Modelling Biochemical Dynamics using Time-Varying S-Systems (BioSysBio 2009)

W-H Huang et al. (presented by F-S Wang)
National Chung Cheng University

Almost no lit has so far talked about modelling power-law models with time-varying parameters. In their model formulation, they used a time varying S-Sytem model. The rate coefficients and the kinetic orders are the time-varying parameters. Several basic functions such as block pulse functions, Lagrange polynomials, and orthogonal polynomials can be used to estimate the time-varying parameters. The model parameters for each time scale are constants.

There are two main challenges to parameter estimation: ODE solving and optimization. They developed a modified collocation approach, which is similar to the conventional method except for the approximation technique. Evolutionary algorithms can be applied to overcome drawbacks to optimization using gradient-based methods. They propose a global-local search method. They also describe Hybrid differential evolution (HDE). They did both wet and in silico experiments. For the latter, they found that the time-varying model fits the experiments very closely, much better than the time-invariant models. The wet-lab study was a kinetic model of ethanol fermentation using mixed sugars. The same conclusion, that the time-invariant model did not closely follow the experiment, while the time-varying one did, was found for this experiment.

DEs including constant parameters are commonly used to model biochemical systems. Such a time-invariant model cannot cover all dynamic behaviour – a time-varying S-system model has been developed by them to overcome these limitations. This model is a close fit with experimental data.

Tuesday Session 1

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot – just let me know!

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