Christian, great idea to ask this question here before taking important decisions.
Are those media articles a hype?
Yes. Over the last 10 years I constantly see those hype stories in media about "revolutionary" large-scale-study/big data projects with mind-blowing numbers (gigabases, teraflops, terabytes, thousands of papers and hundreds of genes).
Journalists are competing for readers with pompous titles as well as scientists need to promote their results. I recall that ~7 years ago Russian national TV came up with nothing less than "Scientists unravel the mystery of Russian soul", when one of our scientific institutions, close to government, finally managed to actually launch (not buy and put on hold as usual) a next-generation sequencing machine and sequence a full genome. Curiously "Russian soul" belongs to half-jewish, half-armenian guy. :)
Though it's nice that in media papers journalists and scientists managed to explicitly articulate the problem we can't solve: how to predict morphology using molecular biology data. This work is not even close to doing that. It essentially predicts a binary trait, "heads or tails".
Is this a new breakthrough approach in science, connecting areas that noone could imagine to connect?
No. This approach is called Systems Biology. It's been getting popular, I'd say, since late 90s-2000s. This paper is not like "look, I've invented a DNA computer/quantum computer" etc. It's an ordinary systems biology study, its quality is a matter of discussion.
Is this a good systems biology paper?
I don't claim that it's bad. But some things sound strange to me:
I don't think that de-novo predicting the network of interactions should be done by systems biology, when some facts are known from literature and could be enriched by Omics. If you're modelling a molbiol/biochem network, too much freedom might render your network totally different from reality.
Robustness test is unconvincing to me. Ok, "We dropped 3 crucial papers" and still network holds. Ok, network predicted some results from papers, not taken into account upon its construction. But this is not a systematic test. Split the data into two parts, optimize the network on one part and test it on another one.
Small number of genes involved in the described process and binary trait in consideration make the authors do overambitious claims, IMHO.
Can a smart and diligent person as yourself make a better career, doing systems biology than something else? Will your higher IQ and/or mathematical background give you an edge?
I think, no. I think, this area downplays the difference in intellect between people, while you might prefer a more discriminating area, even if you allow the assumption that you're not the single brightest person to ever live on Earth. :)
You should have worked out (or have from birth) some mental habits (e.g. habit to consider simple examples with small numbers of elements; use modular thinking; not to get stuck with a single approach - switch tricks; etc. etc.) that less smart people lack. Systems biology often works with too complex objects to imagine, thus your edge might be lost.
In reality cascades of biological pathways include huge numbers of players involved. Moreover often a single protein has several seemingly unrelated functions. I was a bit shocked at first, when aconitase, a Crebbs-cycle protein turned out to be at the same time an iron-responsive element and regulator of mRNA turnover. What a mad engineer could've created this?! Later I heard that many Crebbs-cycle proteins have seemingly unrelated "part-time jobs".
Stability of solution:
Look at any pathway in KEGG. Or this one: a famous cascade of cell signal transfer: . Although, it's not that complex, when you know all those proteins by heart, it might be hard to work with it quantitatively. Solutions might be unstable. Lack of experimental data may lead your simulation in a wrong direction. For instance, famous p53 protein (in this cascade it has 3 arrows or so) in reality has more than a hundred counter-agents, loss of interaction with one of them might lead your simulation to a state, critically different from reality.
Another problem for systems biology papers is lack of comprehensible results. People are usually interested in a paper, when they can build something on top of it or at least when it's an interesting special case to know about. To be honest, I can't make myself read them.
What's the scientific takeaway of this paper for instance? It doesn't exist. I can't use it as a scientist.
Systems biology guys often speak of holism approach as opposed to reductionism. To me it's an empty word - the only comprehensible (and thus publishable) way to approach complex entities for human being is modular thinking - you just replace a complex cascade with a black box with a set of inputs/outputs and rates and treat it as a single object, reducing the complexity that way.
I feel that systems biology approach is better suited for commercial use in industry than for small-lab science. You need tools to mine data, to visualize results etc. Companies can afford building them for themselves.
As for the results, in industry you don't need them to be publishable. In agricultural industry you can optimize producing organism's metabolism with systems biology means to make it output more product of interest e.g. in cows or chemicals-producing organisms (though same goal can often be attained with pure genetic engineering easier). May be pharmacology can be interested in this to optimize their drugs to find best way to modify particular coefficients in particular reactions.
Practical achievements based on fundamental biological science still make use of reductionist works. They stem from little facts that cervical carcinoma is caused by a specific virus and vaccination against it seems (silver bullet warning) to prevent it. Or the fact that CCR5 mutants are seemingly immune to AIDS or similar results for causative agents for stomach cancer or hepatitis. Or useful discoveries of tools, such as siRNAs, Crispr/CAS9 or Tal effectors.
If you still decide to give biology a try, it won't take you too long to grasp the molecular biology. You can mostly work with it without knowledge of biology or chemistry. In a month you'll grasp the basic concepts such as replication-transcription-translation, in a year will be familiar with most key processes, in ~3 years will be a good specialist in some area.