120-year-old gene regulation problem independently solved by a computer. How?
The paper by Lobo and Levin is an attempt to learn a model that represents the inner workings of a biological system by fitting parameters to data. This is a common topic in "systems biology", a model-based approach to studying biology that is popular in some fields. 

Even for small systems, this is a phenomenally hard problem. Unlike most machine learning problems, achieving good accuracy (on independent test sets) is not enough; the model structure itself must be correct, meaning that it should reflect the actual mechanisms operating in the cell or organism. It's like trying to identify the actual structure of an integrated circuit by only observing inputs and outputs --- not only identifying the logical rule that the system exhibits, but the actual wiring diagram that implements that rule. It is of course quite possible that alternative models exist that perfectly captures the behavior, and yet is completely different "on the inside". In other words, the system may not be identifiable from the data --- two different models may produce exactly the same behavior. So there's no way to know beforehand if these problems are even theoretically solvable (let alone practically feasible).

Lobo and Lewin claim to have accomplished this task for a small system of gene products. As far as I can tell, the central piece of evidence is that the model they come up with does capture some mechanisms previously known to exist. Whether this is just plain luck, overfitting, or a special case where (parts of) the system is actually identifiable from data is hard to tell. But it's notable that Lobo and Levin fall back on demonstrating predictive accuracy in the end, which is not enough to support their thesis. The critical test is to take a new prediction from their model and actually test it experimentally, to prove their (strong!) claim that their method can actually infer new mechanisms. Doing so is expensive and time-consuming, and much more difficult to get right than tweaking models to fit mechanisms you already knew beforehand --- which is exactly why I want to see that evidence. If the authors really believe that their method is a breaktrough, those experiments should have been done.

Finally, these type of algorithms are applied to models containing only a handful of components, and typically to systems that have been studied for decades and are very well-characterized to begin with. In most of biology, the problems are orders of magnitude more complex, and often we don't even know what the components are. So I thik there are a limited number of cases where this approach is feasible.