Accelerating Genetic Engineering

Throughout the history of science, those disciplines capable of controlled experimentation have advanced rapidly (e.g. physics) while those with limited capability of this sort have hardly made any progress in comparison (e.g. social sciences or economics). It would be no exaggeration to say that the tweak-it-and-see-what-happens-then approach is the key to gaining insight into how systems operate and how they can be changed to our advantage. Beyond science, it also appears to be the base principle for learning of any kind (consider language development in children, for example). For efficiency, it is crucial that the tweaking occurs in a controlled fashion, ceteris paribus, completely at our will, not disturbed by what is known as "confounding factors" in statistics.

Consider debugging computer software as another example (or troubleshooting of any kind, if you are not into software). If the computer program under inspection only changes its behavior in response to the programmer's modifications and inputs under her control, then the task of understanding and shaping it into whatever form is desired is mostly trivial. However, if there are unknown varying inputs that influence the program's behavior on each run, which mask the programmer's corrective actions, the debugging task becomes a nightmare, or at least calls for statistical analysis (not commonly available to real-life programmers). The same sort of problems arises if the modifications available to the programmer are too coarse-grained, e.g. if she can only replace large (and needed) components rather than "dig inside" and fix them.

It appears that researchers in Genetic Engineering have very recently made a breakthrough by gaining the ability to not just observe, but also tweak their "programs" in a piecewise, controlled fashion. Watch this presentation by Craig Venter to learn more: From Darwin to New Fuels (In A Very Short Time). They now expect that the progress will be greatly accelerated by this capability, and looking at the history, there is every reason to believe them. The potential for grim accidents is also there, of the same sort which is present in software systems. The same tweak-and-see techniques that are so helpful in offline development environments can wreak havoc when (or rather if) applied in production systems. (Most) programmers are smart enough to make the distinction. The same must be expected from genetic engineers.