One of the main challenges facing researchers today is to construct reliable predictive models for applications in systems biology. Regarding gene expression, John Quackenbush, Ph.D., from the Dana-Farber Cancer Institute, maintains that the lack of a statistical model for mRNA representation within the cell has led to a significant knowledge gap. He and his team came up with a solution known as “Mesoscopic Biology” to address this problem. In addition to defining the term and showing how Mesoscopic Biology can help overcome barriers to studying gene expression, Dr. Quackenbush during this week’s GEN podcast discusses the critical role qPCR plays in his research.
Dr. Quackenbush also illustrates how qPCR has been useful in studying gene expression in cancer, how the knowledge gap hinders predictive-model building for systems biology, and what his team was able to specifically demonstrate by employing the mesoscopic technique in their research. Be sure to listen to this topical and highly informative podcast then return to the blog to give your thoughts on the following question:
What do you see as the major barriers to obtaining a better understanding of gene expression systems and what might be some ways to overcome these obstacles?