bioquant

This has been my experience as well. My department has an almost weekly happy hour frequented by grad students and faculty. Some people talk about random things (sports, news, etc.), others talk about teaching, others about research. I've found that in some of these small conversations I've gotten great ideas or insights into my research that I hadn't gotten otherwise. Sometimes just being asked to give the 30 second version of your research can force you into thinking about it in a different way or allow someone else to say something you hadn't thought of. Without those conversations, my work would definitely suffer.
 
And yea, I'm one of those people who can't work all the time. Back when I did my comprehensive exams (which were multiple questions over like 10 days), I remember people in my department (mostly those not yet at the exams stage) being surprised that I was still attending the class I was TAing (I was mostly grading but went to every single lecture), working out, and even watching an episode or two of a TV show online. But you know what? You can't work for 16 hours a day for the 10 days without a break. And really, since I was limited to like 25 pages double-spaced per answer, I would've ended up writing way more than I needed if I'd worked that long. Instead, I rode my bike to the gym, worked out with friends (including some who had PhDs and thus totally understood what comps were and why you might need a break), cooked myself real food, etc. It's about knowing what you need to work efficiently and be productive and taking the time to do whatever that is.
 
Back to the original question though:
- Be open and willing to learn.
- If you're in the humanities or social sciences, take the time to just browse the library shelves in your general field and in your intended research area to get an idea of what's been published and what research resources are available to you. (Even better, meet with a librarian early on to make sure you know what your school has and the support s/he can give you.)
- Skim through recent journal issues in your field to get a sense of what topics are current and which are becoming dated. Pay attention to book reviews if there are any and use those to help you find relevant books for your discipline and research area.
- Learn to use reference management software (EndNote, Zotero, Mendeley, etc.) and start keeping track of your references that way.
- Figure out an easy to use system for staying abreast of current/new research in both books and journals that may be of interest.
- Read your graduate handbook (and TA handbook if needed) so you know what is expected of you. Ask questions if expectations are unclear.
- Start figuring out what, if any, courses outside the department you might want to take, how often they're offered, how difficult they are, etc.
- If you're going to need research methods training, figure out how to get that ASAP. In the social sciences, this often means taking courses in qualitative methods, statistics, and/or GIS and seats in those classes can fill because they're attracting students from an array of disciplines. Getting your methods coursework done means you can start collecting data sooner.
- Get to know whomever helps oversee grant apps (NIH, NSF, SSRC, Fulbright, IAF, etc.) at your institution and ask them what you can do beginning now to prepare to apply in the future, when you should be applying, what you'll need to be competitive, etc. And, while you're there, get them to help you set up some alerts for grant announcements.
 
There's probably more you could do, especially related to conferences and networking, but I don't want to overload anyone with suggestions.

Copy and paste a school rejection letter and replace your name with the school's.

Haha. No. They accept less than 40 people each cohort. You have no idea what you are talking about. Go back and crawl in your hole, no one needs your opinion.

I would think about offering an alternative to the bar crawl for those who don't drink or whose religious beliefs may not allow them to be in that setting. Basically, I'd try to be as inclusive as possible in the planning in terms of thinking about how someone's gender, sexual identity, religion, food allergies, etc. might affect their ability to participate fully in the interview weekend. 
I'd also make sure that the applicants have some downtime so they can relax and decompress. Especially for introverts it can be hard to be "on" all day long. 

If you're that worried, set up a vacation response on your phone saying that you're out of the country, when you'll be back, and leaving the best way to reach you (so provide your email address if that's best). It'll be fine. It's also unlikely you'll hear anything so soon after the deadline.

Well, I was visiting a school in New York so it was toward the end of my trip, and I really felt like I needed a day off. This school that I chose not to visit was in New Jersey. It was good, but not the best fit for me. By that point I had already decided what my top two choices were, and they were not that school. I wrote the faculty member who had been in touch with me to let her know I was going to cancel my visit. Turned out she lived in NYC, and she invited me to visit her at her apartment. I went, we had some tea and a nice chat, and she took me out to dinner. I, being me, wasn't thinking much and ended up talked very enthusiastically about one of the other schools. She said my enthusiasm made it clear that I should go there, and had nice things to say about it. She was very gracious, which I appreciated a lot. I've interacted with other faculty from there since, and I've never felt awkward. I don't think spending their time and money when I already knew I wasn't going there would have been appreciated. 

For people interested in synthetic biology, I updated the list that I mentioned above. 
 
Because http://syntheticbiology.org/Labs.html  list was somewhat outdated, I made a new list. I probably did not include every synthetic biologists. Leave me a message if you think I missed anyone. I will add them in. The number inside the parenthesis is the Google Scholar h-index for your information.   Boston University Densmore Lab (18) Khalil Lab (13)  Wong Lab (9) Caltech Arnold Lab (101) Cornell  John March (n/a) Georgia Tech Gaucher Lab (24) Harvard Silver Lab (90)  Church Lab (118)  MIT Lu Lab (n/a) Prather Lab (n/a) Voigt Lab (n/a)  Weiss Lab (n/a)  Collins Lab (97) Northwestern University Jewett Lab (22) Tyo Lab (18) Penn State Salis Lab (14) Princeton Jose L. Avalos (n/a) Rice University Tabor Lab (11) Stanford University Endy Lab (n/a) Smolke Lab (n/a) UC Berkeley Anderson Lab (n/a) Dueber Lab (15) Keasling Lab (78) Adam Arkin (69) UC Irvine Liu Lab  (n/a) UC Los Angeles  James Liao (70) Yvonne Chen (n/a) UC San Francisco Lim Lab (n/a)  UI Urbana Champaign Huimin Zhao (41) Virginia Tech Peccoud's Lab (21) Washington University is St. Louis  Tae Seok Moon (n/a) Fuzhonng Zhang (n/a) Yinjie Tang (n/a) Yale University Isaacs Lab (n/a) 

Good to see other people interested in SynBio. 
Here is a somewhat outdated list of synthetic biologists in the US: http://syntheticbiology.org/Labs.html I think only Rice has a graduate program that specifically mentions synthetic biology in the program name For other schools, professors working on synthetic biology are in bioengineering, biomedical science, chemical engineering departments. Usually, synbio professors represent multiple departments.  As FLAGtagSwag has mentioned, if you are interested definitely think about applying to UC Berkeley and UC San Francisco. I think Boston University has good reputation in synthetic biology (but James Collins left BU to MIT, so this might change)     
 

Hoping to bring this topic back as the application season approaches! 
I'm applying to both computer science and computational biology PhD programs. My interests are in genomics, algorithms, and bioinformatics software development. I'm generally interested in the following programs: Johns Hopkins CS, Stanford CS, University of Washington Genome Sciences (maybe CS), WashU/WUSTL CB, USC CB, Princeton QCB (maybe CS), UC Berkeley CB.
For perspective: I'm getting a 4-year BS + MS in Computational Biology at a private university well-known in CS and CB. Ugrad GPA 3.7, solid GRE, TA experience, 2 years comp bio research at my home institution plus conference poster, and a highly regarded biology summer REU.
What programs are you all looking at?   

I heard many ppl say GRE are not important, or that it's only used to weed out those who score very low. But I've always thought that a high GRE can help. According to Gatech, the average quant score is 167, and that scoring above that would help the application. This means someone who scores a 170 would get noticed. In fact, according to data, 2% of all test takers get 170, and only 6% of engineering majors get 170.
Furthermore, I was told by UIUC ECE that they weed out ppl that score lower than 165. That means if you scored 163, don't even apply before retaking the test. UIUC also said that a high verbal score would help one get TA positions, so unlike popular belief, verbal score is not completely insignificant for admission into engineering. 
I have also heard that a high verbal score, while not as important as quant for engineering and science, would still help simply because so few ppl score well on the verbal, and a high score would stand out.
Obviously every program is a bit different, but I found this from Gatech's site: 
Graduate Record Examination (GRE)
The GRE is required of all applicants. All applicants must submit scores for the following three parts: Verbal, Quantitative, and Analytical Writing. Your GRE test scores are an important part of your application and you should strive to submit the best possible scores. The average GRE scores for applicants accepted into our graduate program for fall 2014 under the previous grading system are:
New FormatOld FormatVerbal: 158Verbal: 580Quantitative: 167Quantitative: 800Analytical Writing: 3.8Analytical Writing: 3.8The admission committee considers several factors when making admission decisions, your academic performance at prior institutions (GPAs), your GRE test scores, your statement of purpose, and your letters of recommendation. All of these factors are important, and your application will be strengthened if your GRE scores are at or above these average values. If your GRE scores are lower than these averages, your chances for admission will be reduced. The minimum scores that are needed to be considered for the program are 146/V, 155/Q, and 3.5/AW under the new format and 400/V,700/Q, and 3.5/AW under the old format. Furthermore, meeting or exceeding these average numbers for GRE scores does not guarantee admission.
 
 
 
 
 

Like TakeruK, I never strategized while answering that question and just answered honestly. When I applied to MA programs, I honestly didn't think anything of the question. When I applied to PhD programs, I knew that everyone would know where else I was applying based solely on my interests, so I had no qualms about stating where I was applying on my applications. I wanted to work in a very specific subfield of my discipline and there were maybe 10-12 programs in the country that could support my interests so it didn't take much of a genius on the adcom to guess where else I'd apply so, I decided to be forthright about it.
 
Sidebar: If anyone ever gets around to writing a FAQ on applications that we can tag, this question totally belongs there. My bet is it'll come up at least 20 more times this application cycle.

I didn't put any strategy or "mind games" into answering this question. I don't think this information will help you or hurt you. So what if an applicant is applying to one high ranked school and a lot of lower ranked ones--that high ranked school isn't going to turn away a great applicant because they have "tainted" themselves by applying to lower ranked schools! That's silly!!
I answered honestly. I applied to 8 schools in total and most schools only ask for 4 other names. I chose the 4 names somewhat arbitrarily, but usually in programs that are most related. Generally, I already had 3 out of those 8 schools in mind as my "top choices" so I usually included those three. But really, it was whatever I felt like writing when I got to that question--I can't remember which schools I wrote for which application.
The reason they want this information has nothing to do with your admission decision. It helps them to know who the competition are (as philstudent1991 said). It also helps them determine how many offers to make. For a school that wants to fill 5 spots, for example, and if they normally make 8 offers in the first round, and they find out that all 8 of their top applicants list their main rival school as another choice, they make might another offer or two because they might anticipate losing more candidates that normal to their main rival school this year. And I think it would also help them recruit their top applicants better, if they know who their competition are.

My understanding is that it is used to evaluate who the competition is, and can be useful for departments when they approach administrators for funding. For example, in philosophy Rutgers and Pitt are top ten departments along with places like Harvard, Yale and Stanford. Rutgers and Pitt might use that data to show administrators they are competing with the Ivies for students and thus need better stipends for their grad students and such. I don't think the information is ever used in evaluating an applicant, though of course I can see why one might want to apply strategy to the section. If the school you are applying to is a wild reach among more modest apps, it doesn't seem like you do yourself any favors by reporting that in the app, whereas if the school is right in the range of the rest that might be a good sign to the ad comm that your advisors have given you a list of places where you'd be competitive and their school is one such place. 

I can't comment on your chances with any of the other schools, but I think you have an excellent chance at UIUC based on the stats you've posted! We have had several students switch from the experimental side of things to the computational side of things or vice versa when transitioning from undergrad, so I do not think this should hurt you. In fact, one of the guys in my cohort came in with no computational/programming experience whatsoever. He is now happily settled in a purely computational lab, so I think having a minor in Computer Science will make you very competitive, even though you don't have any computational research experience. The important thing is that you have lots of other research experience. Just be sure to explain how your research interests changed and why in your SOP. (That goes for any program you apply to.) Also, unlike other universities, being an international student will not hurt your chances with our program, as the number of international students accepted is often greater than the number of domestic students accepted in any given year. For example, in my cohort, there were (originally) five domestic students and eight international students. 
 
As to your question, I think all of the programs I applied to (see my signature) are worth taking a look at, depending on what kind of computational biology research you're looking for. I do agree with @aberrant that you should definitely be looking now for PIs whose research is of interest to you. When I applied, I found out (almost too late) that my interpretation of the overall focus of a program (as stated on the program website) often did not comport 100% with the research pursued by the faculty associated with the program. Unlike @aberrant, I do not necessarily think it will be a useful exercise to ask PIs at this point if they think they will be accepting students more than a year from now. In my experience, most PIs will not have a clue. However, establishing a good relationship with PIs whose work interests you early on can only benefit you throughout the application process, so I would advise you not hesitate to email these PIs if you feel like doing so.
 
Finally, I feel that I have to note that I disagree with @aberrant's analysis of what is important in computational biology. Computational biology, at least by what I believe to be the most standard definition, requires a lot of knowledge of statistics and computer science, not chemistry or physical chemistry. Chemical or physical chemical knowledge is much more important when doing research that involves simulations of biomolecular systems, which typically falls into the field of computational biophysics or computational (quantum) chemistry, depending on what you're doing.