Microbe computers

http://stanmed.stanford.edu/2013fall/article10.html

by aethertap • 13 years ago

45 19 13 years ago

stanmed.stanford.edu

logn2• 13 years ago

If we want bio computing to take off, somehow we need an IDE for this stuff. I thought following install instructions for typical software was hard, check this out:

http://openwetware.org/wiki/ChIP-Chip_E._coli

edit:

found this: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2989930/

BioCoder, a C++ library that enables biologists to express the exact steps needed to execute a protocol. In addition to being suitable for automation, BioCoder converts the code into a readable, English-language description for use by biologists

timthimmaiah• 13 years ago

You have the right idea. I'm currently build a Bio-CAD/CAM system for engineering biology. Like an AutoCAD for genetic engineering that's easy to use and even provides tools for downstream assembly automation in the lab.

Its built on the cloud with NodeJS & MongoDB. Runs right in the browser.

Check it out. Its free.

http://teselagen.com/

corwinbad• 13 years ago

Also Try http://genomecompiler.com

:-)

elsbree• 13 years ago

Agreed, the current state of software tools for biology is sad- the tools are written by scientists, for scientists, and tend to have messy source code and incomplete/difficult to read documentation.

I don't mean to insult the people who work on the tools currently- they're great! But we need more software people writing tools for the industry.

Fortunately, people are starting to do just that. TeselaGen and Genome Compiler are both good examples. (Disclaimer- I'm a TeselaGen engineer)

dnautics4• 13 years ago

Genome compiler is not a good example. The fundamental premise is incorrect. Biology should not be done as a drag-and-drop exercise... Saving a few minutes of your time is not worth having blinders that increase the likelihood of huge errors in your design. Having an intimate and comprehensive knowledge of your sequence is critical, and having a casual knowledge can lead to disaster. This is not for just anyone, either, you also have to have instant, library recall of as much as possible.

To give an example, I once witnessed an algorithmic redesign effort completely miss two extragenic components in an essential gene that would likely not have been missed by an attentive human (or better yet two or three attentive humans). Luckily, the cells evolved their way around it, the researchers tracked down the problem and how the bugs solved it and the situation is interesting enough to possibly result in a publication.

Fomite2• 13 years ago

It's not necessarily a people problem as much as an incentives problem - the incentives around Biology are, at present, entirely at odds with writing good software.

Clean, well-documented source code won't get you grants. It won't yield citations. It won't get you tenure. Beyond making sure you can run the same code again, and it works if the postdoc who wrote it leaves, everything else is under the "For the good of humanity" incentive structure. And with grant paylines in the middling single digits, its really hard not to triage good code in favor of making sure the lights stay on.

minikomi• 13 years ago

Oh wow, that's awesome. One wonders if there's something to be cribbed from the Recipe world? Voice activated step by step app with built in timers? Cookpad for wetlab? labgenius?

spartango2• 13 years ago

While I definitely agree that the software around synthetic biology is weak, I think this is not actually the problem bottlenecking the field. Ultimately, most efforts in synthetic biology are gated foremost by a lack of underlying biological knowledge. Automation may aid discovery, but the number of components and complexity of biological systems leaves many unanswered questions.

Yes, There have been demonstrators of biological computing and circuits, but from a functional perspective they are extremely limited, especially compared to in vivo systems. Put a different way, even if we can do complex "computation" in a cell, our understanding of the "I/O" is too superficial to design systems to treat diseases or act as devices. The failure of rational drug design over the last 20 years has taught the pharmaceutical community this lesson quite harshly.

Understanding the interfaces and mechanisms that our "biological computers" will work with will require more research in fields like structural and molecular biology, and this work will take time.

With that said, there are some useful systems we can engineer in the meantime; for example, biosynthesis pathway work is well underway. Just realize that these systems don't require substantial engineered computation.

dnautics4• 13 years ago

While I definitely agree that the software around synthetic biology is weak, I think this is not actually the problem bottlenecking the field. Ultimately, most efforts in synthetic biology are gated foremost by a lack of underlying biological knowledge.

This is spot-on correct. In the lab I worked in, we are working on a gene cluster of 11 genes, that result in the assembly of an enzyme that creates hydrogen. The PI's project was to play around with "promoters" that tell the bacterium to produce the genes. Think of this as 'script kiddie' work. I came in and immediately told him that this was the wrong approach; we had to alter the structure and chemistry of the enzymes themselves (think of this as 'assembler-level hacking'). Thankfully he was sympathetic to my argument and let me play around; I achieved a 3x improvement in enzyme activity. This summer,we tested the original idea, and all of the variants we tested were worse than what we started out.

Also, and this is my highly opinionated position informed by my experience as the sole chemist in a synthetic biology lab, but part of the problem is that a lot of biologists tracked themselves into biology because they didn't do so hot at chemistry. Many synthetic biology problems are chemistry problems, and even problems that are not on first blush like those that Drew Endy are trying to solve really become easy to grok if you're used to thinking qualitatively in terms of statistical thermodynamics and can mentally estimate collisions per nanosecond, from concentrations and kinetic parameters. These are quintessentially chemistry skills.

reference: https://www.ncbi.nlm.nih.gov/pubmed/23819621

lifeisstillgood• 13 years ago

This feeds into one of my pet questions : will Moore's Law really die?

Doubling transistor density on silicon will end about 2020/22, when 7 or 5nm etching occurs - beyond that and chip designers are past the wavelength of red (?) light and into quantum tunnelling effects

But ...

maybe the amount of CPU cycles available to use for a given bi-annual price will keep doubling. More efficient systems on a chip, cooling in huge data centers means Siri can keep doubling its ability to run voice analysis on my behalf?

is that true?

3. is there genuinely any chance things like bio-computing?

dnautics4• 13 years ago

3. My opinion is no.

wikiburner• 13 years ago

Also:

Memristors

Graphene

3D Chips

Optical Computing

Spintronics

Quantum Computing

and I'm pretty sure I'm forgetting a couple of other promising paths to extending Moore's Law.

Microbe computers strike me more as a path to nanorobotics.

ksrm• 13 years ago

We're already way beyond the wavelength of visible light - which is around 400-700 nm.

jonmrodriguez• 13 years ago

Hurray, Dr. Endy, Monica, Jerome, and Pakpoom! :) I interned in this lab and it's really cool seeing their amazing work (especially using M13 for high-bandwidth communication) get the press it deserves

Let's also not forget Paul Jaschke! http://www.researchgate.net/profile/Paul_Jaschke/ He was able to design and produce a working refactored version of the PhiX174 bacteriophage.

Refactoring in bio has the typical CS connotation of cleaning up messy code -- The natural PhiX174 has some overlapping genes that cannot be edited in isolation -- The refactored version has no gene overlaps, so the genes can be editted individually in a more rational manner.

mrcactu5• 13 years ago

if the article is too difficult to follow, there is always

  Adventures in Synthetic Biology

http://www.nature.com/nature/comics/syntheticbiologycomic/

That is the comic I read a Synthetic Biology REU at Princeton. At that point, Drew Endy was at MIT and our leader Ron Weiss was at Princeton.

People have shuffled around a bit in 7 years http://openwetware.org/wiki/Endy:Lab http://groups.csail.mit.edu/synbio/

Microbe computers

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