So, I stayed up all night and I worked on this stuff for at least 10 hours straight. I got like 8 out of 10 differential equations set up to where they can be solved nicely in Simulink, but of course the last two consist of some crazy math/chemistry equilibrium process using Michaelis–Menten equations (at least the paper mentioned it so I am not freaking out on what to do!). I have to solve for some crazy stuff in the steady state, kind of described here: http://www-jmg.ch.cam.ac.uk/tools/magnus/michmenten.html
. I'm not that good at chemistry or thermodynamics, either. When I finish this crazy stuff, I will be at least halfway done with Hovorka mathematical modeling, which is by far the most challenging part of my entire project. The only challenging stuff I have left for Hovorka's work after the Simulink DiffEQ stuff is intrinsic Gaussian conditional autoregression with random walks for 3 probability distributions (not bad) and Markov chain Monte Carlo, with rejection Gibbs sampling for nonlinear hierarchical stochastic differential equations. So, I am almost out of the woods, when it comes to completing the really difficult stuff. After that, unless the nonlinear model predictive controller turns out to be a challenge, the only issues I will face are programming hurdles and logistic issues.