Today's news has a story of a report that female mice continue to generate at least some new egg cells after they are born. Eggs, as well as heart and other muscle, and most types of neurons, were for a long time believed to be post-mitotic: that is, they could not be regenerated. But if various reports are accurate, all of these types of cells can regenerate at least to some extent.
If the exceptions to the once-held 'rules' are real, and not trivial, this can be potentially good news for the development of therapeutic approaches in which an individual's own cells could be used to generate lost or damaged cells. But it also raises some interesting basic scientific questions, too.
Most if not all of these results come from animal models. So why is it touted as good news for humans? If one is a fervent Darwinist, and thinks that the pressure of competition is always pushing species towards ever more specialized 'adaptive' states, then there is no reason to expect that human cells would behave the same way as those of laboratory models. But, if one believes that animal models, such as mice or chicks (much less flies and flatworms!) represent the human state, one is correspondingly less rigidly Darwinian.
The issue is a practical one. Regardless of one's views about natural selection, we know that our models are only approximate: but to what extent can we trust results from work with animal models? Many of us work with such models every day (in our case, with mice) and daily lab life can be very frustrating as a result, because it is easy to see that not even the animal models are internally consistent or invariant. But there is an even more profound issue here.
Whether we're working with animal models or taking some other approach, we design our research, and interpret our results, in light of what we accept ('believe'?) to be true. If what we accept is reasonably accurate, we can do our work without too much concern. But if our basic assumptions are far from the truth, we can be way off.
As we noted in another post, every scientist before today is wrong. We are always, to some extent, fishing in the dark. It's another frustration, when you build a study around a bunch of published papers, and then discover that they've all copied one another on basic assumptions and, like the drunk looking for his keys under the lamp-post, have been exploring the same territory in ever-increasing, but perhaps ever more trivial detail.
Yet in fact we can never know just where our assumptions may be wrong. On the other hand, we can't just design new experiments, presumably to advance knowledge beyond its current state, without in some sense building upon that state. How can one be freed of assumptions--such as interpreting data as if certain cell types cannot divide and replenish--and yet do useful research?
There is no easy answer, perhaps no answer at all except that we have to keep on plugging away unless or until we realize we're getting nowhere, or someone has a better, transformative idea. For the former, we're trapped in the research enterprise system that presses us to keep on going just to keep the millwheels turning. For the latter, we have to wait for a stroke of luck, and they may only come every century or more.