RE: [DIYbio] Advice for a computer scientist who doesn't understand biology

[J Adams]

Do something that handles large datasets, but very little processing.  With genome sequencing and analysis, the problem is the large amounts of data that have to be sorted and compared.  So, there is very little “computing” being done, just a lot of shuffling of massive amounts of data.  But, speed is important.   

 

From: diy...@googlegroups.com [mailto:diy...@googlegroups.com] On Behalf Of Ujjwal Thaakar
Sent: Monday, February 04, 2013 9:00 AM
To: diy...@googlegroups.com
Subject: [DIYbio] Advice for a computer scientist who doesn't understand biology

 

Hi, I'm a third year computer science student from Gujarat, India. I have recently picked up synthetic biology as my seminar topic(a subject where you study about something that interests you throughout the semester). I picked this up as I smelled the future in it and because the concept sounded freakishly cool!

Now the problem is that I have experience only with high school biology and haven't touched any since the last 5 years. Where do I start and where should I aim to end by May 2013. Doing something practical is of the highest importance to me but I understand that it might not be feasible given my current knowledge and time so I would like some concrete advice.

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[Iván Esteban Araya]

Hi, im a undergraduate  biotechnology engineering student that almost finish the career. I think important that you be clear about the differences and relation between synthetic biology and bioinformatics.

Basically,  the core idea with synthetic biology is design and synthesis DNA on the lab starting from simples nucleotics, and leave behind the need of copy secuences from a source organism, and forward design completely new funcional genes (^.^)/.  Besides this, bioinformatics focus on the analysis and handle of the information related to DNA, RNA and proteins basically,  and there's where I think you can find a expecific issue with topics more related with computer science. Bioinformatics focused on syntetic biology, but specifically on what? Well maybe the develop of algorithms to predict the estability of the DNA construct during the synthesis and export to the target cell, all of this on the design stage or the analysis of the output information collected from the cell line or organism on study with the goal of elucidate the behavior in vivo (we don't know much of that in a complex level yet, for example on the regulation of genes)

Finally, this will be difficult for you if don't have the basic background of molecular biology, specially genetics .... but isn't imposible, I had to learn some algorithm stuff when intrested on this topic, my advice is look for someone with that knowledge and convinces him to help you =P

Fell free of correct me if im wrong, and sorry for the grammatical mistakes,  im still need to improve my english

[Lisa Thalheim]

Hey,

since you come from a CS background, it probably makes sense to look
for something at the interface of synbio and CS, such as modeling
biological systems (for example, systems biology), or the "composable
systems" approach to synbio. You could start by looking at the work of
Jean Peccoud (http://www.vbi.vt.edu/faculty/personal/Jean_Peccoud) and
work your way from there. Maybe a manageable problem to work on will
pop up. Or you could look into the research that has been done on
biocomputing, a lot of which has a synbio component. I wrote an
introduction to biocomputing a while back, aimed at people with a CS
background. You can find it here:
http://www2.informatik.hu-berlin.de/~thalheim/biocomputing.pdf
Hope that helps you get started.

Cheers,
Lisa

[Inigo Howlett]

Ujjwal,

Maybe you could help me with a project I'm working on right now.

I'm very interested in the human microbiome and its ecology as relates to metagenomics. What i'd like to find is a way of determining "normal" gut microbiota- a way of claiming, likely through some kind of cluster analysis, that the microbiome of one fecal sample is ecologically analogous and statistically within the expected parameters of a consensus cluster of healthy gut microbiomes.

 

This is going to be the first big medical breakthrough to come out of metagenomics, and I'd like a way to be able to claim my designs work.

 

please be in touch,

 

Inigo Howlett

 

InigoWalkerHowlett 

@

gmail.com

[William Heath]

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)

  *  creating standard tools for designing, testing, and deploying synthetic biological solutions  (http://celldesigner.org/, http://www.blueheronbio.com/)

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?

To view this discussion on the web visit https://groups.google.com/d/msg/diybio/-/2cpopNNhdbgJ.

[Nathan McCorkle]

On Fri, Feb 8, 2013 at 3:22 PM, William Heath <wgh...@gmail.com> wrote:
> 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).

This has been discussed before in-depth, and it really is a vast
simplification to say DNA is THE program.

See:
Comparing E. coli to Linux
https://groups.google.com/d/topic/diybio/TfhBPXFgl4M/discussion

> 5. Synthetic biologists are the elite of biology.

WHAT? That's a bit preposterous. Synthetic biologists exist because of
the work traditional biologists and chemists have done, don't scoff at
them because their method for uncovering knowledge seems awkward in
hindsight. The paradigms have served well in the past, that doesn't
mean they're fit for the future, but without them we wouldn't have
synthetic biology at all.

Give this a read if you want to understand the biologist/engineer
paradigm differences:
Can a Biologist Fix a Radio? —or, What I Learned while Studying
Apoptosis. Y. Lazebnik. Cold Spring Harbor Laboratory
http://www.protein.bio.msu.ru/biokhimiya/contents/v69/pdf/bcm_1403.pdf

> 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.

Don't ignore the simple chemistry of the various groups, the shape
only tells you where they are, and gives some indication to the
electronic state of the molecule.

> 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.

Isn't God a point of factual contention, thus limiting it to the realm
of theory? Doesn't science limit theory from becoming fact until proof
has been established?

>
> 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:

Seems like you're ignoring the folks who had the same idea in that
PNAS paper linked via the DIYbio discussion comparing E.coli to linux,
as well as Anselm who directly engaged you in that discussion. Seems
like you're saying you have secret knowledge that other's in the field
don't have, this sounds like the tenure-track professors you
complained about.

--
-Nathan

[Bryan Bishop]

On Fri, Feb 8, 2013 at 6:00 PM, Nathan McCorkle <nmz...@gmail.com> wrote:
> On Fri, Feb 8, 2013 at 3:22 PM, William Heath <wgh...@gmail.com> wrote:
>> 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).
>
> This has been discussed before in-depth, and it really is a vast
> simplification to say DNA is THE program.
>
> See:
> Comparing E. coli to Linux
> https://groups.google.com/d/topic/diybio/TfhBPXFgl4M/discussion

Also, here's another take that I like to refer to.

https://groups.google.com/d/msg/diybio/GxRTESzUWUI/b68eQEeluisJ

> I don't think that's entirely DIYbio's fault. Synthetic biology has been
> telling all the programmers that biology is just like programming for almost 10
> years now, if not more. So, there's a lot of hype you have to cut through. But,
> programmers misinterpret this as pessimism instead of fact. I am more
> optimistic than anyone, overly optimistic, wildly optimistic about things,
> which is hilarious when people accuse me of pessimism when pointing out that
> DNA isn't like software programming.
>
> "... there is no source, the bytecode has multiple reentrent abstractions, is
> unstable and has a very low signal to noise ratio, the runtime is
> unbootstrappable, the execution is nondeterministic, it tries to randomly
> integrate and execute code from other computers... multiple reentrant and
> self-modifying abstractions. absolutely everything has subtle side effects."

and:

> > entity that is "hackable"[1] in the same way a computer system is. In fact
>
> Yes, but what -isn't- hackable? Look, my gripe here is that cells are
> really -nothing- like a von neumann machine. They're both nonlinear
> dynamical systems that happen to carry lots of "code" that controls
> their evolution in time. That's the strongest similarity. Cells
> deserve more than crappy metaphors to other kinds of systems. If
> y'all really want to improve how we engineer cells, it's worth taking
> a few years to begin understanding how they really work.
>
> As Bryan pointed out, SynBio suffered for a long time under the
> domination of a naive pack of EE/CS enthusiasts who couldn't pull the
> blinkers from their eyes to see that they weren't operating in the
> same kind of world anymore. My recommendation to DiyBio enthusiasts
> is not to repeat their mistake.

Remember, the future owes you nothing, and it might not look like
anything you imagine except what you can build.

- Bryan
http://heybryan.org/
1 512 203 0507

[Nathan McCorkle]

Ujjwal,
Here's another discussion that started off talking about the term
thrown around by a lot of new DNA design softwares 'github for
biology'... and how you could simply just use git already, so it's
nothing too new. The discussion then morphs into ASCII vs other types
of digital DNA representations, considering space required and
additional features that DNA can posses other than A T G or C (i.e.
methylation). The discussion really get's interesting when Anselm
said:

On Fri, Sep 7, 2012 at 2:48 PM, Anselm Levskaya <levs...@gmail.com> wrote:
> There's too much comparison going on here with von neumann machine code.
>
> DNA "code" is intrinsically nonlocal, i.e.:
> - when you're editing a protein-coding ORF, there are long-range
> correlations across the entire structure of the gene that corresponds
> to the 3D interactions that define the shape and dynamics of the
> protein.
> - when you're playing around with regulatory elements (cis factors)
> and their protein regulators (trans factors), you're also dealing with
> a network of interactions that are non-local and complexly
> intertwingled across the sequence.
> - eukaryotic cells add a whole new level of state-machine logic on top
> of local (de)compaction as mediated by chromatin remodelling systems
> and poorly-understood insulator/enhancer systems.
>
> So you can't just expect to write useful linear source code unless
> you're doing the most primitive cut-and-paste style synbio of a
> handful of unmodified genes. i.e. just screwing around. (which is fun
> and great - but not really what synbio is ultimately about)
>
> Abstractions work in machine code because we built the machines to
> make abstractions possible. Natural cells don't work like that. The
> closest thing to cells in the machine code world are demoscene x86
> assembly blobs -- filled with insane hacks to make something awesome
> work in a small amount of space, with lots of weird code and data
> reuse and generative magic.
>
> It's worse than that though. Never forget that the true "compiler" of
> DNA is Physics, specifically that of protein folding, conformational
> dynamics, and catalysis. It won't be simulated anytime soon with
> anything approaching useful kinetic accuracy. (It's not clear if
> we'll ever get kT-accurate quantum simulations of correlated electron
> wavefunctions that scale to protein-sized systems, though there is
> some hope in the far future with exotic computing architectures.)
>
> So ultimately what software useful for synthetic biology is going to
> do is help us curate all of our brute-force efforts to build and
> screen libraries of pseudorational libraries of proteins, pathways,
> and cells. -Not- provide shitty abstraction layers that rest upon our
> incredibly shaky understanding of what's going on circa 2012. Think
> of curating an incredibly complicated genetic-programming run across a
> hundred-thousand clusters -- that gives a better sense of the flavor
> of what's needed.
>
> We invented the light-build long before we understood QFT. We'll be
> building amazing cellular machines long before we really understand
> them quantitatively. Synbio's (and diybio's) biggest sin is
> repeatedly elevating the convenient metaphors with EE/CS into a
> slick-looking action plan that doesn't respect the fundamental
> differences between these machine architectures.
>
> -a

Main discussion
https://groups.google.com/d/topic/diybio/GxRTESzUWUI/discussion


--
-Nathan

[Dakota Hamill]

Well to be honest I still don't see the difference between synthetic biology and molecular biology.  People have been cloning this into that and doing gene shuffling for decades now, and it was never called "synthetic".  The only thing that pops into my mind when synthetic biology is brought up was when the Craig Venter institute synthesized the entire genome of an organism, a few million base pairs.  I suppose if you have the resources you can dream up anything and synthesize the entire plasmid or genome of what you want, but it seems like most people still do a lot of core fundamental molecular bio to get what they want.

[Iván Esteban Araya]

Its just a matter of time to take distance from molecular biology, but the lines that guide the development of the field are clear. On the orther hand, obviously SB will use techniques and basic knowledge from molecular biology

[Nathan McCorkle]

On Fri, Feb 8, 2013 at 4:42 PM, Dakota Hamill <dko...@gmail.com> wrote:
> Well to be honest I still don't see the difference between synthetic biology
> and molecular biology.

I think the difference is the goal being to synthesize a biological
system. At the laboratory level it isn't any different than
molbio/cloning, but at the idea level synthetic biology is about
making new stuff or making it in a new way, while molecular biology
aims simply to understand ANY biological system at the molecular
level.



--
-Nathan

[Ujjwal Thaakar]

Dear William,

Thank you for taking out so much time to write all this for me. I cannot tell you how enlightening and guiding it was. This is was what I was looking for :)

I would like to learn more from you. Could you help me out?

Thanks
Ujjwal

[Ujjwal Thaakar]

Yeah I agree it's a vast simplification but I assume that this analogy might be helpful for somebody like me who doesn't understand biology but does understand computers. Eventually when I REALLY understand SB I'll realise how naive that analogy was.

[Ujjwal Thaakar]

Guys I've come across some really great links and ideas but you need to realise from my first post that I have absolutely bare minimum knowledge of SB. Can somebody give me links to get preliminary knowledge about DNA and genetics. How about khan academy?

[Bryan Bishop]

On Sat, Feb 9, 2013 at 4:17 AM, Ujjwal Thaakar <ujjwal...@gmail.com> wrote:

Guys I've come across some really great links and ideas but you need to realise from my first post that I have absolutely bare minimum knowledge of SB. Can somebody give me links to get preliminary knowledge about DNA and genetics. How about khan academy?

Khanacademy is extremely inefficient for knowledge transfer (the videos are really slow and underinformed). I suggest reading things instead. Like books.

http://diyhpl.us/wiki/diybio/faq/books

On Sat, Feb 9, 2013 at 4:15 AM, Ujjwal Thaakar <ujjwal...@gmail.com> wrote:

but I assume that this analogy might be helpful for somebody like me who doesn't understand biology but does understand computers

I think that assumption is wrong, and the other emails in this thread elaborated on that wrongness. Programming is very useful, but it will not give you magical insight into unknown engineering.