From last year I got interested in the relationship between cell reprogramming and gene regulatory networks (GRN). It is a topic in the area of systems biology. I wanted to become a researcher in this area, no matter in which way, so I applied for a scholarship provided by **** foundation at **** University. I was lucky to be shortlisted and got an opportunity to attend the interview there.

Although I was not successful in the end, it was a nice experience for me. The talks with some nice and excellent people there were delightening and helpful, and they made me think further about some questions.

1. I had thought microRNAs are special nodes in GRN, which may be the reason why they provide higher efficiency in induction of cell reprogramming. I wanted to investigate this question by looking at how miRNAs are connected with other genes within the GRN. But how this may help? -- I was asked so.

I hadn't considered this question that far at the moment. miRNAs may be special nodes in different ways.

1) In terms of the nodes themselves, they need only to be transcribed but not translated, they occur and effect in cytoplasm, their regulation to the targets are fine-tuned in a way to be further described (a new report on this is just published, Mukherji et al., 2011). These characteristics may be sufficient to lead to a better performance of particular miRNAs when used for induction of cell reprogramming.

2) But it may be helpful to see how miRNAs are encompassed in context of the whole network. They form a unique layer of post-transcriptional/translational regulation, also it is not precise to say so. I want to ask WHO transcribed them and WHEN. And WHO they are targeting and WHEN. However, only after we see how they are connected to others in the entire network can we see a meaningfull pattern. It may turn out they are more tightly connected based on function groups, it may otherwise turn out they react to the status of a fraction of the whole network instead of reacting to particular proteins... Too early to say anything, but something is definitely awaiting for us ahead there.

2. I appreciated the attractor theory and tried to regard it as important as Hardy-Weinberg law. But I was asked then what is comparable to the neutal theory, which can be put onto that.

I found it hard to answer. I am not that talented. But in a mediocre way I can only say: we just put the properties of the nodes and edges into the model. IF you ask me what Newton's 2nd law is, OK, maybe it is about how a system travel from an attractor to another, and how an attractor becomes non-attractive anymore.

3. We talked about the complexity of the topic. The attractor theory seems a simpliest model. Anyway if we put in time interval, non-boolean even non-continuous state, messed interactions, spatialized networks, it will become too complicated and there seems no hope of finding anything clear.

However, I still think it is a promising topic. No matter how complex the system is, once we know something simple, we begin to know how to segregate the system into simpler sub-systems. I don't think we have to solve out the 'all-in-one' theory at the moment, nor we have to predict as much as possible in terms of every detail. We just try to make small progresses, one step more into a more realistic systems at one time. And they are not meaningless, because those people doing synthetic biology and making artificial systems don't need to know everything of a natural system before their work -- anything as a solid progress, no matter how small, will be helpful to them.

4. How to propose a testable hypothesis? How to design a meaningful experiment to test it? Now we begin to think about networks and 'systems', but it is yet hard to make logical interpretation when we are talking how to propose a project.

I don't have a well-established answer yet. Anyway I think the 'deletion-insertion' fashion, based on the linear, molecular ways of thinking, is definitely insufficient here.

In the old fashion we always consider how one molecule react to another molecule. We want to find the molecules and pathways and expect everything will be clear when the whole pathways are drawn out.

However, in a perspective of systems, we may consider the status of the network or a cub-network instead of the status of a molecule. It may be connectivity, flux, stability or whatever of a set of pathways. Later we may find that the behaviour of a sub-network is responding to the status of another sub-network -- without a systems viewpoint it may not be seen.

Anyway we need evidence. But what is an effective evidence? I can't make an inclusive statement here, but in future I definitely have to dispute with people a million times on this issue. Certainly that evidence, examples and visible data are all needed to verify a new discovery/theory. But sometimes we really need to revise our knowledge of the relationship between evidence and a proposition.

5. I made some stupid answers at one moment. It's hard to hide my shortages. Although I had revised my knowledge of neutral drift theory, I still got a messed mind when asked with a basic question. And another is about binary search method in programming. Again (because it wasn't the first time) I mistook it with another question, where golden-section-search is a better algorithm.

OK at least I learned again the importance of optimizing algorithms when programming.

6. I was not asked about the relationship between my previous area of study and systems biology. Perhaps they thought those are irrelevant.

My previous interest before last year was mainly on ESS theory and its use under cellular level. I have to say, ESS and network theories sometime combine with each other on particular topics. These topics are currently irrelevant to GRN and cell reprogramming. But first, I have been trained to think theoretically and to look for steady states within a dynamic system, which is OK to be adapted to other theoretical topics. Second, researchers in networks theories are currently incorporating more evolutionary questions into the organization of networks, where ESS will finally find its use, and the advances from relevant studies will finally help with GRN studies.

It is still too early to say anything for my future. Anyway, life is an adventure. Although I don't like adventure, I will try to enjoy everyhing I experience.