Perhaps for the time being. But I think that Systems Biology is going to surpass the proteomic era soon. It already has started to gain momentum. I use the term surpass in the context that proteomic data will be used in the field of Systems Biology, not neccessarily that it is a thing of the past.
On the contrary, the complexity now increases. There are many genes that act in completely differen't roles depending on the cell type (nerve, epidermal, etc.). So a common language changes from cell type to cell type-- if one would even call it a common language. There is a large part of Bioinformatics/Computational Biology that deals with trying to determine interaction networks between genes. It's very complex, and difficult to deal with.
With less genes we then expect to have a larger amount of downstream interactions between other genes. It might seem that with less genes then we have less to worry about, but we have already speculated for a long time that gene regulatory networks are complex.
To use an analogy (for all you computer geeks), it's like a programmer trying to read poorly modularized code. When you have no idea what class is doing what, and how they interact with other classes (as every class has multiple roles and talks to multiple other classes) then it is difficult to understand why the program behaves the way it does. If the program had many classes that were well modularized and designed with very distinct roles, then it would be easier to understand why things work the way they do.
With less genes and increased complexity we have an even more difficult task. It also highlights some of the reasons on why microarray analysis has not done what we expected it to do. Increasing the complexity and dependency between genes means that we probably are going to take a longer time understanding and extrapolating information from all these networks (which means more job security for me:) ).
If you can try typing and your typing is apearing exactly after you type it then it is probably your mouse. I haven't had a chance to check on the link so I'm not sure what it looks like.
I had a similar problem with my computer after I bought a wireless mouse. It turns out that I had two computer next to each other that where both using logitech wireless mice. When I moved one mouse it lagged a lot, slowly following what I wanted it to do after a second or so. If I moved that mouse too close to the other computer it started controlling that mouse as well (same frequency of the mice I would guess). But, even if they didn't interfere with the pointer displays on each other, it seemed that the near frequency of the mice caused interference and would cause visible lag.
It didn't happen until I actually hit the connect button on the receiver. So it might not have shown up until you installed new hardware and it recycled the configurations. Try using a standard wired mouse and see if it still happens. Or, at the very least, turn off the other computer and click the connect button on the receiver and mouse to try and realign them. This theory is of course assuming that you have more than one computer in the same vacinity.
I think what we're really lacking is extremely talented scientists. It is the same issue within the computer field.
There's a difference between the huge influx of Computer Scientists and someone with a Ph.D. Most of these students have proven themselves through classes/qualifiying exams/thesis that they are a good scientist. In addition to the paper that says they have a Ph.D they have papers backing up what they did. As for the worth on your salary, there is the old phrase "publish or perish". If you're not doing the work and putting out papers you won't last long.
I don't see how you can compare this to the computer field. There is a huge difference between the education level requirements here.
He has some very interesting points in that article.
I think it is agreed that most people that go on for Ph.D's don't do it for the money.
In academia there is a lot of money that get's blown on stuff that could be used to increase grad students pay. In addition to low pay, post-docs also tend to get walked on-- especially if you are in an industry post-doc. It's just another opportunity for PI's to get low income work out of qualified people.
After everything is said and done with, most people come out of graduate school disallusioned and burnt out. They'll be lucky if their stipends actually average out to minimium wage. After that, what do they have to look forward to? Another 3 years in a post doc getting paid crap. And those are the lucky ones that actually make it through. 1/3 of all ph.d students drop out before the end.
A couple of years ago Yale University grad students protested and tried to get unionized to increase wages. I'm not sure of the outcome now but they were very roughly shunned at first because everyone argued that they were students and not workers. In reality, these students are put through hours/conditions that would make the labour board turn it's head.
The whole point of a Ph.D program is to train people into independently thinking scientists. Many people would argue that the whole point of post-docs is to further train people to become independent scientists. In reality, ph.d students are much more of an asset to the school than a liability that the "training" claims to be and they should be getting
recognition.
Behind every good graduate school is a good set of graduate students
The good news is that stipends are beginning to be pushed up. This is partly due to the large discrepency in NSF fellowships as compared to university stipends (which is around a $5,000-10,000 difference per year). I'm glad to see that at least someone is paying attention the well being of the grad students.
I think the point he was trying to make is that it seems like what they have done is so miraculous that it should save the world. Using the HIV virus as a recombinant vector isn't anything new-- it has been done for a while. And where there have been successes in cells there have been failures in bodies. If there was a clinical test to back up the research then it would be great.
Also, I've seen many different computer models that simulate viral infection in cells. I don't see how this is groundbreaking for them to do it. It just seems that the article focus's on the researchers coming up with great strides in computational biology where work has already been done.
Again, the article focuses on research that is, as of now, incomplete. There are many other researchers with more comprehensive research in other areas that are complete that are being overshadowed. And I bet they aren't all Berkeley professors.
I would like to reiterate hak1du's post. There are a lot of very bright people with very interesting thesis topics that didn't come from a well named school, yet are highly successful. Too many times people judge the reputation of the school by its name and not that of the program or the research. Probably one of the best programs for Bioinformatics is at Boston University-- not Yale, Columbia, or MIT (although all three have recently started Bioinformatics programs).
Take an in depth look at the schools you want to apply to. What kind of research do they do? Does it interest you? It doesn't hurt nowadays to fire off a couple of emails to some of the current grad students or researchers at the school. Inquire about their research, what they do, also try downloading and reading some of their papers.
There are many branches of Bioinformatics (Systems biology, proteomics, genomics). Also look at schools that have Computational Biology as well. The terms tend to get mixed up a lot, but a lot of schools use them interchangingly. You might be more interested in Computational Biology than bioinformatics if you have a more mathematical kind of mind. The field is advancing so fast that a lot of the names haven't caught up.
Lastly, make sure about the advisor. Really make sure their personality isn't horrible. You can find an advisor with really interesting research topics and a good reputation but sucks as an advisor because they have horrible research skills. The current students would be able to judge him best for you. It's very important to have an advisor with a personality that doesn't clash with yours. Too many times people come out of Ph.D programs frustrated or burnt out because they chose the wrong advisor (and some never go on to complete).
Slashdot Mirror
Perhaps for the time being. But I think that Systems Biology is going to surpass the proteomic era soon. It already has started to gain momentum. I use the term surpass in the context that proteomic data will be used in the field of Systems Biology, not neccessarily that it is a thing of the past.
On the contrary, the complexity now increases. There are many genes that act in completely differen't roles depending on the cell type (nerve, epidermal, etc.). So a common language changes from cell type to cell type-- if one would even call it a common language. There is a large part of Bioinformatics/Computational Biology that deals with trying to determine interaction networks between genes. It's very complex, and difficult to deal with.
:) ).
With less genes we then expect to have a larger amount of downstream interactions between other genes. It might seem that with less genes then we have less to worry about, but we have already speculated for a long time that gene regulatory networks are complex.
To use an analogy (for all you computer geeks), it's like a programmer trying to read poorly modularized code. When you have no idea what class is doing what, and how they interact with other classes (as every class has multiple roles and talks to multiple other classes) then it is difficult to understand why the program behaves the way it does. If the program had many classes that were well modularized and designed with very distinct roles, then it would be easier to understand why things work the way they do.
With less genes and increased complexity we have an even more difficult task. It also highlights some of the reasons on why microarray analysis has not done what we expected it to do. Increasing the complexity and dependency between genes means that we probably are going to take a longer time understanding and extrapolating information from all these networks (which means more job security for me
If you can try typing and your typing is apearing exactly after you type it then it is probably your mouse. I haven't had a chance to check on the link so I'm not sure what it looks like.
I had a similar problem with my computer after I bought a wireless mouse. It turns out that I had two computer next to each other that where both using logitech wireless mice. When I moved one mouse it lagged a lot, slowly following what I wanted it to do after a second or so. If I moved that mouse too close to the other computer it started controlling that mouse as well (same frequency of the mice I would guess). But, even if they didn't interfere with the pointer displays on each other, it seemed that the near frequency of the mice caused interference and would cause visible lag.
It didn't happen until I actually hit the connect button on the receiver. So it might not have shown up until you installed new hardware and it recycled the configurations. Try using a standard wired mouse and see if it still happens. Or, at the very least, turn off the other computer and click the connect button on the receiver and mouse to try and realign them. This theory is of course assuming that you have more than one computer in the same vacinity.
I think what we're really lacking is extremely talented scientists. It is the same issue within the computer field.
There's a difference between the huge influx of Computer Scientists and someone with a Ph.D. Most of these students have proven themselves through classes/qualifiying exams/thesis that they are a good scientist. In addition to the paper that says they have a Ph.D they have papers backing up what they did. As for the worth on your salary, there is the old phrase "publish or perish". If you're not doing the work and putting out papers you won't last long.
I don't see how you can compare this to the computer field. There is a huge difference between the education level requirements here.
He has some very interesting points in that article.
I think it is agreed that most people that go on for Ph.D's don't do it for the money.
In academia there is a lot of money that get's blown on stuff that could be used to increase grad students pay. In addition to low pay, post-docs also tend to get walked on-- especially if you are in an industry post-doc. It's just another opportunity for PI's to get low income work out of qualified people.
After everything is said and done with, most people come out of graduate school disallusioned and burnt out. They'll be lucky if their stipends actually average out to minimium wage. After that, what do they have to look forward to? Another 3 years in a post doc getting paid crap. And those are the lucky ones that actually make it through. 1/3 of all ph.d students drop out before the end.
A couple of years ago Yale University grad students protested and tried to get unionized to increase wages. I'm not sure of the outcome now but they were very roughly shunned at first because everyone argued that they were students and not workers. In reality, these students are put through hours/conditions that would make the labour board turn it's head.
The whole point of a Ph.D program is to train people into independently thinking scientists. Many people would argue that the whole point of post-docs is to further train people to become independent scientists. In reality, ph.d students are much more of an asset to the school than a liability that the "training" claims to be and they should be getting recognition.
Behind every good graduate school is a good set of graduate students
The good news is that stipends are beginning to be pushed up. This is partly due to the large discrepency in NSF fellowships as compared to university stipends (which is around a $5,000-10,000 difference per year). I'm glad to see that at least someone is paying attention the well being of the grad students.
I think the point he was trying to make is that it seems like what they have done is so miraculous that it should save the world. Using the HIV virus as a recombinant vector isn't anything new-- it has been done for a while. And where there have been successes in cells there have been failures in bodies. If there was a clinical test to back up the research then it would be great. Also, I've seen many different computer models that simulate viral infection in cells. I don't see how this is groundbreaking for them to do it. It just seems that the article focus's on the researchers coming up with great strides in computational biology where work has already been done. Again, the article focuses on research that is, as of now, incomplete. There are many other researchers with more comprehensive research in other areas that are complete that are being overshadowed. And I bet they aren't all Berkeley professors.
I would like to reiterate hak1du's post. There are a lot of very bright people with very interesting thesis topics that didn't come from a well named school, yet are highly successful. Too many times people judge the reputation of the school by its name and not that of the program or the research. Probably one of the best programs for Bioinformatics is at Boston University-- not Yale, Columbia, or MIT (although all three have recently started Bioinformatics programs). Take an in depth look at the schools you want to apply to. What kind of research do they do? Does it interest you? It doesn't hurt nowadays to fire off a couple of emails to some of the current grad students or researchers at the school. Inquire about their research, what they do, also try downloading and reading some of their papers. There are many branches of Bioinformatics (Systems biology, proteomics, genomics). Also look at schools that have Computational Biology as well. The terms tend to get mixed up a lot, but a lot of schools use them interchangingly. You might be more interested in Computational Biology than bioinformatics if you have a more mathematical kind of mind. The field is advancing so fast that a lot of the names haven't caught up. Lastly, make sure about the advisor. Really make sure their personality isn't horrible. You can find an advisor with really interesting research topics and a good reputation but sucks as an advisor because they have horrible research skills. The current students would be able to judge him best for you. It's very important to have an advisor with a personality that doesn't clash with yours. Too many times people come out of Ph.D programs frustrated or burnt out because they chose the wrong advisor (and some never go on to complete).