Did you have trouble extending to 7 years ?
From my department and committee, I started to feel rumblings around mid-year-6 of the "you should get on with it" lines. But they weren't trying to force me out or anything, it was more like concern.
I was spending (and still do) a much bigger percentage of my time on collaborative work than my peers, and they were concerned I wasn't adequately focused on my own career. But that wasn't the case at all -- I was doing what I thought was in my best interests, especially considering my field (bioinformatics) is inherently highly collaborative compared to the wet-lab stuff going on around me.
However, there were some institution-level reasons I cut it off at 7. After that, the institution's policy is that you have to start re-taking some classes you already took (and passed). It was irritating to always have to go to an irrelevant journal club every week (I was in a department that really had nothing to do with my research). Also, I had some appealing opportunities available if I finished when I did. But in no way was I "forced out".
https://www.dropbox.com/sh/ujf73cdu4m6p1lj/AAD4vVwpCfgSf7GGo...
ps: sexy title
So I wondered "is there some semi-empirical way to find out what is 'most important' in aging so I can focus my future efforts on that?"
The solution I hit on was to take all the available gene expression data and to build a system to ask "what genes/pathways/systems change most strongly and consistently with age across species, experimental conditions, and tissues"? This would be a "core aging signature", if it exists. Obviously this is only one of many ways to answer my question and neglects epigenetics, proteomics, etc, although we're currently extending the system to DNA methylation. There is not enough high-throughput proteomics data to make it possible to do this with protein yet. We do not use sequencing for now because it is much more of a processing burden and human RNA is behind dbGaP embargo. And at the time I started this, there really wasn't that much of it compared to GEO.
My boss's interests are much more general than aging, so he encouraged me to develop the system to be more generic while still answering my question, which I did. It became a general meta-analysis system for asking "what genes change expression with <arbitrary condition> across the available experiments in GEO?" We found other things we could do with such a huge amount of expression data, and some of them are in Chapter 5.
I would say the system itself is 80-90% done. But sadly I did not get to a really detailed analysis of aging yet, although my findings so far on that are in Chapter 4.
The best aging researcher alive right now IMO though is Jim Kirkland. I've had the good fortune to work a little with him and the man is a living encyclopedia. His brilliance is obvious even in a conference full of PhDs.
From I what I could hear, he has to spend a lot of time managing funding for sub parts of the foundation and other efforts. Maybe this dilluted his claims a bit in time. All of his friends seemed to be pretty high grade researchers, it was a bit of an SF experiment sitting among that crowd.
Thanks for the name, there's another Kirkland studying aged things https://en.wikipedia.org/wiki/James_Kirkland_(paleontologist... but well .. couldn't resist that joke.
The panel I attended was the ICSA 2017 at Paris (Pasteur) http://www.icsa2017-senescence-on-the-seine.org/
Kirkland wasn't there but a nice international bunch
They were supposed to plan for a sequel since a few hundred people attended their surprise talk; we'll see.