S. Ramsey, J.J. Smith, D. Orrell, M. Marelli, T.W. Petersen, P. de Atauri, H. Bolouri, J.D. Aitchison. Dual feedback loops in GAL regulon suppress cellular heterogeneity in yeast. Nature Genetics, 38, 1082-1087, 2006 (abstract). Presents experimental results which explore the role of feedback loops in a genetic network.
D. Orrell, S. Ramsey, M. Marelli, J.J. Smith, T.W. Petersen, P. de Atauri, J.D. Aitchison, H. Bolouri. Feedback control of stochastic noise in the yeast galactose utilization pathway. Physica D, 217, 64-76, 2006. Gives a technique for determining the sources of noise in a genetic network – i.e. the reactions which contribute most to fluctuations in individual proteins – and applies it to the galactose utilization pathway in yeast.
D. Orrell, S. Ramsey, P. de Atauri, and H. Bolouri. A method to estimate stochastic noise in large genetic regulatory networks. Bioinformatics, 21, 208-217, 2005. Describes a fast way to estimate fluctuations in genetic networks, without doing the detailed stochastic simulations. Based on the same techniques used to analyse error growth in weather models.
D. Orrell. Estimating error growth and shadow behavior in nonlinear dynamical systems. Int. J. Bifurcat. Chaos., 15 (10), 3265-3280, 2005. Analyses the growth of prediction errors. Includes applications to biology and weather forecasting.
D. Orrell. Filtering chaos: A technique to estimate dynamical and observational noise in nonlinear systems. Int. J. Bifurcat. Chaos, 15 (1), 99-107, 2005. Prediction error is due to observational error, and model error. This paper shows how the model drift can be used to separate the two.
D. Orrell. Ensemble forecasting in a system with model error. J. Atmos. Sci., 62 (5), 1652-1659, 2005. Shows how ensemble forecasts are adversely affected by model error in a simple system, and discusses the implications for weather forecasts.
S. Ramsey, D. Orrell, and H. Bolouri. Dizzy: Stochastic simulation of large-scale genetic regulatory networks. J. Bioinformatics Comput. Biol., 3 (2), 1-21, 2005. A computational tool developed at the Institute for Systems Biology.
D. Orrell and H. Bolouri. Control of internal and external noise in genetic regulatory networks. J. Theor. Biol., 230, 301-312, 2004. Uses techniques from nonlinear dynamics to show how feedback loops and other features can reduce stochastic fluctuations in genetic networks.
P. de Atauri, D. Orrell, S. Ramsey, H. Bolouri. Evolution of “design principles” in biology and engineering. IEE Syst. Biol., 1, 28-40, 2004. Presents a detailed mathematical model of the galactose utilization pathway in yeast, and discusses the roles of various network features.
D. Orrell. Model error and predictability over different timescales in the Lorenz ’96 systems. J. Atmos. Sci., 60, 2219-2228, 2003. Explores the connection between short, medium and long-range predictions for a “toy” weather model.
D. Orrell and L. Smith. The spectral bifurcation diagram: Visualizing bifurcations in high-dimensional systems. Int. J. Bifurcat. Chaos, 13, 3015-3027, 2003. A method to visualize the dynamics of nonlinear systems using harmonics.
D. Orrell. Role of the metric in forecast error growth: how chaotic is the weather? Tellus, 54A, 350-362, 2002. Shows that the apparent sensitivity to initial condition of weather models is largely an artefact of the measuring technique.
D. Orrell, L. Smith, J. Barkmeijer, and T. Palmer. Model error in weather forecasting. Nonlinear Proc. Geoph., 9, 357-371, 2001. Argues that weather forecast error is due mostly to model error, rather than the butterfly effect. See also No more butterfly effect, a transcript of a 2003 radio show by the Australian Broadcasting Corporation on the role of chaos in weather forecasting.
Orrell, D. Modelling nonlinear dynamical systems: chaos, error, and uncertainty. D. Phil. Thesis, Oxford University, 2001 (4 MB pdf).