Details of Grant 

EPSRC Reference:	EP/I036680/1
Title:	Development, validation and application of population-based pulmonary disease models using robustness analysis and ensemble forecasting
Principal Investigator:	Bates, Professor D
Other Investigators:	Stephenson, Professor DB Hardman, Professor JG
Researcher Co-Investigators:
Project Partners:
Department:	Engineering Computer Science and Maths
Organisation:	University of Exeter
Scheme:	Standard Research
Starts:	01 December 2011	Ends:	31 May 2013	Value (£):	451,693
EPSRC Research Topic Classifications:	Control Engineering Medical science & disease
EPSRC Industrial Sector Classifications:	Healthcare
Related Grants:
Panel History:	Panel Date Panel Name Outcome 19 Apr 2011 Materials,Mechanical and Medical Engineering Announced
Summary on Grant Application Form
Lung diseases affecting the critically ill (acute respiratory distress syndrome (ARDS), pneumonia and ventilator-associated lung injury) are acute, severe injuries affecting most or all of both lungs. Patients with these conditions experience defects in gas-exchange and tissue oxygenation and often require mechanical ventilation (life support) because of respiratory failure. While medical researchers and physiologists have been studying these disease for many years, very limited progress has been made in understanding the underlying causes and mechanisms (especially of ARDS and VALI). As a result of the explosive developments and demonstrated successes of molecular systems biology in the last decade, many researchers are now attempting to apply similar systems-based computational approaches to develop and analyse physiological simulation models at the organ level. However, the uncertainty that arises due to patient and disease heterogeneity, and the difficulty in rigorously validating simulation models which take this uncertainty into account, represent serious roadblocks to progress in applying systems approaches in a clinical setting. We propose to tackle both of these problems by adopting a highly novel and interdisciplinary approach. Using approaches from the field of Control Engineering, we will develop population-based disease models which explicitly take account of uncertainty and variability both within a single patient and between diverse members of a patient population, and rigorously validate these models using advanced robustness analysis techniques. By adapting ensemble modelling techniques from the field of climate forecasting, we will develop reliable descriptors of disease severity and novel disease-specific therapeutic strategies that are applicable to populations of individual patients, rather than a single "typical" patient. By transferring state-of-the-art technologies from control engineering and climate science into critical-care medicine, this project will deliver a significant improvement in the understanding, diagnosis and treatment of ARDS, pneumonia and VALI, and a corresponding reduction in both their impact on patients and the associated cost to the NHS.
Key Findings
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Organisation Website:	http://www.ex.ac.uk