Technological advances in the field of cDNA microarrays have facilitated quick access to mRNA expression data, which can be useful in enhancing basic understanding of cell culture processes. However, microarray analyses generate large datasets that often pose a challenge to interpret. In this study, we developed a workflow to analyze high-dimensional time course gene expression data generated for a fed-batch bioreactor process. For this we implemented the use of data pre-processing methods in conjunction with sparse partial least squares, to identify the specific transcripts whose expression correlated well with phenotypic changes observed in the culture. Next we utilized a pathway analysis tool to shed light upon the underlying biological context that impacted cell culture performance in the fed-batch bioreactors. Of the approximately 13,528 genes with higher-than-threshold expression in our dataset, we were able to narrow down to 380 of interest, which helped significantly in identification of biological pathways of relevance.

Time course gene expression data analysis in fed-batch bioreactors

245th National Meeting (2013)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.