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:
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
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.
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.
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)
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?
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?
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
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.
[+] [-] logn|12 years ago|reply
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
[+] [-] spartango|12 years ago|reply
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.
[+] [-] timthimmaiah|12 years ago|reply
Its built on the cloud with NodeJS & MongoDB. Runs right in the browser.
Check it out. Its free.
http://teselagen.com/
[+] [-] elsbree|12 years ago|reply
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)
[+] [-] corwinbad|12 years ago|reply
:-)
[+] [-] minikomi|12 years ago|reply
[+] [-] lifeisstillgood|12 years ago|reply
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?
[+] [-] wikiburner|12 years ago|reply
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|12 years ago|reply
[+] [-] dnautics|12 years ago|reply
[+] [-] jonmrodriguez|12 years ago|reply
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|12 years ago|reply
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/