Last week, I was sitting in a lab meeting, listening to about 8 other lab mates’ work, each of them working on different diseases, targeting different proteins and different organs, using different viral vectors. It stuck me that there was only a very broad unifying theme in this superlab. Sometimes I think we all speak a different language! I started wondering about how the structure of a research lab can affect its efficiency.
Last week, I was sitting in a lab meeting, listening to about 8 other lab mates’ work, each of them working on different diseases, targeting different proteins and different organs, using different viral vectors. It stuck me that there was only a very broad unifying theme in this superlab. Sometimes I think we all speak a different language! I started wondering about how the structure of a research lab can affect its efficiency. The doodle on my notepad looked something a bit messier than this:
Traditionally, academic research labs were small, and focused on answering basic research questions about TFP (Their Favourite Protein) or TFV (Their Favorite Virus). As technologies advance, a number of labs pioneered certain specialist techniques, such as mass spectrometry, or multicolor flow cytometry, making them more accessible for investigating various scientific questions. This type of research requires significant investment in equipment, as well as expert knowledge, and these labs sometimes function as ‘core facilities,’ offering the service to other labs. The lab I work in now is a merge between a methods lab and smaller disease-focused mini-labs. We’re big and powerful in the field, but what is the most efficient way of organizing a lab? Are most discoveries really made in superlabs which constantly seem to effortlessly get their papers in high-impact journals?
History shows us that successful, clinically-applicable innovations are often actually made in smaller labs where (possibly slightly obsessed and nerdy!) scientists come up with a brilliant new idea. For instance, the dawn of cloning and recombinant proteins occurred when biologists discovered and characterized restriction enzymes from bacteria. This technology was the foundation for Genentech, which went on to create therapies that have changed the lives of hundreds of thousands of patients.
A number of big pharmas have completely revamped their organizational structures in an effort to recreate the fractionated but focused environments which have been so successful for biotech. For example, GlaxoSmithKline restructured its R&D in disease-focused CEDDs (Centers for Excellence for Drug Discovery), TAUs (Therapy Area Units) and even smaller DPUs (Drug Performance Units –don’t they love their acronyms!). The success of these changes has yet to be reported in terms of drugs approved, but another new approach pharma are taking to fill their drug pipelines is to partner up with academic labs. Is there a pattern here?
If you’re choosing a lab, try to identify whether the focus is on a basic science question, a technology or a translational goal. How novel is it? What is the size of the lab, and is it efficient? Are there supporting ‘cores’ or collaborating labs that will be able to help and brainstorm with you and stimulate you? Where will the research be published and how will it contribute to advancing science? Your research dreams are out there, you just gotta chase ‘em!
