Publications by topic

Antimicrobial Resistance

  1. Hooton SP, Millard AD, Baker M, Stekel DJ and Hobman JL 2019. DNA Traffic in the Environment and Antimicrobial Resistance. In: Nishida H., Oshima T. (eds) DNA Traffic in the Environment. Springer, Singapore.
  2. Williams O, Clark I, Gomes RL, Perehinec T, Hobman JL, Stekel DJ, Hyde R, Dodds C and Lester E 2019. Removal of copper from cattle footbath wastewater with layered double hydroxide adsorbents as a route to antimicrobial resistance mitigation on dairy farms. Science of The Total Environment 655:1139-1149.
  3. Stekel DJ 2018. First report of antimicrobial resistance pre-dates penicillin. Nature 562: 192.
  4. Zhu Y-G, Gillings M, Simonet P, Stekel DJ, Banwart S and Penuelas J 2018. . Human dissemination of genes and microorganisms in Earth’s Critical Zone. Global Change Biology 24:1488-1499.
  5. Zhu Y-G, Gillings M, Simonet P, Stekel DJ, Banwart S and Penuelas J 2017. Microbial mass movements. Science 357: 1099-1100.
  6. Pal C, Asiani K, Arya S, Rensing C, Stekel DJ, Larsson DGJ and Hobman JL 2017. Metal Resistance and Its Association With Antibiotic Resistance. Advances in Microbial Physiology. Advances in Microbial Physiology 70: 261-313.
  7. Baker M, Hobman JL, Dodd CER, Ramsden SJ and Stekel DJ (2016). Mathematical modelling of antimicrobial resistance in agricultural waste highlights importance of gene transfer rate. FEMS Microbial Ecology DOI:10.1093/femsec/fiw040.
  8. Ibrahim DR, Dodd CER, Stekel DJ, Ramsden SJ and Hobman JL (2016). Multi drug and extended spectrum beta-lactamase resistant Escherichia coli isolated from a dairy farm. FEMS Microbial Ecology DOI:10.1093/femsec/fiw013.

Mathematical Models in Molecular / Cell Biology / Biochemistry

  1. Takahashi H, Oshima T, Hobman JL, Doherty N, Clayton SR, Iqbal M, Hill PJ, Tobe T, Ogasawara N, Kanaya S and Stekel DJ 2015. The dynamic balance of import and export of zinc in Escherichia coli suggests a heterogeneous population response to stress. Journal of the Royal Society Interface DOI: .
  2. Fletcher, S.J., Iqbal, M., Jabbari, S., Stekel, D.J. and Rappoport, J.Z. 2014. Analysis of Occludin Trafficking, Demonstrating Continuous Endocytosis, Degradation, Recycling and Biosynthetic Secretory Trafficking. PLoS ONE DOI: 10.1371/journal.pone.0111176.
  3. Stekel, D.J. 2014. Modelling Plasmid Regulatory Systems. In: Bell E., Bond J., Klinman J., Masters B., Wells R. (Ed.) Molecular Life Sciences: An Encyclopedic Reference: SpringerReference ( Springer-Verlag Berlin Heidelberg.
  4. Herman, D., Thomas, C.M. and Stekel, D.J. 2012. Adaptation for Protein Synthesis Efficiency in a Naturally Occurring Self-Regulating Operon. PLoS ONE 7: e49678.
  5. Herman, D., Thomas, C.M. and Stekel, D.J. 2011. Global transcription regulation of RK2 plasmids: a case study in the combined use of dynamical mathematical models and statistical inference for integration of experimental data and hypothesis exploration. BMC Systems Biology 2011, 5:119.
  6. Fernando, C.T., Liekens, A.M.L., Bingle, L.E.H., Beck, C., Lenser, T., Stekel. D.J. and Rowe, J.E. 2009. Molecular circuits for associative learning in single-celled organisms. Journal of The Royal Society Interface, 6: 463-9.
  7. Welham, P.A. and Stekel, D.J. 2009. Mathematical model of the Lux luminescence system in the terrestrial bacterium Photorhabdus luminescens. Molecular BioSystems, 5:68-76.
  8. Stekel, D.J. and Jenkins, D.J. 2008. Strong negative self regulation of prokaryotic transcription factors increases the intrinsic noise of protein expression. BMC systems biology, 2:6.
  9. May, R.M., Stekel, D.J. and Nowak, M.A. 1997. Antigenic diversity thresholds and hazard functions. Mathematical Biosciences, 139:59-68.

Mathematical Models / Data Analysis in Agriculture

  1. Sakaridis I, Ellis RJ, Cawthraw SA, van Vliet AHM, Stekel DJ, Penell J, Chambers M, La Ragione RM and Cook AJ 2018. Investigating the Association Between the Caecal Microbiomes of Broilers and Campylobacter Burden. Frontiers in Microbiology 9:927.
  2. Ajmera I, Shi J, Giri J, Wu P, Stekel DJ, Lu C and Hodgman TC 2018. Regulatory feedback response mechanisms to phosphate starvation in rice. npj Systems Biology and Applications 4:4. doi:10.1038/s41540-017-0041-0.
  3. Yang, J., Osman, K., Iqbal, M., Stekel, D.J., Luo, Z., Armstrong, S.J. and Franklin, F.C.H. 2013. Inferring the Brassica rapa interactome using protein–protein interaction data from Arabidopsis thaliana. Frontiers in Plant Science 3:297.
  4. Bowles, E.J., Lee, J-H., Alberio, R., Lloyd, R.E.I., Stekel, D.J., Campbell, K.H.S. and St. John, J.C. 2007. Contrasting effects of in vitro fertilization and nuclear transfer on the expression of mtDNA replication factors. Genetics, 176:1511-26.
  5. Stekel, D.J., Nowak, M.A. and Southwood, T.R.E. 1996. Prediction of future BSE spread. Nature, 381:119.

Data Analysis / Bioinformatics Methodologies

  1. Shaw LM, Blanchard A, Chen Q, An X, Davies P, Tötemeyer S, Zhu Y-G and Stekel DJ 2019. DirtyGenes: testing for significant changes in gene or bacterial population compositions from a small number of samples. Scientific Reports 9: 2373.
  2. Iqbal, M, Doherty N, Page AML, Qazi SNA, Ajmera I, Lund PA, Kypraios T, Scott DJ, Hill PJ and Stekel DJ 2017.Reconstructing Promoter Activity From Lux Bioluminescent Reporters.
  3. Gerstgrasser M, Nicholls S, Stout M, Smart K, Powell C, Kypraios T and Stekel DJ. 2016. A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters. J. Bioinform. Comput. Biol. DOI: 10.1142/S0219720016500074
  4. Swan, A.L., Stekel, D.J., Hodgman, T.C., Allaway, D., Alqahtani, M.H., Mobasheri, A. and Bacardit, J. 2015.A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data. BMC Genomics 16(Suppl 1):S2.
  5. Iqbal, M., Hodgman, T.C. and Stekel, D.J. 2014. Computational prediction of domain-domain interactions: factor-graph based modelling and inference. Current Chemical Biology 7:234-240.
  6. Salama, R.A. and Stekel, D.J. 2013. A non-independent energy based multiple sequence alignment improves prediction of transcription factor binding sites. Bioinformatics 29: 2699-2704.
  7. Herbert, J.M.J., Stekel, D.J., Mura, M., Sychev, M. and Bicknell, R. 2011. Bioinformatic methods for finding differentially expressed genes in cDNA libraries, applied to the identification of tumour vascular targets. Methods in Molecular Biology, 729: 99-119.
  8. Salama, R.A. and Stekel, D.J. 2010. Inclusion of neighboring base interdependencies substantially improves genome-wide prokaryotic transcription factor binding site prediction. Nucleic Acids Research, 38: e135.
  9. Herbert, J.M.J., Stekel, D.J., Sanderson, S., Heath, V.L. and Bicknell, R., 2008. A novel method of differential gene expression analysis using multiple cDNA libraries applied to the identification of tumour endothelial genes. BMC Genomics, 9:153.
  10. Chaudhuri, R.R., Loman, N.J., Snyder, L.A., Bailey, C.M., Stekel, D.J. and Pallen, M.J. 2008. xBASE2: a comprehensive resource for comparative bacterial genomics. Nucleic Acids Research, 36: D543-546.
  11. Stekel, D.J., Sarti, D., Trevino, V., Zhang, L., Salmon, M., Buckley, C.D., Stevens, M., Pallen, M.J., Penn, C.W. and Falciani, F. 2005. Analysis of host response to bacterial infection using error model based gene expression microarray experiments. Nucleic Acids Research, 33:e53.
  12. Stekel, D.J., 2003. Microarray Bioinformatics. Cambridge University Press.
  13. Stekel, D.J., Git, Y. and Falciani, F. 2000. The comparison of gene expression from multiple cDNA libraries. Genome Research, 10:2055-61.

Data Analysis / Bioinformatics Applications

  1. Haoula, Z., Ravipati, S., Stekel, D.J., Ortori, C.A., Hodgman, T.C., Daykin, C., Raine-Fenning, N., Barrett, D.A. and Atiomo, W. 2015. Lipidomic analysis of plasma samples from women with polycystic ovary syndrome. Metabolomics 11:657-666.
  2. Gibbs, D. J., Voß, U., Harding, S. A., Fannon, J., Moody, L. A., Yamada, E., Swarup, K., Nibau, C., Bassel, G. W., Choudhary, A., Lavenus, J., Bradshaw, S. J., Stekel, D. J., Bennett, M. J. and Coates, J. C. 2014. AtMYB93 is a novel negative regulator of lateral root development in Arabidopsis. New Phytologist 203:1194-207.
  3. Gasson, P., Miller, R., Stekel, D.J., Whinder, F. and Zieminska, Z. 2010. Wood identification of Dalbergia nigra (CITES Appendix I) using quantitative wood anatomy, principal components analysis and naive Bayes classification. Annals of botany, 105: 45-56.
  4. Ma, H., Hagen, F., Stekel, D.J., Johnston, S.A., Sionov, E., Falk, R., Polacheck, I., Boekhout, T. and May, R.C. 2009. The fatal fungal outbreak on Vancouver Island is characterized by enhanced intracellular parasitism driven by mitochondrial regulation. Proceedings of the National Academy of Sciences of the United States of America, 106: 12980-5.
  5. Chaudhuri, R.R., Peters, S.E., Pleasance, S.J., Northern, H., Willers, C., Paterson, G.K., Cone, D.B., Allen, A.G., Owen, P.J, Shalom, G., Stekel. D.J., Charles, I.G. and Maskell, D.J. 2009. Comprehensive identification of Salmonella enterica serovar typhimurium genes required for infection of BALB/c mice. PLoS pathogens, 5: e1000529.
  6. Schettino, G., Folkard, M., Vojnovic, B., Michette, A.G., Stekel, D.J., Pfauntsch, S.K., Prise, K.M. and Michael, B.D. 2000. The ultrasoft X-ray microbeam: a sub-cellular probe of radiation response. Radiation Research, 153:222-225.

In Silico Evolution / Artificial Life

  1. Stekel, D.J. and Jenkins, D.J. 2012. Evolution of resource and energy management in biologically realistic gene regulatory network models. Advances in Experimental Medicine and Biology 751: 301-328.
  2. Jenkins, D.J. and Stekel, D.J. 2010. De Novo Evolution of Complex, Global and Hierarchical Gene Regulatory Mechanisms. Journal of Molecular Evolution, 71: 128-40.
  3. Jenkins, D.J. and Stekel, D.J. 2010. Stochasticity Versus Determinism: Consequences for Realistic Gene Regulatory Network Modelling and Evolution. Journal of Molecular Evolution, 70: 215-231.
  4. Fernando, C.T., Goldstein, R.A., Husbands, P. and Stekel, D.J. 2010. In silico biology. Pacific Symposium on Biocomputing, 477-80.
  5. Jenkins, D.J. and Stekel, D.J. 2009. A new model for investigating the evolution of transcription control networks. Artificial Life, 15:259-91.
  6. Jenkins, D.J. and Stekel, D.J. 2008. Effects of signalling on the evolution of gene regulatory networks. In: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems. pp. 289-296
  7. Jones, B., Stekel, D.J., Rowe, J.E., Fernando, C.T. 2007. Is there a Liquid State Machine in the Bacterium Escherichia Coli?. In: Proceedings of IEEE Symposium on Artificial Life. pp. 187-191

Mathematical / Computer Models of Tissues / Organs

  1. Leadbeater, B.S., Yu, Q., Kent, J. and Stekel, D.J. 2009. Three-dimensional images of choanoflagellate loricae. Proceedings of the Royal Society B: Biological Sciences, 276:3-11.
  2. Stekel, D.J. 1998. The simulation of density-dependent effects in the recirculation of T lymphocytes. Scandinavian journal of immunology, 47:426-30.
  3. Stekel, D.J., Parker, C.E. and Nowak, M.A. 1997. A model of lymphocyte recirculation. Immunology Today, 18:216-21.
  4. Stekel, D.J. 1997. The role of inter-cellular adhesion in the recirculation of T lymphocytes. Journal of Theoretical Biology, 186:491-501.
  5. Rashbass, J., Stekel, D.J. and Williams, E.D. 1996. The use of a computer model to simulate epithelial pathologies. Journal of Pathology, 179:333-9.
  6. Stekel, D.J., Rashbass, J. and Williams, E.D. 1995. A computer graphic simulation of squamous epithelium. Journal of Theoretical Biology, 175:283-93.

Social Research

  1. Levine DT and Stekel DJ 2016. So why have you added me? Adolescent girls’ technology-mediated attachments and relationships. Computers in Human Behaviour 63:25-34.