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Motivation: Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists.
Modeling the robustness of these networks with respect to the dysfunction of one or several reactions is important to understand the basic principles of biological network organization, and to identify new drug targets.
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Applying a noise model the robustness properties of the algorithm are enhanced considering plant-model mismatch.
In contrast to the two-loop model, the robustness analysis demonstrated that the model is extremely sensitive to a specific minor group of parameters, which are generally conserved across all reference parameter sets.
As the optimization model takes results from the evaluation model, the robustness of the optimization results therefore also depends upon the validity of the evaluation model.
Results: We propose a new model —the flux balance impact degree —to model the robustness of large metabolic networks with respect to gene knock-out.
Although, SCOUT does not explicitly include batch effects in its statistical model, the robustness of its model-fitting procedure at small sample sizes allows known batch effects to be remedied by independent scoring of batches.
First, a major challenge of parametric methods, like the logistic model, is the robustness and reliability of the modeling.
Feeding enough observations to a nonlinear model ensures the robustness and precision of plant growth modeling.
The results clearly indicate that the interlocking model increases the robustness to parameter variation over the simpler Goodwin model, while the parallel model decreases the robustness.
It is shown that the solution obtained using the deterministic model cannot outperform the result of the stochastic model considering the robustness and opportuneness criteria.
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