How about a simplification: chance that computational technology will increase by 3 orders of magnitude in 30 years: almost 100%. Chance that increases in computational power lead to increases in biological breakthroughs: 100%.
Therefore, we should not measure the current rate of anti-aging research but rather project it exponentially in some fashion to match the likely trajectory. This has been true for DNA sequencing for example.
>chance that computational technology will increase by 3 orders of magnitude in 30 years: almost 100%.
Define "computational technology". If you mean something along the lines of storage space or parallel processing power, I'll grant you that. We're probably not getting another 1000x increase in processor clock-speeds (serial processing power), though.
>Chance that increases in computational power lead to increases in biological breakthroughs: 100%.
This is exactly why I mentioned data science/machine learning. These are the tools we use to turn increasing amounts of computer processing power into significant, reproducible scientific advancements.
Besides, if I want to be ludicrously optimistic and base everything on computing I could simply yell, "Chance of solving the FAI problem in 30 years: almost 100%! EVERYTHING WILL BE FINE, GUYS!".
Your point about sequencing is a good one, but this is the only area of biological research clearly improving exponentially...
Without being too argumentative, I'd put the odds of increases in computer power leading to biological breakthroughs at much less than 100%! I'd be surprised if it was as much as 5-10%.
What areas of biological research are fundamentally limited by computer power? I can think of a few where more power would be nice to have, but none where that power would be transformative.
The possible exception is the example you mention---DNA sequencing--but the problem there is that the rate of growth of sequence information is greater than More's rate; slightly ironically, this field really needs computer scientists to help develop new algorithms much more than it needs computers...
But your question is specifically about computational power. Any area that requires modeling or methods that must be over-simplified in order to run on today's hardware are candidates for benefit from computational power. That seems to leave a lot of room: http://en.wikipedia.org/wiki/Modelling_biological_systems
But I will grant that development of new algorithms is also important. It's just that computational power itself assists in the discovery of new algorithms via the development of better research platforms.
Therefore, we should not measure the current rate of anti-aging research but rather project it exponentially in some fashion to match the likely trajectory. This has been true for DNA sequencing for example.