: an expanding universe. Nat Rev Genet 10:94–108PubMedPubMedCentralCrossRefGoogle Scholar
  36. Gilchrist MA, Wagner A (2006) A model of protein translation including codon bias, nonsense errors, and ribosome recycling. J Theor Biol 239(4):417–434PubMedCrossRefPubMedCentralGoogle Scholar
  37. Goz E, Tuller T (2015) Widespread signatures of local mRNA folding structure selection in four Dengue virus serotypes. BMC Genomics 16(10):S4PubMedPubMedCentralCrossRefGoogle Scholar
  38. Greulich P, Ciandrini L, Allen RJ, Romano MC (2012) Mixed population of competing totally asymmetric simple exclusion processes with a shared reservoir of particles. Phys Rev E 85:011142CrossRefGoogle Scholar
  39. Gyorgy A, Jimenez JI, Yazbek J, Huang H, Chung H, Weiss R, Del Vecchio D (2015) Isocost lines describe the cellular economy of genetic circuits. Biophys J 109:639–46PubMedPubMedCentralCrossRefGoogle Scholar
  40. Holza M, Fahrb A (2001) Compartment modeling. Adv Drug Deliv Rev 48:249–264CrossRefGoogle Scholar
  41. Horn RA, Johnson CR (2013) Matrix analysis. Cambridge University Press, CambridgeGoogle Scholar
  42. Ingolia NT (2014) Ribosome profiling: new views of translation, from single codons to genome scale. Nat Rev Genet 15(3):205–213PubMedCrossRefPubMedCentralGoogle Scholar
  43. Ingolia NT, Ghaemmaghami S, Newman JR, Weissman JS (2009) Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324(5924):218–223PubMedPubMedCentralCrossRefGoogle Scholar
  44. Inui M, Martello G, Piccolo S (2010) MicroRNA control of signal transduction. Nat Rev Mol Cell Biol 11(4):252–263PubMedCrossRefPubMedCentralGoogle Scholar
  45. Iwasaki S, Ingolia NT (2016) Seeing translation. Science 352(6292):1391–1392PubMedCrossRefPubMedCentralGoogle Scholar
  46. Jacquez JA (1996) Compartmental analysis in biology and medicine, 3rd edn. BioMedware, Ann Arbor, MIGoogle Scholar
  47. Jacquez JA, Simon CP (1993) Qualitative theory of compartmental systems. SIAM Rev 35(1):43–79CrossRefGoogle Scholar
  48. Jens M, Rajewsky N (2015) Competition between target sites of regulators shapes post-transcriptional gene regulation. Nat Rev Genet 16(2):113–126PubMedCrossRefGoogle Scholar
  49. Johansson M, Chen J, Tsai A, Kornberg G, Puglisi J (2014) Sequence-dependent elongation dynamics on macrolide-bound ribosomes. Cell Rep 7:1534–1546PubMedPubMedCentralCrossRefGoogle Scholar
  50. Keiler K (2015) Mechanisms of ribosome rescue in bacteria. Nat Rev Microbiol 13:285–297PubMedCrossRefPubMedCentralGoogle Scholar
  51. Keiler K, Waller P, Sauer R (1996) Role of a peptide tagging system in degradation of proteins synthesized from damaged messenger RNA. Science 271:990–993PubMedCrossRefPubMedCentralGoogle Scholar
  52. Kolomeisky AB (1998) Asymmetric simple exclusion model with local inhomogeneity. J Phys A Math Gen 31(4):1153CrossRefGoogle Scholar
  53. Kozak M (1986) Point mutations define a sequence flanking the aug initiator codon that modulates translation by eukaryotic ribosomes. Cell 44(2):283–92PubMedCrossRefPubMedCentralGoogle Scholar
  54. Kurland C (1992) Translational accuracy and the fitness of bacteria. Ann Rev Genet 26:29–50PubMedCrossRefPubMedCentralGoogle Scholar
  55. Kurland C, Mikkola R (1993) The impact of nutritional state on the microevolution of ribosomes. In: Kjelleberg S (ed) Starvation in bacteria. Plenum Press, New York, NY, pp 225–238CrossRefGoogle Scholar
  56. Lakatos G, Chou T (2003) Totally asymmetric exclusion processes with particles of arbitrary size. J Phys A Math Gen 36:20272041CrossRefGoogle Scholar
  57. Lodish HF (1974) Model for the regulation of mRNA translation applied to haemoglobin synthesis. Nature 251:385–388PubMedCrossRefPubMedCentralGoogle Scholar
  58. Lohmiller W, Slotine JJE (1998) On contraction analysis for non-linear systems. Automatica 34:683–696CrossRefGoogle Scholar
  59. MacDonald CT, Gibbs JH (1969) Concerning the kinetics of polypeptide synthesis on polyribosomes. Biopolymers 7(5):707–725CrossRefGoogle Scholar
  60. MacDonald CT, Gibbs JH, Pipkin AC (1968) Kinetics of biopolymerization on nucleic acid templates. Biopolymers 6:1–25PubMedCrossRefPubMedCentralGoogle Scholar
  61. Margaliot M, Coogan S (2017) Approximating the frequency response of contractive systems. CoRR abs/1702.06576. http://arxiv.org/abs/1702.06576
  62. Margaliot M, Tuller T (2012) Stability analysis of the ribosome flow model. IEEE/ACM Trans Comput Biol Bioinform 9:1545–1552PubMedCrossRefPubMedCentralGoogle Scholar
  63. Margaliot M, Tuller T (2013) Ribosome flow model with positive feedback. J R Soc Interface 10:20130267PubMedPubMedCentralCrossRefGoogle Scholar
  64. Margaliot M, Sontag ED, Tuller T (2014) Entrainment to periodic initiation and transition rates in a computational model for gene translation. PLoS ONE 9(5):e96039PubMedPubMedCentralCrossRefGoogle Scholar
  65. Margaliot M, Sontag ED, Tuller T (2016) Contraction after small transients. Automatica 67:178–184CrossRefGoogle Scholar
  66. Margaliot M, Grüne L, Kriecherbauer T (2018) Entrainment in the master equation. Roy Soc Open Sci 5(4).  https://doi.org/10.1098/rsos.172157
  67. Mayer A, Churchman L (2016) Genome-wide profiling of rna polymerase transcription at nucleotide resolution in human cells with native elongating transcript sequencing. Nat Protoc 11:813–833PubMedPubMedCentralCrossRefGoogle Scholar
  68. Mills EW, Green R (2017) Ribosomopathies: there’s strength in numbers. Science 358(6363).  https://doi.org/10.1126/science.aan2755
  69. Moks T, Abrahmsen L, Holmgren E, Bilich M, Olsson A, Pohl G, Sterky C, Hultberg H, Josephson SA (1987) Expression of human insulin-like growth factor I in bacteria: use of optimized gene fusion vectors to facilitate protein purification. Biochemistry 26(17):5239–5244PubMedCrossRefPubMedCentralGoogle Scholar
  70. Myasnikov AG, Kundhavai Natchiar S, Nebout M, Hazemann I, Imbert V, Khatter H, Peyron JF, Klaholz BP (2016) Structure-function insights reveal the human ribosome as a cancer target for antibiotics. Nat Commun 7:12856PubMedPubMedCentralCrossRefGoogle Scholar
  71. Newhart A, Janicki SM (2014) Seeing is believing: Visualizing transcriptional dynamics in single cells. J Cell Physiol 229(3):259–265PubMedPubMedCentralCrossRefGoogle Scholar
  72. Nikolaev EV, Rahi SJ, Sontag E (2017) Subharmonics and chaos in simple periodically-forced biomolecular models. bioRxiv p 145201Google Scholar
  73. Nudler E (2012) RNA polymerase backtracking in gene regulation and genome instability. Cell 149(7):1438–1445PubMedCrossRefPubMedCentralGoogle Scholar
  74. Perez JT, Pham AM, Lorini MH, Chua MA, Steel J, tenOever BR (2009) MicroRNA-mediated species-specific attenuation of influenza A virus. Nat Biotechnol 27(6):572–576PubMedCrossRefPubMedCentralGoogle Scholar
  75. Pinkoviezky I, Gov N (2013) Transport dynamics of molecular motors that switch between an active and inactive state. Phys Rev E 88(2):022714CrossRefGoogle Scholar
  76. Poker G, Zarai Y, Margaliot M, Tuller T (2014) Maximizing protein translation rate in the nonhomogeneous ribosome flow model: a convex optimization approach. J R Soc Interface 11(100):20140713PubMedPubMedCentralCrossRefGoogle Scholar
  77. Raveh A, Zarai Y, Margaliot M, Tuller T (2015) Ribosome flow model on a ring. IEEE/ACM Trans Comput Biol Bioinform 12(6):1429–1439PubMedCrossRefPubMedCentralGoogle Scholar
  78. Raveh A, Margaliot M, Sontag E, Tuller T (2016) A model for competition for ribosomes in the cell. J R Soc Interface 13(116):20151062PubMedPubMedCentralCrossRefGoogle Scholar
  79. Reuveni S, Meilijson I, Kupiec M, Ruppin E, Tuller T (2011) Genome-scale analysis of translation elongation with a ribosome flow model. PLoS Comput Biol 7(9):e1002127PubMedPubMedCentralCrossRefGoogle Scholar
  80. Rice GA, Chamberlin MJ, Kane CM (1993) Contacts between mammalian RNA polymerase II and the template DNA in a ternary elongation complex. Nucleic Acids Res 21(1):113–118PubMedPubMedCentralCrossRefGoogle Scholar
  81. Richter JD, Smith LD (1981) Differential capacity for translation and lack of competition between mRNAs that segregate to free and membrane-bound polysomes. Cell 27:183–191PubMedCrossRefGoogle Scholar
  82. Romanos M, Scorer C, Clare J (1992) Foreign gene expression in yeast: a review. Yeast 8(6):423–488PubMedCrossRefGoogle Scholar
  83. Russo G, di Bernardo M, Sontag ED (2010) Global entrainment of transcriptional systems to periodic inputs. PLoS Comput Biol 6:e1000739PubMedPubMedCentralCrossRefGoogle Scholar
  84. Salis H, Mirsky E, Voigt C (2009) Automated design of synthetic ribosome binding sites to control protein expression. Nat Biotechnol 27(10):946–950PubMedPubMedCentralCrossRefGoogle Scholar
  85. Schadschneider A, Chowdhury D, Nishinari K (2011) Stochastic transport in complex systems: from molecules to vehicles. Elsevier, AmsterdamGoogle Scholar
  86. Shapiro E (2012) A mechanical turing machine: blueprint for a biomolecular computer. Interface Focus 2(4):497–503PubMedPubMedCentralCrossRefGoogle Scholar
  87. Sharp PM, Emery LR, Zeng K (2010) Forces that influence the evolution of codon bias. Philos Trans R Soc Lond B 365(1544):1203–1212CrossRefGoogle Scholar
  88. Shaw LB, Zia RK, Lee KH (2003) Totally asymmetric exclusion process with extended objects: a model for protein synthesis. Phys Rev E Stat Nonlin Soft Matter Phys 68:021910PubMedCrossRefGoogle Scholar
  89. Shaw LB, Kolomeisky AB, Lee KH (2004a) Local inhomogeneity in asymmetric simple exclusion processes with extended objects. J Phys A Math Gen 37(6):2105CrossRefGoogle Scholar
  90. Shaw LB, Sethna JP, Lee KH (2004b) Mean-field approaches to the totally asymmetric exclusion process with quenched disorder and large particles. Phys Rev E 70(2):021901CrossRefGoogle Scholar
  91. Shoemaker C, Eyler D, Green R (2010) Dom34:Hbs1 promotes subunit dissociation and peptidyl-tRNA drop-off to initiate no-go decay. Science 330(6002):369–372PubMedPubMedCentralCrossRefGoogle Scholar
  92. Sin C, Chiarugi D, Valleriani A (2016) Quantitative assessment of ribosome drop-off in E. coli. Nucleic Acids Res 44(6):2528–2537PubMedPubMedCentralCrossRefGoogle Scholar
  93. Smith HL (1995) Monotone Dynamical systems: an introduction to the theory of competitive and cooperative systems. Mathematical surveys and monographs, vol 41. American Mathematical Society, Providence, RIGoogle Scholar
  94. Spitzer F (1970) Interaction of Markov processes. Adv Math 5:246–290CrossRefGoogle Scholar
  95. Subramaniam A, Zid B, O’Shea E (2014) An integrated approach reveals regulatory controls on bacterial translation elongation. Cell 159(5):1200–1211PubMedPubMedCentralCrossRefGoogle Scholar
  96. Tavazoie SF, Alarcón C, Oskarsson T, Padua D, Wang Q, Bos PD, Gerald WL, Massagué J (2008) Endogenous human microRNAs that suppress breast cancer metastasis. Nature 451(7175):147–152PubMedPubMedCentralCrossRefGoogle Scholar
  97. Tripathy G, Barma M (1998) Driven lattice gases with quenched disorder: Exact results and different macroscopic regimes. Phys Rev E 58:1911–1926CrossRefGoogle Scholar
  98. Tuller T, Zur H (2015) Multiple roles of the coding sequence 5’ end in gene expression regulation. Nucleic Acids Res 43(1):13–28PubMedCrossRefGoogle Scholar
  99. Tuller T, Carmi A, Vestsigian K, Navon S, Dorfan Y, Zaborske J, Pan T, Dahan O, Furman I, Pilpel Y (2010) An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell 141(2):344–354PubMedCrossRefGoogle Scholar
  100. Tuller T, Veksler I, Gazit N, Kupiec M, Ruppin E, Ziv M (2011) Composite effects of gene determinants on the translation speed and density of ribosomes. Genome Biol 12(11):R110PubMedPubMedCentralCrossRefGoogle Scholar
  101. Turing A (2004) Intelligent machinery. In: Copeland BJ (ed) The essential turing. Clarendon Press, Oxford, pp 411–432Google Scholar
  102. Vind J, Sorensen MA, Rasmussen MD, Pedersen S (1993) Synthesis of proteins in Escherichia coli is limited by the concentration of free ribosomes: expression from reporter genes does not always reflect functional mRNA levels. J Mol Biol 231:678–688PubMedCrossRefGoogle Scholar
  103. Wang Q, Contag C, Ilves H, Johnston B, Kaspar R (2005) Small hairpin RNAs efficiently inhibit hepatitis C IRES-mediated gene expression in human tissue culture cells and a mouse model. Mol Ther 12(3):562–568PubMedCrossRefGoogle Scholar
  104. Zadeh LA, Desoer CA (1963) Linear system theory. McGraw-Hill, New YorkGoogle Scholar
  105. Zaher S, Green R (2009) Quality control by the ribosome following peptide bond formation. Nature 457:161–166PubMedCrossRefGoogle Scholar
  106. Zaher H, Green R (2011) A primary role for elastase factor 3 in quality control during translation elongation in Escherichia coli. Cell 147:396–408PubMedPubMedCentralCrossRefGoogle Scholar
  107. Zarai Y, Tuller T (2018) Oscillatory behavior at the translation level induced by mRNA levels oscillations due to finite intracellular resources. PLoS Comput Biol 14(4):e1006055PubMedPubMedCentralCrossRefGoogle Scholar
  108. Zarai Y, Margaliot M, Kolomeisky AB (2017a) A deterministic model for one-dimensional excluded flow with local interactions. PLoS ONE 12(8):1–23Google Scholar
  109. Zarai Y, Margaliot M, Sontag ED, Tuller T (2017b) Controllability analysis and control synthesis for the ribosome flow model. IEEE/ACM Trans Comput Biol Bioinform (to appear)Google Scholar
  110. Zarai Y, Margaliot M, Tuller T (2017c) A deterministic mathematical model for bidirectional excluded flow with langmuir kinetics. PLoS ONE 12(8):e0182178PubMedPubMedCentralCrossRefGoogle Scholar
  111. Zarai Y, Margaliot M, Tuller T (2017d) Optimal down regulation of mRNA translation. Sci Rep 7:41243PubMedPubMedCentralCrossRefGoogle Scholar
  112. Zarai Y, Margaliot M, Tuller T (2017e) Ribosome flow model with extended objects. J R Soc Interface 14(135)Google Scholar
  113. Zarai Y, Ovseevich A, Margaliot M (2017f) Optimal translation along a circular mRNA. Sci Rep 7:9464PubMedPubMedCentralCrossRefGoogle Scholar
  114. Zhang L, Yang N, Mohamed-Hadley A, Rubin S, Coukos G (2003) Vector-based RNAi, a novel tool for isoform-specific knock-down of VEGF and anti-angiogenesis gene therapy of cancer. Biochem Biophys Res Commun 303(4):1169–1178PubMedCrossRefPubMedCentralGoogle Scholar
  115. Zhang G, Fedyunin I, Miekley O, Valleriani A, Moura A, Ignatova Z (2010) Global and local depletion of ternary complex limits translational elongation. Nucleic Acids Res 38(14):4778–4787PubMedPubMedCentralCrossRefGoogle Scholar
  116. Zia R, Dong J, Schmittmann B (2011) Modeling translation in protein synthesis with TASEP: a tutorial and recent developments. J Stat Phys 144:405–428CrossRefGoogle Scholar
  117. Zupanic A, Meplan C, Grellscheid SM, Mathers JC, Kirkwood TB, Hesketh JE, Shanley DP (2014) Detecting translational regulation by change point analysis of ribosome profiling data sets. RNA 20(10):1507–1518PubMedPubMedCentralCrossRefGoogle Scholar
  118. Zur H, Tuller T (2012) RFMapp: ribosome flow model application. Bioinformatics 28(12):1663–1664PubMedCrossRefPubMedCentralGoogle Scholar
  119. Zur H, Tuller T (2013) New universal rules of eukaryotic translation initiation fidelity. PLoS Comput Biol 9(7):e1003136PubMedPubMedCentralCrossRefGoogle Scholar
  120. Zur H, Tuller T (2016) Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution. Nucleic Acids Res 44(19):9031–9049PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringTel Aviv UniversityTel AvivIsrael

Personalised recommendations