The robustness of mathematical models for biological systems is studied by sensitivity analysis and stochastic simulations. Using a neural network model with three genes as the test problem, we study robustness properties of synthesis and degradation processes. For single parameter robustness, sensitivity analysis techniques are applied for studying parameter variations and stochastic simulations are used for investigating the impact of external noise. Results of sensitivity analysis are consistent with those obtained by stochastic simulations. Stochastic models with external noise can be used for studying the robustness not only to external noise but also to parameter variations. For external noise we also use stochastic models to study the robustness of the function of each gene and that of the system.
@article{908,
title = {Robustness of mathematical models for biological systems},
journal = {ANZIAM Journal},
volume = {45},
year = {2004},
doi = {10.21914/anziamj.v45i0.908},
language = {EN},
url = {http://dml.mathdoc.fr/item/908}
}
Tian, Tianhai. Robustness of mathematical models for biological systems. ANZIAM Journal, Tome 45 (2004) . doi : 10.21914/anziamj.v45i0.908. http://gdmltest.u-ga.fr/item/908/