The life sciences, and particularly medicine, are often regarded as highly empirical in nature. To this day, established journals in these domains actively pursue a policy by which publication of theoretical ideas and methods is encouraged solely when used to rationalise previously obtained experimental results. As a result, computational modelling is primarily used a posteriori to explain quantitative measurements obtained by experimental biologists. The notion that theories and models can be used in a genuinely predictive fashion, and are worth publishing in their own right and before experimental work is performed, is not widely held.
This approach is in dramatic contrast with how the physical sciences work today. However, the whole purpose of personalised medicine is the explicit use of theory and modelling to make predictions of an individual’s response to a drug or to a surgical intervention that are of sufficient fidelity that they can be used for clinical decision making. In this symposium, we shall look at the reasons for scepticism toward theory in the life sciences, provide examples of the successful use of theory in this domain, and discuss ways in which the credibility of theory, modelling and simulation can be enhanced through new approaches to education and training in biomedicine.
|An agent-based model for investigation of immunological synapse patterns|
|13:20||Benjamin Czaja||Simulation and experimental evidence for the decrease of platelet margination with an increase in volume fraction of stiffened red blood cells in flow|
|13:40||Shunzhou Wan||Accurate, Precise and Reliable Binding Affinity Predictions for G Protein Coupled Receptors|
|The Noisy Physics of Protein Signalling: Global Low Frequency Protein Motions in Allosteric Binding|