If you are a cell biologist, you will have noticed the change in emphasis in our field.
At one time, cell biology papers were – in the main – qualitative. Micrographs of “representative cells”, western blots of a “typical experiment”… This descriptive style gave way to more quantitative approaches, converting observations into numbers that could be objectively assessed. More recently, as technology advanced, computing power increased and data sets became more complex, we have seen larger scale analysis, modelling, and automation begin to take centre stage.
This change in emphasis encompasses several areas including (in no particular order):
- Statistical analysis
- Image analysis
- Automation allowing analysis at scale
- Version control
- Data storage, archiving and accessing large datasets
- Electronic lab notebooks
- Computer vision and machine learning
- Prospective and retrospective modelling
- Mathematics and physics
The application of these areas is not new to biology and has been worked on extensively for years in certain areas. Perhaps most obviously by groups that identified themselves as “systems biologists”, “computational biologists”, and people working on large-scale cell biology projects. My feeling is that these methods have now permeated mainstream (read: small-scale) cell biology to such an extent that any groups that want to do cell biology in the future have to adapt in order to survive. It will change the skills that we look for when recruiting and it will shape the cell biologists of the future. Other fields such as biophysics and neuroscience are further through this change, while others have yet to begin. It is an exciting time to be a biologist.
I’m planning to post occasionally about the way that our cell biology research group is working on these issues: our solutions and our problems.
Part of a series on the future of cell biology in quantitative terms.