PDW_Eppendorf_DASbox_0.jpgThe Bioexpression and Fermentation Facility recently acquired the DasBox system from Eppendorf.  The system is capable of independently controlling 4 x 250 mL bioreactors. This new capability will the BFF to accelerate process development projects in cell culture.  Along with our Celligen 310 system we will be able to develop processes and scale to 40L within the facility.  Since both systems are stirred tank reactors, scaling will be more predictable.  The parallel processing capabilities will allow for Design of Experiments (DOE) methodologies. 

What is Design of Experiments (DOE)?

Most people have faced this problem: I have a gene of interest (GOI) that I want to express, but how do I determine the best conditions for expression?  The most common way is to search for a paper where your GOI is expressed. If no paper exists then people default to their favorite method. For transient transfection many researchers immediately go to DNA:PEI ratio modification thinking that this will give them the best chance for expressing soluble protein. Some may go with a standard media at 37 °C and introduce an additive or a feed. What is the best method?  What about other factors such as temperature, cell density or timing of induction?  For stable cell lines the same questions can also be applied.
For bioreactor process development especially when companies are working with a CRO, cost is a major factor. How do you screen for the best conditions without breaking the bank! One methodology that may be of use is design of experiments or DOE. Using DOE can reduce the total number of runs required to reach a target expression level.  This also saves time to get your product to market which is critical in many cases.
DOE is a principle of statistics whereby multiple factors are tested simultaneously at different levels and then sophisticated software will deconvolute the results and define the optimal parameters.  There are several different software packages that can assist in setting up and performance analyses.  DOE is different than traditional one factor at a time (OFAT) approaches in that more than one factor is varied across a series of experiments allowing more conditions to be tested in fewer number of experiments.