I wouldn't call it virtuous, but it is deeply intellectually satisfying.
Agree about biologists not being ready - computational biology needs more computer scientists, not more biologists.
Why do you think "improving the efficiency of photosynthesis" will have a greater impact on global food security than improving the efficiency of social and commercial networking? If I'm not mistaken, economists (eg Amartya Sen) agree that food insecurity is caused by dysfunction in the distribution mechanism, not by a lack of supply (so growing more food won't necessarily help).
IMHO, the important problems in the world are much more social and political, than technological. The work twitter does may easily have greater beneficial impact, direct or indirect (Arab spring and all that), on global food security than working for Monsanto on GE crops. I wouldn't be so self-righteous for choosing to work on "hard" science problems. Are you really doing it for the benefit of the world, or just for the deep satisfaction of your own curiosity?
There are huge problems to solve in all those areas. The biological problems are made more important by the lack of progress in solving the world equality problems. By 2050, when the world population is something in the region of 9-12 billion, either billions will be starving or we will have solved one or more of those problems. The science problems are tractable, while the others are ill defined and involve many factors we cannot control, so I think there's a stronger moral imperative to work on the science.
The other consideration is that working in a job that, by chance, invokes positive social results is not equivalent to working directly on trying to solve a problem. Progress in science suffers because there aren't enough good people working on these problems, because so many of them are seduced by industry.
I don't work for Monsanto; that's a straw man. We're talking about academic computational biology jobs.
The answer to your last question is: both. I couldn't do a job where I didn't satisfy my curiosity. But I know working in tech would do that just fine - there are hard problems in many fields. I chose science because I want to use whatever skills I have to try to solve the problems I see.
Having experience in both fields, I don't work in plant genomics because I want to feed more people (I do, but if that's what solely motivated me I'd be working under economics still). I do it because genetics is awesome, and plants are great to study.
But, I'd argue that the hard sciences are always a good worth investing in. Being capable of trying to understand our world with the scientific method is something that is uniquely human. We should use this talent as much as possible. Drosophila (fruit fly) genetics is a great example. Decades ago, drosophila was chosen because it was cheap to grow in a lab and had a short generation time. Yet through drosophila we've learned so much about genetics, development, and evolution in ways that are just unparalleled. Yet Sarah Palin[1] and others attack it as a silly waste of money. If we'd have limited drosophila research decades ago because we didn't think an organism with a ~700 million year split with humans would be useful for us, where would be? Much stupider, and much worse off. Basic science matters, big time.
Absolutely agree about the importance of post-harvest problem-solving, but I disagree about the benefit/cost ratio. There are a few key things in photosynthesis which, if achieved (which won't cost that much), could have massive benefits. In post-harvest research there are many small, localised problems that change over time. It's a less tractable, but extremely important, set of problems.
I agree that there are intellectually satisfying problems to solve. However, without getting into the tedious debate on the values of basic science vs translational science, how much of that intellectual satisfaction is mental masturbation?
Are these problems really that important? How much of the cool intellectual questions will directly give you a meaningful biological interpretation? Perhaps this is more of a comment on our field. I found a lot of the intellectual satisfying questions during my phd to involve algorithms/data structures, which mostly are just the tools to get at the biological interpretation.