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The week began (leaving aside a historic victory at Lord’s) with a meeting with several other funders designed to ensure joined-up strategies for the funding of research into animal health, a topic of considerable importance to BBSRC. Other activities included a useful dinner with the Technology Strategy Board, a monthly meeting of the RCUK (Chief) Executive Group, a talk from John Wilbanks (Executive Director of the Science Commons and a leading light at Creative Commons) at the British Library on Scientific findings in a digital world, and co-chairing a session on Systems Biology at the major biennial Yeast Genetics and Molecular Biology meeting, held this year in Manchester.
Continue reading: Animal health, digital biology, yeast, and iron
The recipe for much of modelling in systems and network biology is comparatively easy. First one establishes the topology or ‘structure’ of the network (the curly arrow version seen in wallcharts – such as those for metabolism, now available electronically – of ‘who talks to whom?’). Then one finds out the equations – such as that of Henri, Michaelis and Menten – describing the effect of the concentrations of the various interacting partners on the rate of the reaction step involved. Finally one determines the parameters of those equations (such as the rate constants, measured using the methods of molecular enzymology, and ‘fixed concentrations of substrates and other effectors). Armed with the model, it is then straightforward to run the model using appropriate software (e.g. the COPASI software found on the web) either using ordinary differential equations or when necessary stochastic methods. These give the time evolution of the system variables – typically the concentrations and fluxes of molecules – and one may also determine the local or global sensitivities and summation laws of every variable to every parameter. Even for large models, modern algorithms (including those for solving so-called ‘stiff’ ordinary differential equations) require comparatively little computer power.
Continue reading: When scientific progress means going backwards: reverse engineering of biochemical networks
One of the biggest problems confronting science right now is how we deal with the floods of data (not least genomics data), and in particular how we visualise them. En route to the Science Foo camp (SciFoo) held at the Googleplex over last weekend, I was privileged to be shown, by its curator Bonnie DeVarco and her collaborator Eileen Clegg, a wonderful exhibition of scientific visualisations (data visualisations) at MediaX at Stanford. It is also online.
Continue reading: Scientific data visualisation and #SciFoo09
Last week began with a presentation – in a session with John Barrett from the Department for International Development (DfID) – at a workshop we co-sponsored on the subject of Food Security and Sustainability at the World Congress of Science Journalists in London. My message – and that of the other speakers – was that while demographic and other factors are alerting us to the need to do something to sustain Food Security, if there is a crisis coming it is an avoidable crisis if we invest in the right kinds of scientific research now. Previous generations of agricultural research and innovation have boosted yields considerably, and we need to continue this – this time with fewer inputs of fertilizer and water. Our public consultation on Food Security lists the relevant issues.
Continue reading: Another agricultural revolution for food security – from genomics to farming practices and data management
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