Grima Group - Stochastic modelling of biochemical reaction kinetics

Publications

  1. 132. Zabaikina I and Grima R. 2026. Imperfect molecular detection renormalizes apparent kinetic rates in stochastic gene regulatory networks (submitted)
  2. 131. Marmolejo Lozano JD, Popovic N and Grima R. 2026. Effects of Model Reduction on Coherence and Information Transfer in Stochastic Biochemical Systems (submitted)
  3. 130. Ma M and Grima R. 2026. DART: Deep learning for the Analysis and Reconstruction of Transcriptional dynamics from live-cell imaging data (submitted)
  4. 129. Wu Bingjie, Grima R and Jia C. 2026. Spectral theory of stochastic gene expression: a Hilbert space framework (submitted)
  5. 128. Wang Y, Shu Z, Cao Z and Grima R. 2026. From Noise to Models to Numbers: Evaluating Negative Binomial Models and Parameter Estimations in Single-Cell RNA-seq (submitted)
  6. 127. Valdivia-Cabrera M, Butlin P, Grima R and Halliday KJ. 2026. Arabidopsis thaliana Phytochrome A Sensory Properties in Canopy Shade (accepted for publication in PNAS)
  7. 126. Wang Y, Szavits-Nossan J, Cao Z and Grima R. 2025. Joint distribution of nuclear and cytoplasmic mRNA levels in stochastic models of gene expression: analytical results and parameter inference, Physical Review Letters 135, 068401
  8. 125. Sukys A and Grima R. 2025. Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data, Nucleic Acids Research, 53, gkaf295
  9. 124. Nicoll AG, Szavits-Nossan J, Evans MR and Grima R. 2025. Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression, Nature Communications 16, 2833
  10. 123. Cao Z, Wang Y and Grima R. 2025. Universal deterministic patterns in stochastic count data, npj Systems Biology and Applications 11, 6
  11. 122. Garcia-Tejera R, Amoyel M, Grima R and Schumacher L. 2025. Licensing and niche competition in spermatogenesis: mathematical models suggest complementary regulation of tissue maintenance, Development 152 (1): dev202796
  12. 121. Jia C and Grima R. 2024. Holimap: an accurate and efficient method for solving stochastic gene network dynamics, Nature Communications 15, 6557
  13. 120. Cao Z, Chen R, Xu L, Zhou X, Fu X, Zhong W and Grima R. 2024. Efficient and scalable prediction of spatio-temporal stochastic gene expression in cells and tissues using graph neural networks, Mathematical Biosciences 375, 109248
  14. 119. Ham L, Coomer M, Öcal K, Grima R and Stumpf MPJ. 2024. A stochastic vs deterministic perspective on the timing of cellular events, Nature Communications 15, 5286
  15. 118. Ma M, Szavits-Nossan J, Singh A and Grima R. 2024. Analysis of a detailed multi-stage model of stochastic gene expression using queueing theory and model reduction, Mathematical Biosciences 373, 109204
  16. 117. Szavits-Nossan, J and Grima R. 2024. Solving stochastic gene expression models using queueing theory: a tutorial review, Biophysical Journal DOI: 10.1016/j.bpj.2024.04.004
  17. 116. Wu Bingjie, Holehouse J, Grima R and Jia C. 2024. Solving the time-dependent protein distributions for autoregulated bursty gene expression using spectral decomposition, J. Chem. Phys. 160, 074105
  18. 115. Grima R and Esmenjaud PM. 2023. Quantifying and correcting bias in transcriptional parameter inference from single-cell data, Biophysical Journal DOI: 10.1016/j.bpj.2023.10.021
  19. 114. Wang Y, Yu Z, Grima R and Cao Z. 2023. Exact solution of a three-stage model of stochastic gene expression including cell-cycle dynamics, Journal of Chemical Physics 159.22
  20. 113. Kerr L, Grima R and Sproul D. 2023. Genome-wide single-molecule analysis of long-read DNA methylation reveals heterogeneous patterns at heterochromatin, PLOS Genetics DOI:10.1371/journal.pgen.1010958
  21. 112. Szavits-Nossan, J and Grima R. 2023. Uncovering the effect of RNA polymerase steric interactions on gene expression noise: analytical distributions of nascent and mature RNA numbers, Physical Review E 108, 034405 (2023)
  22. 111. Weidemann D, Singh A, Grima R and Hauf S. 2023. The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian. Science Advances 9.32: eadh5138
  23. 110. Öcal K, Sanguinetti G and Grima R. 2023. Model Reduction for the Chemical Master Equation: an Information-Theoretic Approach. Journal of Chemical Physics, 10.1063/5.0131445
  24. 109. Szavits-Nossan, J and Grima R. 2023. Steady-state distributions of nascent RNA for general initiation mechanisms. Physical Review Research 5, 013064
  25. 108. Jia C and Grima R. 2023. Coupling gene expression dynamics to cell size dynamics and cell cycle events: exact and approximate solutions of the extended telegraph model. iScience 26: 105746
  26. 107. Garcia-Tejera R, Schumacher L and Grima R. 2022. Regulation of stem cell dynamics through volume exclusion. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 478, 20220376
  27. 106. Fu X, Patel H, Coppola S, Xu L, Cao Z, Lenstra TL and Grima R. 2022. Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions. eLife 11:e82493
  28. 105. Jia C, Singh A and Grima R. 2022. Concentration fluctuations in growing and dividing cells: insights into the emergence of concentration homeostasis. PLoS Computational Biology 18(10): e1010574
  29. 104. Saint-Antoine M, Grima R. and Singh A. 2022. A fluctuation-based approach to infer kinetics and topology of cell-state switching (accepted for publication in the Proceedings of the 61st IEEE Conference on Decision and Control)
  30. 103. Sukys A, Öcal K and Grima R. 2022. Approximating Solutions of the Chemical Master Equation using Neural Networks. iScience 25, 105010
  31. 102. Braichencko S, Grima R and Sanguinetti G. 2022. Bayesian learning of effective chemical master equations in crowded intracellular conditions, In: Petre, I., Păun, A. (eds) Computational Methods in Systems Biology. CMSB 2022. Lecture Notes in Computer Science, vol 13447. Springer, Cham. https://doi.org/10.1007/978-3-031-15034-0_12.
  32. 101. Fu X, Zhou X, Gu D, Cao Z, Grima R. 2022. DelaySSAToolkit.jl: stochastic simulation of reaction systems with time delays in Julia. Bioinformatics doi:10.1093/bioinformatics/btac472
  33. 100. Öcal K, Gutmann MU, Sanguinetti G, Grima R. 2022. Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models. Journal of the Royal Society Interface 19: 20220153
  34. 99. Filatova T, Popovic N and Grima R. 2022. Modulation of nuclear and cytoplasmic mRNA fluctuations by time-dependent stimuli: analytical distributions. Mathematical Biosciences 347, 108828
  35. 98. Kerr L, Sproul D and Grima R. 2022. Cluster mean-field theory accurately predicts statistical properties of large-scale DNA methylation patterns. J. Roy. Soc. Interface 19, 20210707
  36. 97. Jia C, Singh A and Grima R. 2022. Characterizing non-exponential growth and bimodal cell size distributions in fission yeast: an analytical approach. PLoS Computational Biology 18, e1009793
  37. 96. Szavits-Nossan, J and Grima R. 2022. Mean-field theory accurately captures the variation of copy number distributions across the mRNA's life cycle. Physical Review E 105, 014410.
  38. 95. Braichenko S, Holehouse J. and Grima R. 2021. Distinguishing between models of mammalian gene expression: telegraph-like models versus mechanistic models. J. Roy. Soc. Interface 18, 20210510
  39. 94. Sukys A and Grima R. 2021. MomentClosure.jl: automated moment closure approximations in Julia. Bioinformatics 38, 289
  40. 93. Qingchao J, Fu X, Yan S, Li R, Du W, Cao Z, Qian F and Grima R. 2021. Neural network aided approximation and parameter inference of stochastic models of gene expression. Nature Communications 12, 2618
  41. 92. Jia C, and Grima R. 2021. Frequency domain analysis of fluctuations of mRNA and protein copy numbers within a cell lineage: theory and experimental validation. Physical Review X 11, 021032
  42. 91. Jia C, Singh A and Grima R. 2021. Cell size distribution of lineage data: analytic results and parameter inference. iScience 24, 102220
  43. 90. Filatova T, Popovic N and Grima R. 2021. Statistics of nascent and mature RNA fluctuations in a stochastic model of transcriptional initiation, elongation, pausing and termination. Bull. Math. Biol. 83, 3
  44. 89. Holehouse J, Sukys A and Grima R. 2020. Stochastic time-dependent enzyme kinetics: closed-form solution and transient bimodality. J. Chem. Phys. 153, 164113
  45. 88. Cao Z, Filatova T, Oyarzun D and Grima R. 2020. A stochastic model of gene expression with polymerase recruitment and pause release. Biophysical Journal 119, 1002-1014
  46. 87. Holehouse J, Gupta A, and Grima R. 2020. Steady-state fluctuations of a genetic feedback loop with fluctuating rate parameters using the unified colored noise approximation. Journal of Physics A: Mathematical and Theoretical 53, 405601
  47. 86. Perez-Carrasco R, Beentjes CHL, and Grima R. 2020. Effects of cell cycle variability on lineage and population measurements of mRNA abundance. J. R. Interface 17:20200360
  48. 85. Jia C, and Grima R. 2020. Dynamical phase diagram of an auto-regulating gene in fast switching conditions. J. Chem. Phys. 152:174110
  49. 84. Jia C, and Grima R. 2020. Small protein number effects in stochastic models of autoregulated bursty gene expression. Journal of Chemical Physics 152:084115
  50. 83. Beentjes CHL, Perez-Carrasco R, and Grima R. 2020. Exact solution of stochastic gene expression models with bursting, cell cycle and replication dynamics. Physical Review E 101:032403
  51. 82. Holehouse J, Cao Z, and Grima R. 2020. Stochastic modeling of auto-regulatory genetic feedback loops: a review and comparative study. Biophysical Journal 118, 1517-1525
  52. 81. Cao Z and Grima R. 2020. Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells. Proceedings of the National Academy of Sciences of the United States of America 117, 4682-4692
  53. 80. Öcal K, Grima R and Sanguinetti G. 2019. Parameter estimation for biochemical reaction networks using Wasserstein distances. Journal of Physics A: Mathematical and Theoretical 53, 034002
  54. 79. Holehouse J and Grima R. 2019. Revisiting the reduction of stochastic models of genetic feedback loops under fast promoter switching conditions. Biophysical Journal 117, 1311-1330
  55. 78. Keizer EM, Bastian B, Smith RW, Grima R and Fleck C. 2019. Extending the linear-noise approximation to biochemical systems influenced by intrinsic noise and lognormal distributed extrinsic noise. Physical Review E 99:052417
  56. 77. Cao Z and Grima R. 2019. Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data. Journal of the Royal Society Interface 16: 20180967
  57. 76. Smith S and Grima R. 2018. Plasticity of the truth table of low-leakage genetic logic gates. Physical Review E 98:062410
  58. 75. Moraki E, Grima R and Painter K. 2018. A stochastic model of corneal epithelium maintenance with applications to wound healing. Journal of Mathematical Biology https://doi.org/10.1007/s00285-018-1308-9
  59. 74. Cao Z and Grima R. 2018. Linear mapping approximation of gene regulatory networks with stochastic dynamics. Nature Communications 9:3305
  60. 73. Smith S and Grima R. 2018. Spatial stochastic intracellular kinetics - a review of modelling approaches. Bulletin of Mathematical Biology https://doi.org/10.1007/s11538-018-0443-1
  61. 72. Grima R, Sonntag S, Venezia F, Smith RW, Kircher S and Fleck C. 2018. Insight into nuclear body formation of phytochromes through theory and experiment. Physical Biology 15:056003
  62. 71. Smith S and Grima R. 2018. Single-cell variability in multicellular organisms. Nature Communications 9:345
  63. 70. Schnoerr D, Cseke B, Grima R and Sanguinetti G. 2017. Efficient low-order approximation of first-passage time distributions. Physical Review Letters 119: 210601
  64. 69. Cianci C, Schnoerr D, Piehler A and Grima R. 2017. An alternative route to the system-size expansion. Journal of Physics A: Mathematical and Theoretical 50: 395003
  65. 68. Smith S and Grima R. 2017. Model reduction for stochastic reaction systems. (published as a chapter in the Springer book: Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology DOI 10.1007/978-3-319-62627-7_7)
  66. 67. Smith S, Cianci C and Grima R. 2017. Macromolecular crowding directs the motion of small molecules inside cells. J. R. Soc. Interface 14:20170047
  67. 66. Cianci C, Smith S and Grima R. 2017. Capturing Brownian dynamics with an on-lattice model of hard-sphere diffusion. Physical Review E 95:052118
  68. 65. Smith S and Grima R. 2017. Fast simulation of Brownian dynamics in a crowded environment. Journal of Chemical Physics 146:024105
  69. 64. Grima R and Leier A. 2017. Exact product formation rates for stochastic enzyme kinetics. J. Phys. Chem. B, 121:13
  70. 63. Schnoerr D, Sanguinetti G and Grima R. 2017. Approximation and inference methods for stochastic biochemical systems - a tutorial review. Journal of Physics A: Mathematical and Theoretical 50.9 (2017): 093001
  71. 62. Frohlich F, Thomas P, Kazeroonian A, Theis FJ, * Grima R and * Hasenauer J.. 2016. Inference for stochastic chemical kinetics using moment equations and system size expansion. PLoS Computational Biology 12(7):e1005030 (* Joint corresponding authors)
  72. 61. Singh A and Grima R. 2016. The Linear-Noise Approximation and moment-closure approximations for stochastic chemical kinetics. (published as a chapter in a book by MIT Press)
  73. 60. Voliotis M, Thomas P, Bowsher C and Grima R. 2016. The extra reaction algorithm for stochastic simulation of networks in fluctuating environments (published as a chapter in a book by MIT Press)
  74. 59. Andreychenko A, Bortolussi L, Grima R, Thomas P and Wolf V. 2016. Distribution approximations for the chemical master equation: comparison of the method of moments and the system size expansion. (accepted for publication as a chapter in Springer book series) http://arxiv.org/abs/1509.09104
  75. 58. Voliotis M, Thomas P, * Grima R and * Bowsher C. 2016. Stochastic simulation of biomolecular networks in dynamic environments, PLoS Computational Biology 12(6):e1004923 (* Joint corresponding authors)
  76. 57. Schnoerr D, Grima R and Sanguinetti G. 2016. Cox-process representation and inference for stochastic reaction-diffusion processes. Nature Communications 7:11729
  77. 56. Smith S, Cianci C and Grima R. 2016. Analytical approximations for spatial stochastic gene expression in single cells and tissues. J. R. Soc. Interface 13:20151051
  78. 55. Smith S and Grima R. 2016. Breakdown of the reaction-diffusion master equation with nonelementary rates. Physical Review E 93:052135
  79. 54. Cianci C, Smith S, and Grima R. 2016. Molecular finite-size effects in stochastic models of equilibrium chemical systems. Journal of Chemical Physics 144:084101
  80. 53. Smith S, Cianci C and Grima R. 2015. Model reduction for stochastic chemical systems with abundant species. Journal of Chemical Physics 143:214105
  81. 52. Schnoerr D, Sanguinetti G and Grima R. 2015. Comparison of different moment-closure approximations for stochastic chemical kinetics. Journal of Chemical Physics 143:185101
  82. 51. Grima R. 2015. The linear-noise approximation and the chemical master equation exactly agree up to second-order moments for a class of chemical systems. Physical Review E 92: 042124
  83. 50. Thomas P and Grima R. 2015. Approximate distributions of the Master equation, Physical Review E 92:012120
  84. 49. Duncan A, Liao S, Vejchodsky T, Erban R and Grima R. 2015. Noise-induced multistability in chemical systems: Discrete vs Continuum modeling, Physical Review E 91:042111
  85. 48. Thomas P, Fleck C, Grima R and Popovic N. 2014. System size expansion of the master equation using Feynman rules and diagrams, Journal of Physics A: Mathematical and Theoretical 47:455007
  86. 47. Johansson H, Jones H. J, Foreman J, Hemsted J. R., Stewart K, Grima R and Halliday K. 2014. Arabidopsis cell expansion is controlled by a photothermal switch, Nature Communications 5:4848
  87. 46. Schnoerr D, Sanguinetti G and Grima R. 2014. Validity conditions and stability of moment closure approximations for stochastic chemical kinetics. Journal of Chemical Physics 141:084103
  88. 45. Schnoerr D, Sanguinetti G and Grima R. 2014. The complex chemical Langevin equation. Journal of Chemical Physics 141:024103
  89. 44. Middleton A, Fleck C and Grima R. 2014. A continuum approximation to an off-lattice, individual-cell based model of cell migration and adhesion. Journal of Theoretical Biology 359: 220 (Recommended reading by Faculty of 1000)
  90. 43. Thomas P, Popovic N and Grima R. 2014. Phenotypic switching in gene regulatory networks. Proceedings of the National Academy of Sciences of the United States of America 111: 6994
  91. 42. Chew Y.H, Smith R.W, Jones H. J, Seaton D., Grima R and Halliday K. 2014. Mathematical models light up plant signaling. Plant Cell 26: 5
  92. 41. Grima R. 2014. Anomalous fluctuation scaling laws in stochastic enzyme kinetics: increase of noise strength with the mean concentration. Physical Review E. 89: 012710
  93. 40. Grima R, Nils W and Schnell S. 2014. Single molecule enzymology à la Michaelis-Menten. Febs Journal 281: 518
  94. 39. Toner DLK, and Grima R. 2013. Effects of bursty protein production on the noisy oscillatory properties of downstream pathways. Scientific Reports 3:2438
  95. 38. Thomas P, Straube A. V., Timmer J., Fleck C., Grima R. 2013. Signatures of nonlinearity in single cell noise-induced oscillations. Journal of Theoretical Biology 335:222
  96. 37. Basile R., Grima R. and Popovic N. 2013. A graph-based approach for the approximate solution of the chemical master equation. Bulletin of Mathematical Biology 75:1653
  97. 36. Thomas P, Matuschek H, Grima R. 2013. How reliable is the linear noise approximation of gene regulatory networks? BMC Genomics 14(Suppl 4):S5
  98. 35. Toner DLK, and Grima R. 2013. Molecular noise induces concentration oscillations in chemical systems with stable node steady states. Journal of Chemical Physics 138:055101
  99. 34. Grima R, and Kim J. 2012. Editorial: Modelling noise in biochemical reaction networks. 2012 IET Systems Biology 6:101
  100. 33. Ramaswamy R, Gonzalez-Segredo N, Sbalzarini IF, Grima R. 2012. Discreteness-induced concentration inversion in mesoscopic chemical systems. Nature Communications. 3:779.
  101. 32. Thomas P, Matuschek H, Grima R. 2012. Computation of biochemical pathway fluctuations beyond the linear noise approximation using iNA. IEEE International Conference on Bioinformatics and Biomedicine. 10.1109/BIBM.2012.6392668
  102. 31. Thomas P, Matuschek H, Grima R. 2012. intrinsic Noise Analyzer: a software package for the exploration of stochastic biochemical kinetics using the system-size expansion. PLoS ONE. 7(6):e38518.
  103. 30. Thomas P, Grima R, Straube AV. 2012. Rigorous elimination of fast stochastic variables from the linear-noise approximation using projection operators. Physical Review E. 86: 041110
  104. 29. Thomas P, Straube AV, Grima R. 2012. The slow-scale linear noise approximation: an accurate, reduced stochastic description of biochemical networks under timescale separation conditions. BMC Systems Biology. 6:39. (Editor's Pick for 2012)
  105. 28. Wenden B, Toner DLK, Hodge SK, Grima R, Millar AJ. 2012. Spontaneous spatiotemporal waves of gene expression from biological clocks in the leaf. Proceedings of the National Academy of Sciences of the United States of America. 109:6757. (Selected as "Must Read" by Faculty of 1000)
  106. 27. Grima R, Schmidt D, Newman TJ. 2012. Steady-state fluctuations of a genetic feedback loop: an exact solution. Journal of Chemical Physics. 137:035104.
  107. 26. Grima R. 2012. A study of the accuracy of moment-closure approximations for stochastic chemical kinetics. Journal of Chemical Physics . 136:154105.
  108. 25. Grima R. 2011. Construction and accuracy of partial differential equation approximations to the chemical master equation. Physical Review E. 84:056109.
  109. 24. Thomas P, Straube AV, Grima R. 2011. Limitations of the stochastic quasi-steady-state approximation in open biochemical reaction networks. Journal of Chemical Physics. 135:181103.
  110. 23. Grima R, Thomas P, Straube AV. 2011. How accurate are the chemical Langevin and Fokker-Planck equations? Journal of Chemical Physics. 135:084103. (Editor's Choice for 2011)
  111. 22. Grima R, Yaliraki SN, Barahona M. 2010. Crowding-induced anisotropic transport modulates reaction kinetics in nanoscale porous media. Journal of Physical Chemistry B. 114:5380.
  112. 21. Grima R. 2010. An effective rate equation approach to reaction kinetics in small volumes: theory and application to biochemical reactions in nonequilibrium steady-state conditions. Journal of Chemical Physics. 133:035101
  113. 20. Grima R. 2010. Intrinsic biochemical noise in crowded intracellular conditions. Journal of Chemical Physics. 132:185102.
  114. 19. Le Bihan T, Grima R, Martin S, Forster T, Le Bihan Y. 2010. Quantitative analysis of low abundance peptides in HeLa cell cytoplasm by targeted LC-MS and stable isotope dilution: Emphasizing the distinction between peptide detection and peptide identification. Rapid Communications in Mass Spectrometry. 24:1093.
  115. 18. Thomas P, Straube AV, Grima R. 2010. Stochastic theory of large-scale enzyme-reaction networks: finite-copy number corrections to rate equation models. Journal of Chemical Physics. 133:195101.
  116. 17. Grima R. 2009. Investigating the robustness of the classical enzyme kinetic equations in small intracellular compartments. BMC Systems Biology. 3:101.
  117. 16. Grima R. 2009. Noise-induced breakdown of the Michaelis-Menten reaction in steady-state conditions. Physical Review Letters. 102:218103.
  118. 15. Grima R, Schnell S. 2008. Modelling reaction kinetics inside cells. Essays in Biochemistry. 45:41.
  119. 14. Grima R. 2008. Multiscale modeling of biological pattern formation. Current Topics in Developmental Biology. 81:435.
  120. 13. Grima R, Yaliraki SN. 2007. Brownian motion of an asymmetrical particle in a potential field. Journal of Chemical Physics. 127:084511.
  121. 12. Grima R, Schnell S. 2007. Can differential adhesion drive somite formation? Developmental Biology. 307:248.
  122. 11. Grima R. 2007. Directed cell migration in the presence of obstacles. Theoretical Biology and Medical Modelling. 4:2.
  123. 10. Grima R, DeGraffenreid J, Venables JA. 2007. Mean-field theory of nucleation on strained surfaces. Physical Review B. 76:233405.
  124. 9. Grima R, Schnell S. 2007. A mesoscopic simulation approach for modelling intracellular reactions. Journal of Statistical Physics. 128:139.
  125. 8. Schnell S, Grima R, Maini PK. 2007. Multiscale modelling in biology. American Scientist. 95:134.
  126. 7. Grima R, Schnell S. 2006. How reaction kinetics with time-dependent rate coefficients differ from generalized mass action. ChemPhysChem. 7:1422.
  127. 6. Grima R. 2006. Phase transitions and superuniversality in the dynamics of a self-driven particle. Physical Review E. 74:011125.
  128. 5. Grima R, Schnell S. 2006. A systematic investigation of the rate laws valid in intracellular environments. Biophysical Chemistry. 124:1.
  129. 4. Grima R. 2005. Strong coupling dynamics of a multi-cellular chemotactic system. Physical Review Letters. 95:128103.
  130. 3. Grima R, Newman TJ. 2004. Accurate discretization of advection-diffusion equations. Physical Review E. 70:036703.
  131. 2. Newman TJ, Grima R. 2004. Many-body theory of chemotactic interactions. Physical Review E. 70:051916.
  132. 1. Grima R, Micallef A, Colls JJ. 2002. External contribution to urban air pollution. Environmental monitoring and assessment. 73:291.