2010-2013, World Finance
Column on economics for World Finance magazine.
The Institute of Ideas, No. 4, Fall 2013
Kruszac krysztalowe sfery (Shattering the crystal spheres)
Article for Poland's Instytut Obywatelski (Civic Institute)
Winter 2013, Foresight
November 2012, Huffington Post
Science and the power of aesthetics.
November 2012, Strike! magazine
Article for inaugural issue of Strike! magazine.
October 2012, Books magazine
Extract from Le Crépuscule de l'Homo Economicus (The Twilight of Homo Economicus).
March 2011, Adbusters 94
Extract from Economyths. Section reprinted November 2011 in Adbusters 98, and in Meme Wars: The Creative Destruction of Neoclassical Economics by Lasn and Adbusters (2012). Also available in Spanish, Portuguese, and audio versions.
March 2011, Books magazine
French translation of a Literary Review of Canada review of Megadisasters: The Science of Predicting the Next Catastrophe by Florin Diacu.
February 2011, Foresight
Review of This Time Is Different: Eight Centuries of Financial Folly by Carmen M. Reinhart and Kenneth S. Rogoff
January 2010, Literary Review of Canada
Researchers have developed models to predict everything from earthquakes to pandemics. The trouble is, they don't work. A review of Megadisasters: The Science of Predicting the Next Catastrophe by Florin Diacu.
July 2009, Adbusters
Modern economics is based on a Pythagorean paradigm. Article first published in 2006, reprinted 2009. Also available in Spanish translation.
April 2009, Foresight: The International Journal of Applied Forecasting
Review of Adam Gordon's book Future Savvy: Identifying Trends to Make Better Decisions, Manage Uncertainty, and Profit from Change.
Selected research publications
Systems Biology Approaches to Cancer Drug Development. C. Snell, D. Orrell, E. Fernandez, C. Chassagnole and D. Fell
In A. Cesario, F. Marcus (eds.), Cancer Systems Biology, Bioinformatics and Medicine, Springer, 2011.
Responds to Adam Gordon's article in issue 19 of Foresight.
Using predictive mathematical models to optimise the scheduling of anti-cancer drugs. D. Orrell and E. Fernandez.
Innovations in Pharmaceutical Technology, 59-62, June 2010
Predicting the best schedules for drug combination treatments.
Systems economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach. D. Orrell and P. McSharry.
International Journal of Forecasting, 25, 734-43, 2009 (abstract)
Special issue on "Decision Making and Planning Under Low Levels of Predictability." Discusses the problems faced in predicting complex systems ranging from the human body to the economy, and how some of the methodologies of systems biology can be applied to economics.
Reply to commentaries by Roy Batchelor, Paul Goodwin and Robert Fildes on "A systems approach to forecasting".
Discusses new predictive tools for complex systems such as the economy.
Dual feedback loops in GAL regulon suppress cellular heterogeneity in yeast. S. Ramsey, J.J. Smith, D. Orrell, M. Marelli, T.W. Petersen, P. de Atauri, H. Bolouri, J.D. Aitchison.
Nature Genetics, 38, 1082-1087, 2006 (abstract)
Presents experimental results which explore the role of feedback loops in a genetic network.
Feedback control of stochastic noise in the yeast galactose utilization pathway. D. Orrell, S. Ramsey, M. Marelli, J.J. Smith, T.W. Petersen, P. de Atauri, J.D. Aitchison, H. Bolouri.
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.
A method to estimate stochastic noise in large genetic regulatory networks. D. Orrell, S. Ramsey, P. de Atauri, and H. Bolouri.
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.
Estimating error growth and shadow behavior in nonlinear dynamical systems. D. Orrell
Int. J. Bifurcat. Chaos., 15 (10), 3265-3280, 2005.
Analyses the growth of prediction errors. Includes applications to biology and weather forecasting.
Filtering chaos: A technique to estimate dynamical and observational noise in nonlinear systems. D. Orrell
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.
Ensemble forecasting in a system with model error. D. Orrell
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. See also this unpublished earlier version which includes a test for ensemble error.
Dizzy: Stochastic simulation of large-scale genetic regulatory networks. S. Ramsey, D. Orrell, and H. Bolouri.
J. Bioinformatics Comput. Biol., 3 (2), 1-21, 2005
A computational tool developed at the Institute for Systems Biology.
Control of internal and external noise in genetic regulatory networks. D. Orrell and H. Bolouri.
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.
Evolution of "design principles" in biology and engineering. P. de Atauri, D. Orrell, S. Ramsey, H. Bolouri.
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.
Model error and predictability over different timescales in the Lorenz '96 systems. D. Orrell.
J. Atmos. Sci., 60, 2219-2228, 2003.
Explores the connection between short, medium and long-range predictions for a "toy" weather model.
The spectral bifurcation diagram: Visualizing bifurcations in high-dimensional systems. D. Orrell and L. Smith.
Int. J. Bifurcat. Chaos, 13, 3015-3027, 2003.
A method to visualize the dynamics of nonlinear systems using harmonics.
Role of the metric in forecast error growth: how chaotic is the weather? D. Orrell.
Tellus, 54A, 350-362, 2002.
Shows that the apparent sensitivity to initial condition of weather models is largely an artefact of the measuring technique.
Model error in weather forecasting. D. Orrell, L. Smith, J. Barkmeijer, and T. Palmer.
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.