Hi Jerry,
If I understand your problem correctly, you are trying to figure out how to change your SimBiology model so that the simulation results better match your expectations. I can think of two features in SimBiology to help in this situation: sensitivity analysis and parameter estimation. The following example illustrates both of these:
http://www.mathworks.com/products/simbiology/examples.html?file=/products/demos/shipping/simbio/gprotein.html
To go in a little more detail on the use of sensitivity analysis, it sounds like you are most interested in how things affect species S. So you could run a sensitivity analysis simulation with S as your sensitivity output and all the parameters (and probably species initial concentrations) as your sensitivity inputs. If you see a sensitivity with a large magnitude over the entire simulation, that might identify a good parameter or initial concentration to adjust in order to decrease the magnitude of S. If you see a sensitivity that changes after 2 hours, that might identify a good parameter or initial condition to adjust in order to cause the dynamics of S to occur at later times.
After you've identified the parameters and initial conditions of interest, you can either manually try varying their values or you can try to estimate the parameters using the function sbioparamestim. This function requires you to have a time course that you are trying to fit the data to, so if you don't have real experimental data you're trying to fit, you'll need to come up with synthetic data that represents the dynamics you are trying to reproduce. Then, you can try to estimate the parameters and initial conditions that you identified as important from sensitivity analysis.
Don't be surprised if the parameter estimation doesn't immediately converge to a good set of of parameters. This is a very difficult optimization problem that is often very sensitive to your initial guesses for the optimal parameter values. If you need to explore a large range of parameter values, you may find it useful to use one of the optimization methods provided to sbioparamestim via the Global Optimization Toolbox (pattern search, genetic algorithm, and particle swarm optimization).
-Arthur
"Jerry " <
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