Papers Published

2016

  1. Extending and Applying Spartan to Perform Temporal Sensitivity Analyses for Predicting Changes in Influential Biological Pathways in Computational Models. K.Alden, J.Timmis, P.S.Andrews, H.Veiga-Fernandes, M.C.Coles. IEEE Transactions on Computational Biology and Bioinformatics. vol.PP, no.99. doi: 10.1109/TCBB.2016.2527654
  2. Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems. R.A.Williams, J.Timmis, E.Qwarnstrom. PLoS One 11(8): e0160834. doi: 10.1371/journal.pone.0160834
  3. Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection. M.Read, J.Bailey,J.Timmis,T.Chtanova. PLoS Comp. Bio 12(9): e1005082. doi: 10.1371/journal.pcbi.1005082
  4. Automated multi-objective calibration of biological agent-based simulations. M.Read, K.Alden, L.Rose, J.Timmis. Royal Society Interface. 13 doi:

2015

  1. Using argument notation to engineer biological simulations with increased confidence. K.Alden, P.S.Andrews, F.A.C.Polack, H.Veiga-Fernandes, M.C.Coles, J.Timmis. Journal of the Royal Society Interface. doi: 10.1098/rsif.2014.1059
  2. The rise in computational systems biology approaches for understanding NF-kB signaling dynamics. Richard A. Williams, Jon Timmis, Eva E. Qwarnstrom. Science Signalling. doi:10.1126/scisignal.aac6503.
  3. Agent-Based Modeling in Systems Pharmacology. J Cosgrove, J Butler, K Alden, M Read, V Kumar, L Cucurull-Sanchez, J Timmis, M Coles. CPT Pharmacometrics Syst. Pharmacol.

2014

  1. Utilising a Simulation Platform to Understand the Effect of Domain Model Assumptions. K.Alden, P.S. Andrews, H. Veiga-Fernandes, J. Timmis, M.C. Coles. Natural Computing. doi: 10.1007/s11047-014-9428-7
  2. Easing Parameter Sensitivity Analysis of Netlogo Simulations using Spartan. K.Alden, J. Timmis, M.C. Coles. Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems. MIT Press. doi: 10.7551/978-0-262-32621-6-ch100
  3. Novel Approaches to the Visualisation and Quantification of Biological Simulations by Emulating Experimental Techniques. J.Butler, K.Alden, H.Veiga-Fernandes, J. Timmis, M.C. Coles. Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems. MIT Press. doi: 10.7551/978-0-262-32621-6-ch099
  4. Modelling Biological Behaviours with the Unified Modelling Language: an Immunological Case Study and Critique. M. Read, P.S. Andrews, J. Timmis, V. Kumar. Journal of the Royal Society Interface, vol 11, no 99, doi: 10.1098/​rsif.2014.0704
  5. Computational Models of the NF-KB Signalling Pathway. R.A.Williams, J.Timmis, E.E.Qwarnstrom. Computation. 2(4), 131-158; doi:10.3390/computation2040131
  6. Applying spartan to Understand Parameter Uncertainty in Simulations. K.Alden, M.Read, P.S.Andrews, J.Timmis, M.C.Coles. R Journal

2013

  1. A Petri Net Model of Granulomatous Inflammation: Implications for IL-10 Mediated Control of Leishmania donovani Infection L. Albergane, J. Timmis L. Beattie and P. Kaye. PLoS Comput Biol 9(11): e1003334. doi:10.1371/journal.pcbi.1003334
  2. Determining Disease Intervention Strategies Using Spatially Resolved Simulations. M. Read, P. Andrews, J. Timmis, R. Williams, R. Greaves, H. Sheng, M. Coles and V. Kumar. PLoS ONE 8(11): e80506. doi:10.1371/journal.pone.0080506
  3. Functional complexity of the Leishmania granuloma and the potential of in silico modelling. J. Moore, D. Moyo, L. Beattie, P. Andrews, J. Timmis and P. Kaye. Front. Immun. 4:35. doi: 10.3389/fimmu.2013.00035
  4. SPARTAN: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems. K. Alden, M. Read, J. Timmis, P.S. Andrews, H. Veiga-Fernandes, M.C. Coles. PLoS Comput Biol 9(2): e1002916. doi:10.1371/journal.pcbi.1002916
  5. In silico investigation into dendritic cell regulation of CD8Treg mediated killing of Th1 cells in murine experimental autoimmune encephalomyelitis. R.A. Williams, R.B. Greaves, M. Read, J. Timmis, P.S. Andrews and V. Kumar. BMC Bioinformatics (2013) 14(Suppl 6):S9. doi:10.1186/1471-2105-14-S6-S9
  6. In silico investigation of novel biological pathways: the role of CD200 in regulation of T cell priming in Experimental Autoimmune Encephalomyelitis. R.B. Greaves, M. Read, J. Timmis, P.S. Andrews, J.A. Butler, B. Gerckens and V. Kumar. Biosystems (2013). doi: 10.1016/j.biosystems.2013.03.007
  7. Automated Calibration of Agent-based Immunological Simulations (PDF  , 127kb). M. Read, M. Tripp, H. Leonova, L. Rose and J. Timmis. Extended abstract European Conference on Artificial Life. doi: 10.7551/978-0-262-31709-2-ch129

2012

  1. Extending an established simulation: exploration of the possible effects using a case study in experimental autoimmune encephalomyelitis. R.B. Greaves, M. Read, J. Timmis, P.S. Andrews, V. Kumar. LNCS 7222:150-161. doi: 10.1007/978-3-642-28792-3_20
  2. Differential RET signaling responses orchestrate lymphoid and nervous enteric system development. A. Patel, N. Harker, L. Moreira-Santos, M. Ferreira, K. Alden, J. Timmis, K. Foster, A. Garefalaki, P. Pachnis, P.S. Andrews, H. Enomoto, J. Milbrandt, V. Pachnis, M.C. Coles, D. Kioussis, H. Veiga-Fernandes. Science Signalling, Volume 5, Issue 235. doi: 10.1126/scisignal.2002734
  3. Pairing experimentation and computational modelling to understand the role of tissue inducer cells in the development of lymphoid organs. K. Alden, J. Timmis, P.S. Andrews, H. Veiga-Fernandes, M.C Coles. Frontiers in Immunology. Volume 3:172. doi: doi: 10.3389/fimmu.2012.00172.
  4. Techniques for Grounding Agent-Based Simulations in the Real Domain: a case study in Experimental Autoimmune Encephalomyelitis. M. Read, P. Andrews, J. Timmis and V. Kumar. Mathematical and Computer Modelling of Dynamical Systems (MCMDS), 18(1):67-86. doi: 10.1080/13873954.2011.601419
  5. Domain Modelling – When UML is not enough: A case study using the IL-1 Stimulated NF-kB Intracellular Signalling Pathway. R. Williams, J. Timmis and E. Qwarnstrom ICARIS 2012.(abstract)
  6. Building Confidence in Agent-Based Computational Models of the Immune System. J. Timmis et al. ICARIS 2012. (abstract)
  7. CD200 Regulation can Promote Recovery from Autoimmunity in Experimental Autoimmune Encephalomyelitis. M. Read, J.A. Butler, B. Gerckens, J. Timmis and V. Kumar. ICARIS 2012. (abstract)

2011

  1. In Silico Investigation into CD8Treg Mediated Recovery in Murine Experimental Autoimmune Encephalomyelitis. R. Williams, M. Read, J.Timmis, P. Andrews and V. Kumar. Proceedings of the 10th International Conference on Artificial Immune Systems (ICARIS), LNCS volume 6825, pp 51-54, 2011.
  2. Towards Argument-Driven Validation of an in-silico Model of Immune Tissue Organogenesis. K. Alden P. Andrews, J. Timmis, H. Veiga-Fernandes and M.C. Coles. Extended Abstract in LNCS 6826, pp:66-70.
  3. CoSMoS Process, Models and Metamodels. P.S. Andrews, S. Stepney, T. Hoverd, F.A.C. Polack, A. Sampson and J. Timmis. In Proceedings of the 2011 Workshop on Complex Systems Modelling and Simulation. Part of ECAL 2011. pp: 1-14

2010

  1. A Petri Net Model of Granulomatous Inflammation. L. Albergante, J. Timmis, P. Andrews, L. Beattie and P. Kaye. LNCS 6209, pp:1-3. Hart et al, (Eds)
  2. Elucidation of T cell signalling models. N. Owens, J. Timmis, A. Greensted and A. Tyrrell. Journal of Theoretical Biology 262:452-470
  3. Reflections on the Simulation of Complex Systems for Science. F.A.C. Polack, P.S. Andrews, T. Ghetiu, M. Read, S. Stepney, J. Timmis and A.T. Sampson. In ICECCS 2010: Fifteenth IEEE International Conference on Engineering of Complex Computer Systems pp. 276-285 IEEE Press
  4. Hierarchical Classification of G-Protein-Coupled-Receptors with Data-Driven Selection of Attributes and Classifiers. A. Secker, M. Davis, A. Fretais, J. Timmis, E. Clark and D. Flower. International Journal of Data Mining and Bioinformatics Vol.4, No.2, pp:191-210
  5. Tunable Detectors for Artificial Immune Systems: From Model to Algorithm. P.S. Andrews and J. Timmis. The Handbook of Bioinformatics, pp:107-123. Flower, D and Davies, M. (Eds)

2009

  1. Using UML to Model EAE and its Regulatory Network. M. Read, J. Timmis, P. Andrews and V. Kumar. Proceedings of 8th International Conference on AIS. LNCS 566. pp:4-6
  2. Modelling and Simulation of Granuloma Formation in Visceral Leishmaniasis. A. Flugge, J. Timmis, P. Andrews, J. Moore and P. Kaye. Congress on Evolutionary Computation (CEC). pp.3052-3059. IEEE Press.
  3. A Domain Model of Experimental Autoimmune Encephalomyelitis. M. Read, J. Timmis, P. Andrews and V. Kumar. 2nd Workshop on Complex Systems Modelling and Simulation.pp:9-44

2008

  1. Alignment-Independent Techniques for Protein Classification. M. Davies, A. Secker, A. Freitas, J. Timmis, E. Clark and D. Flower. Current Proteomics. 5(4), pp.217-223.
  2. Optimising amino acid groupings for GPCR classification. M. Davies, A. Secker, A. Freatis, J. Timmis, E. Clark and D. Flower. Bioinformatics 24(18):1980-1986
  3. An Interdisciplinary Perspective on Artificial Immune Systems. J. Timmis, P. Andrews, N. Owens and E. Clark. Evolutionary Intelligence 1(1):5-26.
  4. Simulating biology: towards understanding what the simulation shows. P. Andrews, F. Polack, A. Sampson, J. Timmis, L. Scott and M. Coles CoSMoS workshop on Modelling and Simulating of Complex Systems.
  5. Complex Systems Models: Engineering Simulations. F.A.C. Polack, S. Stepney, A. Sampson, J. Timmis and T. Hoverd. pp. 482-489. ALife XI
  6. Empirical Investigation of an Artificial Cytokine Network. M. Read, J. Timmis and P. Andrews. LNCS 5132. pp. 340-351. Bentley et al (Eds). 2008
  7. Modelling the Tunability of Early T Cell Signalling Events. N. Owens, J. Timmis, A.Tyrrell and A. Greensted. LNCS 5132. pp. 12-23. Bentley et al (Eds) 2008

2007

  1. On the hierarchical classification of G Protein-Coupled Receptors. M. N. Davies; A. Secker; A. A. Freitas; M. Mendao; J. Timmis; D. R. Flower Bioinformatics 23: 3113-3118.
  2. Proteomic applications of automated GPCR classification. M. Davies, D. Gloriam, A. Secker, A. Freitas, M. Mendao, J. Timmis and D. Flower. Proteomics Vol. 7 (6). pp. 2800-2814

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems