Namaste Ujjwal!
I love that another computer scientist is getting into the synthetic biology area! I am also a computer scientist and have the same fascination with synthetic biology. I have been studying synthetic biology for a number of years now and here is a summary as to what I have found important as a computer scientist:
1. Synthetic biology is forward engineering, systems biology is backward engineering. The difference is that systems biology reverse engineers how nature has selected organisms/chemical pathways/etc... to accomplish goals. Synthetic biology takes what has been discovered by systems biology and uses that knowledge to design/modify organisms to accomplish goals nature has not selected for yet.
2. Synthetic biology is a very young engineering field. Keep in mind that I say engineering not science. Synthetic biology is about doing not just discovering and hypothesizing as a science is usually confined to. Engineering involves:
* standard approaches to solving problems (The leader in this field is Drew Endy and the best examples of this are biobricks
http://biobricks.org/)
* modularizing solutions into components and that can easily be reused and combined to create new solutions (SBML, biobricks)
3. The biology field and most of the people in the field are not computer scientists and are very shocked to discover that cells are actually hardware (cells) executing programs (dna). They are not skilled in the arts of computer science and quickly overwhelmed by these advanced concepts/approaches. Keep this in mind when working with them as you are massively disrupting the entire field. Imagine you were used to small innovations every year and very comfortable careers with no innovational demands. All of a sudden all hell breaks loose and you are kind of left behind. As computer scientists we are aware of this environment and quite comfortable with it (except when we aren't :> ) but biologists are not used to this at all. If you can extend a peaceful coexistence with them it works soooo much better. Unfortunately most computer scientists have no social skills :( It is my opinion that synthetic biology is a sub domain of computer science. This infuriates biologists.
4. Realize that as a young engineering field you can't just pick up a book and learn synthetic biology. NO SUCH BOOKS HAVE BEEN WRITTEN YET!!! What is even worse is the following:
* The US Government finances 90% of drugs and their expenses and picks certain companies to develop the drugs. The companies in turn have colleges/academics etc... do research and development of these drugs. Colleges have these things called PHD doctors that want to get tenure. The only way they can get tenure is to present papers on their research and be recognized in the field. As a result they are not inclined to share their knowledge and special knowledge as most computer scientists are used to talking with each other. You will quickly alienate yourself by asking them to share such knowledge so tread very lightly. The entire model of biological innovation is breaking down and its no fun for them as they can't get tenure for presenting papers on knowledge that is already in the public domain. Whats even worse is all the knowledge they are creating gets patented and all doors get shut on your innovations. When you realize this you will quickly understand why you can't just find this information easily. You have to get access to websites/libraries of special peer reviewed papers that cost lots of money to get accounts on. Only tier 1 universities have access to such repositories of knowledge. If you make friends with people who have access they can assist you in getting such research. Because no standard books have been created on synthetic biology, the path to innovation is to find such papers, understand them and utilize their knowledge to improve synthetic biology. Its a hellish nightmare of duplication of effort and lots of noise trying to determine what is important what is not in the ocean of peer reviewed papers and how they relate to synthetic biology. Its getting better but just understand this.
5. Synthetic biologists are the elite of biology. Most non-synthetic biologists hate them and are quite jealous of them. Its kind of how regular computer programmers don't like lisp programmers if they don't know how to do it :> You can quickly turn biology allies into enemies by just mentioning synthetic biology. I hate to say it but there are alot of politics in the biology community, it sucks. If you play your cards right you get info that takes most people years to get and understand in minutes by people who know. As a computer scientist this idea is very foreign as most computer scientists yearn to innovate and cooperate together as the complexity is so non-trivial there is no other real way to move forward with efficiency. Biology is under the delusion that this is not true yet. They are slowly learning the opposite however.
6. The fundamental way that innovation/research is done in synthetic biology is understanding proteins. The accepted way to understand/analyze proteins is to do xray crystallography. Imagine the most inefficient unstable time consuming archaic way of analyzing something. For example, counting salt particle by particle instead of weighing the salt shaker to find how many particles of salt are in the salt shaker. This barely approaches how difficult and inefficient xray crystallography is. It is also quite expensive. You can send in a sample to find out its dna but this is not as useful as everyone wants to tell you. You can't know its actual shape without xray crystallography. SHAPE MEANS A GREAT DEAL!!!!!!!!! The human genome is composed of genes that create proteins when expressed with promoters. We can know what protein will get created but we can't readily know it shape. This is why people who know will tell you the genome is about 10 trillion, trillion, trillion times easier to understand than the protein genome or pronome. The same protein can't exist in a trillion different shapes and all those shapes matter! We are in the age of the pronome now. If you realize this and why this is important you will be ahead of 90% of the biological innovation community.
7. Start from something that works and diverge slowly from there. For example you may want to create a protein of a certain coding. You know of a protein that is similar to the protein you want. Find the gene that creates the protein and modify it slowly to create your desired protein. This is how insulin is now created using e-coli.
8. Understand that synthetic biology is trying to understand the mind of God. This is also heresy in the biological community as most are darwinists and refuse to acknowledge a creator. When you realize this it helps you really think outside the box. For example, understand that nature is using quantum mechanics to select for fitness. God wrote DNA and setup all the systems that cause life to work. It is not wasteful or inneficient. I absolutely did not believe in junk DNA as was later proved right. God doesn't make junk and the entire universe is engineered by God to support life. Understanding this allows you to make discoveries that normal darwinists cannot make as easily in my opinion.
9. Use python not perl when possible. If you use python other biologists can use it as it is 10 times easier than perl. Python has a wonderful library called biopython (
http://biopython.org/wiki/Main_Page). It is not as good as perl but could be close enough for what you want to do.
10. An hour in the library is worth 100 in the labratory. If you can simulate or find papers on what your trying to do it is going to save you much time and money on accomplishing your goal in synthetic biology.
11. I am probably one of the only people in the world that can give you an accurate comparison of silicon based computing compared to cell based computing:
Silicon Based Cell Based
Instructions per second Base pairs per second
Storage 0/1 DNA (CGAT) mostly/ 26 amino acids (used to make
proteins)
Battery/power/electricity Mitochondria/glycogen
Internet IP packets Motor proteins, messenger proteins, dispersion physics
at the nano-scale of the cell (inside cell) and/or proteins
themselves (outside the cell)
through exocitosis/endocitosis/protein binding from the
cell membrane
CPU cell nucleus
Hardrive histones in the cell nucleus
RAM Genes/cell nucleus proteins in the cytoplasm
Program Counter Combination of promoter proteins, dna promoter areas
end codon, ribosome transcription position
Now for the finally! Here is my helloworld example:
Hardware: e-coli cell
program: gene that transcribes the helloworld protein
Steps to do the helloworld synthetic biology program:
1. Of the 26 amino acids choose one for each of the letters in helloworld (h,e,l,o,w,r,d)
2. Form the protein (with a flourscent green marker) and then use biopython to reverse engineer the gene that would transcribe the helloworld protein
3. Put the gene in the e-coli ring dna
4. Create a promoter protein to turn the gene on
5. Create a silencer protein to turn the gene off
Send the promoter protein into the cell and watch the cell create the helloworld protein that will show up under uv light as fluorescent green. Stop the program by sending in the silencer protein.
You have just created a helloworld program using a cell based machine!
-Tim
P.S.
If you want to know what I think is the holy grail of synthetic biology it is the following:
1. The ability to easily determine the structure/shape of a protein
2. A model organism to test programs on (currently it is e-coli for the most part but is not easy to work with)
3. A cell simulator that can be used to simulate how dna/proteins would be executed in a real cell. (This does not exist at this time)
On Fri, Feb 8, 2013 at 6:28 AM, Ujjwal Thaakar
<ujjwal...@gmail.com> wrote:
Do you think I should start with biocomputing and then eventually try to understand pure biology as I get more and more comfortable?