Batch-to-batch variability of two human designer cell lines-AGE1.HN and AGE1.HN.AAT - carried out by different laboratories under defined culture conditions using a mathematical model

Freund S, Rath A, Barradas OP, Skerhutt EM, Scholz S, Niklas J, Sandig V, Rose T, Heinzle E, Noll T, Poertner R, et al. (2013)
Engineering In Life Sciences 13(6): 580-592.

Journal Article | Published | English

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Abstract
Systems biology approaches involve collaboration of a larger number of research groups. Experiments are being performed in different laboratories dealing with different aspects of the topic of interest. Therefore, comparability of data collected for further analysis and modeling needs critical assessment. Here, growth and product formation of two human designer cell lines (AGE1.HN and its 1-antitrypsin producing clone AGE1.HN.AAT) were investigated by four research laboratories. Cell lines were cultivated in shake flasks and stirred tank bioreactors operated at standardized conditions using a chemically defined medium, and a simple mathematical model was used to estimate characteristic process parameters. Results obtained for 35 batches showed that neither the initial viable cell concentration nor the initial concentration of glucose and glutamine showed significant differences between laboratories. For these measurements with low variations, specific growth rate and yields varied between 8.5 and 26% (relative standard error), indicating comparability of cultivations between laboratories for exponential growth. Higher variations of fitted parameters related to measurements with high initial variation. Comparing the nonproducing with the producing cell line, no significant differences were found regarding growth dynamics and metabolism. Overall, it seems justified to draw conclusions based on the entire experimental dataset of this systems biology project.
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Freund S, Rath A, Barradas OP, et al. Batch-to-batch variability of two human designer cell lines-AGE1.HN and AGE1.HN.AAT - carried out by different laboratories under defined culture conditions using a mathematical model. Engineering In Life Sciences. 2013;13(6):580-592.
Freund, S., Rath, A., Barradas, O. P., Skerhutt, E. M., Scholz, S., Niklas, J., Sandig, V., et al. (2013). Batch-to-batch variability of two human designer cell lines-AGE1.HN and AGE1.HN.AAT - carried out by different laboratories under defined culture conditions using a mathematical model. Engineering In Life Sciences, 13(6), 580-592.
Freund, S., Rath, A., Barradas, O. P., Skerhutt, E. M., Scholz, S., Niklas, J., Sandig, V., Rose, T., Heinzle, E., Noll, T., et al. (2013). Batch-to-batch variability of two human designer cell lines-AGE1.HN and AGE1.HN.AAT - carried out by different laboratories under defined culture conditions using a mathematical model. Engineering In Life Sciences 13, 580-592.
Freund, S., et al., 2013. Batch-to-batch variability of two human designer cell lines-AGE1.HN and AGE1.HN.AAT - carried out by different laboratories under defined culture conditions using a mathematical model. Engineering In Life Sciences, 13(6), p 580-592.
S. Freund, et al., “Batch-to-batch variability of two human designer cell lines-AGE1.HN and AGE1.HN.AAT - carried out by different laboratories under defined culture conditions using a mathematical model”, Engineering In Life Sciences, vol. 13, 2013, pp. 580-592.
Freund, S., Rath, A., Barradas, O.P., Skerhutt, E.M., Scholz, S., Niklas, J., Sandig, V., Rose, T., Heinzle, E., Noll, T., Poertner, R., Zeng, A.P., Reichl, U.: Batch-to-batch variability of two human designer cell lines-AGE1.HN and AGE1.HN.AAT - carried out by different laboratories under defined culture conditions using a mathematical model. Engineering In Life Sciences. 13, 580-592 (2013).
Freund, Susann, Rath, Alexander, Barradas, Oscar Platas, Skerhutt, Eva Maria, Scholz, Sebastian, Niklas, Jens, Sandig, Volker, Rose, Thomas, Heinzle, Elmar, Noll, Thomas, Poertner, Ralf, Zeng, An Ping, and Reichl, Udo. “Batch-to-batch variability of two human designer cell lines-AGE1.HN and AGE1.HN.AAT - carried out by different laboratories under defined culture conditions using a mathematical model”. Engineering In Life Sciences 13.6 (2013): 580-592.
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