Multi-factorial experimentation is essential in understanding the link between mammalian cell culture conditions and the glycoprotein product of any biomanufacturing process. This understanding is increasingly demanded as bioprocess development is influenced by the Quality by Design paradigm. We have developed a system that allows hundreds of micro-bioreactors to be run in parallel under controlled conditions, enabling factorial experiments of much larger scope than is possible with traditional systems. A high-throughput analytics workflow was also developed using commercially available instruments to obtain product quality information for each cell culture condition. The micro-bioreactor system was tested by executing a factorial experiment varying four process parameters: pH, dissolved oxygen, feed supplement rate, and reduced glutathione level. A total of 180 micro-bioreactors were run for 2 weeks during this DOE experiment to assess this scaled down micro-bioreactor system as a high-throughput tool for process development. Online measurements of pH, dissolved oxygen, and optical density were complemented by of fine measurements of glucose, viability, titer, and product quality. Model accuracy was assessed by regressing the micro-bioreactor results with those obtained in conventional 3 L bioreactors. Excellent agreement was observed between the micro-bioreactor and the bench-top bioreactor. The micro-bioreactor results were further analyzed to link parameter manipulations to process outcomes via leverage plots, and to examine the interactions between process parameters. The results show that feed supplement rate has a significant effect (P < 0.05) on all performance metrics with higher feed rates resulting in greater cell mass and product titer. Culture pH impacted terminal integrated viable cell concentration, titer and intact immunoglobulin G titer, with better results obtained at the lower pH set point. The results demonstrate that a micro-scale system can be an excellent model of larger scale systems, while providing data sets broader and deeper than are available by traditional methods. Biotechnol. Bioeng. 2009; 104: 1107-1120. (C) 2009 Wiley Periodicals, Inc.