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I've been meaning to mention that molecular biologist and healthy life extension advocate Attila Chordash is in the midst of blogging the construction of his PhD thesis. His long term interest is in what he calls partial immortalization (or, alternately, systemic regenerative medicine) - as much healthy life extension as possible attained through period replacement of organs and vital cell populations, as well as via manipulation of stem cells in situ. I have been varyingly skeptical of the degree to which this alone is sufficient for radical life extension:
But it is still an interesting concept, and will clealry be explored in the years ahead, given the massive levels of funding and research interest justifiably directed towards stem cell science.
But back to the thesis, which is a good insight for those interested in what is presently going on down in the trenches of the research community:
During my PhD work I’ve done various stem cell transplantations (local and systemic) into brain, heart, muscle tissues using different stem cell sources, just like freshly isolated bone marrow derived cells (hematopoietic, mesenchymal stem cells), murine embryonic stem cells, cultured hematopoietic stem cells. And I was heavily involved in the mechanisms by which exogenous stem cells can contribute to host tissues and the way these exogenous cells and lesion models can motilize the built in endogenuous stem and progenitor cell populations.
So for me the unifying concept behind is a kind of systemic approach, that is to collect many stem cell data from various tissues, organs, compare them to each other and derive some unifying principles from them that could be adapted to other tissue environments too.
Chordash is not the only person engaged in online thesis building in the regenerative medicine space. I view this as a facet of the overall trend in scientific work towards more open access, meritocratic open review, a gift economy of information, and incremental publication by release. The present information infrastructure in the scientific community - much of it still geared to and informed by an era of paper libraries and hand-delivered mail - isn't up to the task of enabling efficient management and utilization of data at scale. Change is underway, and must go a lot further if the pace of research is to keep up with the pace of data generation. As Chordash puts it:
after all, scientists should conduct nice experiments and publish their results in short, inforich and accessible research papers in order to share it ASAP with the research community, not in book-length, otherwise unaccessible PDFs
The ideal infrastructure would look - from above the API layer - something like a vast distributed and cross-referenced database, constantly updated and constantly accessible to automated discovery and correlation agents, raw data neatly split out from conclusions and theories about that data. As even small fields grow far beyond the ability of one researcher - or one small team - to encompass and understand, automation of the time-consuming parts of academic research will become increasingly necessary.
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Posted by Reason
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