Std. bioprocess conditions have been widely applied for the microbial conversion of raw material to essential industrial products. Successful metabolic engineering (ME) strategies require a comprehensive framework to manage the complexity embedded in cellular metab., to explore the impacts of bioprocess conditions on the cellular responses, and to deal with the uncertainty of the physiochem. parameters. We have recently developed a computational and statistical framework that is based on Metabolic Control Anal. and uses a Monte Carlo method to simulate the uncertainty in the values of the system parameters [Wang, L., Birol, I., Hatzimanikatis, V., 2004. Metabolic control anal. under uncertainty: framework development and case studies. Biophys. J. 87(6), 3750-3763]. In this work, we generalize this framework to incorporate the central cellular processes, such as cell growth, and different bioprocess conditions, such as different types of bioreactors. The framework provides the math. basis for the quantification of the interactions between intracellular metab. and extracellular conditions, and it is readily applicable to the identification of optimal ME targets for the improvement of industrial processes [Wang, L., Hatzimanikatis, V., 2005. Metabolic engineering under uncertainty. II: anal. of yeast metab. Submitted]. [on SciFinder (R)]