1
The previous blog mentioned crop science and the John Innes Centre (meanwhile another broadsheet obituary for Chris Lamb has appeared), and the role of research in ‘biomedical agriculture’ to produce nutritionally enhanced plants. Continuing this latter theme, I paid a visit last week to Warwick HRI at Wellesbourne, formerly Horticulture Research International and prior to that the National Vegetable Research Station. Warwick HRI was once an institute of BBSRC (it is now part of the University of Warwick), and is a leading centre for research into important horticultural crops. I was introduced to several recent recruits, many from abroad, who described some very impressive work indeed. Horticulture is an important part of BBSRC’s landscape, especially given our interest in Food Security, and it was pleasing to know that this research area has a sound intellectual (and financial) base. The genome sequence of the potato blight pathogen Phytophthora infestans has just been published online. I was pleased to note that several UK and BBSRC-funded institutes and laboratories – including Warwick HRI – have been able to participate in the experiments leading to this important milestone.
Continue reading: Horticulture, fellowships and informatics
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
Back to top