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Many years ago (1978) I got a PhD in biochemistry from MIT, and for the last 13 years or so I have been consulting in biochemistry and data analysis. I have a long-standing interest in modeling of biological systems at the molecular level and I suppose the modeling is what I am most interested in talking about.

Currently I am fitting models of metabolic pathways using Markov chain Monte Carlo (MCMC) simulation. The results of a MCMC simulation are very nice in that they provide joint probability density estimates for the parameters instead of the usual point estimates. If the model parameters are correlated, the joint density is needed for reliable estimates of functions of the parameters such as confidence intervals. The software I am using is based on the Hydra library.

So far, I have used data simulated with the Gillespie algorithm. A perl script that writes C source code and a configuration file is downloadable as a tarball. In my master's thesis from the University of Rhode Island I used MCMC simulation to fit Michaelis-Menten-type equations of irreversible reactions to sequences of enzymatic reactions. The results are very nice (I think!) and they demonstrate the usefulness of MCMC simulation, but unfortunately most enzymatic reactions are reversible. Now I am using the Haldane equation for reversible reactions and subjecting the results to Metabolic Control Analysis (MCA). At this point I can fit the Haldane equations but I have to use a simulation of several of several steady states, and I am not sure how to extract useful information with MCA. All suggestions and comments are welcome.

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