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The demon lurking in the corner here is the a priori assumption that we know enough about nucleic acids to be confident in our knowledge of how they'll behave o

The demon lurking in the corner here is the a priori assumption that we know enough about nucleic acids to be confident in our knowledge of how they'll behave once compiled. I.e., i write some code, hit F5, and yeast comes out the other side of the compiler-machine. How confident can I be while debugging that the changes in behavior between the compiled output (yeast) and my code are bugs versus as-yet-unknown behaviors of complex molecules bumping into each other in a very crowded bag of other complex molecules sitting in a dish somewhere? As of right now, and I posit, as a biological researcher, for a very long time to come, the answer is "A lot less than 100%". Simple stuff like gene-toggle switches [0] have been known and pretty much work since the late 90s, but scaling is deeply nonlinear. This doesn't mean that synbio is useless, but it means that the error bars are a lot higher than they are when you're running code on printed circuits and can have a high degree of confidence that your computational substrate actually does what you think it does. [0]: https://www.nature.com/articles/35002131 https://www.nature.com/articles/35002131

deleted 8y ago
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newhacker2719 8y ago
Well, if you were creating the language you would have to test that the expected output was repeatable. And presumably before someone who was creating the language would advise the end users of that software not to use it in production until the 1.0 version. Basically you would wait until someone smarter than you or I posts it on Hacker News and says it's production ready.
hsikka 8y ago
This is an awesome detailed response from an informed perspective, thank you! I’ll check out the article. Do you think that despite the high error resulting from the sheer complexity of those interactions, design tools would be useful to researchers? Or is it mostly just long shot proof of concepts with no practical purpose right now?
- jfarlow 8y ago
  Design tools are absolutely useful to researchers. However, the economics of building/selling software to researchers don't work. The state of the art DNA design app that is the gold-standard for every molecular biologist is not much changed over the past 15 years [1]. And it's free. [1] http://jorgensen.biology.utah.edu/wayned/ape/ http://jorgensen.biology.utah.edu/wayned/ape/
  - iso1337 8y ago
    One could argue that the state of the art is now benchling. But I have similar thoughts on the economics of building software to researchers. The domain is complex, the customers require a high level of correctness, and features are often implemented to try things out in the lab and then abandoned once it's a dead-end in the real world. Oh, and most academics don't have money to pay for software, nor do they respect the software engineering process.
    - asdff 8y ago
      I'm in cell biology, and many of the programs used in research these days are run from the command line and are open source and freely available. I don't even have a desktop powerful enough to work with genomic sequencing data, I need to ssh to a supercomputer. There's a lot of documentation so it's really not hard to go from understanding Unix command line syntax to writing short scripts then full on python or Perl with biological examples every step of the way. There are also a lot of very powerful web tools, such as the ucsc genome browser, which are freely available to anyone. Commercial gui-based software is usually sold along with proprietary equipment like a microscope or something, and universally loathed even by people who managed to wrap their heads around it. It's much easier to work with different software from different people or groups if it's all the same syntax, rather than relearning clunky proprietary gui after clunky proprietary gui.
etrautmann 8y ago
A great example of this is Green Fluorescent Protein (GFP), one of the most important and foundational fluorescent proteins for biology research. The fluorescent molecule at the center is not actually a nucleic acid, but is formed several hours after the protein has folded after a secondary reaction between some of the atoms at the center of the protein find a lower energy state. This seems to me (not a protein engineer) to be virtually impossible to design a-priori.
- jfarlow 8y ago
  Except this exact thing was accomplished and published a few months ago [1, 2]. The Baker lab at the University of Washington designed a synthetic fluorescent protein, de novo. [1] Meta-Meta-Article: https://www.bakerlab.org/index.php/2019/01/02/nature-article-ipd-work-voted-2018-readers-choice/ https://www.bakerlab.org/index.php/2019/01/02/nature-article... [2] Actual paper: https://www.nature.com/articles/s41586-018-0509-0 https://www.nature.com/articles/s41586-018-0509-0
  - iso1337 8y ago
    eh, it still took ~56 real-world attempts to get two functional designs. That's not even quantifying how strongly they fluoresce, and other qualitative/quantitative aspects (eg, how good of a GFP were these proteins). It's very impressive, and the Baker lab has done amazing stuff, but we are a long ways off from rational design. From the paper: "Synthetic genes encoding the 56 designs were obtained and the proteins were expressed in E. coli. Thirty-eight of the proteins were wellexpressed and soluble; SEC and far-ultraviolet CD spectroscopy showed that 20 were monomeric β-sheet proteins (Supplementary Table 3). Four of the oligomer-forming designs became monomeric upon incorporation of a disulfide bond between the N-terminal 3–10 helix and the barrel β-strands. The crystal structure of one of the monomeric designs (b10) was solved to 2.1 Å, and was found to be very close to the design model (0.57 Å backbone r.m.s.d., Fig. 3c)." "Two of the 20 monomeric designs—b11 and b32—were found to activate DFHBI fluorescence by 12- and 8-fold with binding dissociation constants (KD) values of 12.8 and 49.8 μM, respectively (Extended Data Fig. 6f)"
  - etrautmann 8y ago

deleted 8y ago

[deleted]

newhacker2719 8y ago

Well, if you were creating the language you would have to test that the expected output was repeatable. And presumably before someone who was creating the language would advise the end users of that software not to use it in production until the 1.0 version. Basically you would wait until someone smarter than you or I posts it on Hacker News and says it's production ready.

hsikka 8y ago

This is an awesome detailed response from an informed perspective, thank you! I’ll check out the article. Do you think that despite the high error resulting from the sheer complexity of those interactions, design tools would be useful to researchers? Or is it mostly just long shot proof of concepts with no practical purpose right now?

jfarlow 8y ago
Design tools are absolutely useful to researchers. However, the economics of building/selling software to researchers don't work. The state of the art DNA design app that is the gold-standard for every molecular biologist is not much changed over the past 15 years [1]. And it's free. [1] http://jorgensen.biology.utah.edu/wayned/ape/ http://jorgensen.biology.utah.edu/wayned/ape/
- iso1337 8y ago
  One could argue that the state of the art is now benchling. But I have similar thoughts on the economics of building software to researchers. The domain is complex, the customers require a high level of correctness, and features are often implemented to try things out in the lab and then abandoned once it's a dead-end in the real world. Oh, and most academics don't have money to pay for software, nor do they respect the software engineering process.
  - asdff 8y ago
    I'm in cell biology, and many of the programs used in research these days are run from the command line and are open source and freely available. I don't even have a desktop powerful enough to work with genomic sequencing data, I need to ssh to a supercomputer. There's a lot of documentation so it's really not hard to go from understanding Unix command line syntax to writing short scripts then full on python or Perl with biological examples every step of the way. There are also a lot of very powerful web tools, such as the ucsc genome browser, which are freely available to anyone. Commercial gui-based software is usually sold along with proprietary equipment like a microscope or something, and universally loathed even by people who managed to wrap their heads around it. It's much easier to work with different software from different people or groups if it's all the same syntax, rather than relearning clunky proprietary gui after clunky proprietary gui.

etrautmann 8y ago

A great example of this is Green Fluorescent Protein (GFP), one of the most important and foundational fluorescent proteins for biology research. The fluorescent molecule at the center is not actually a nucleic acid, but is formed several hours after the protein has folded after a secondary reaction between some of the atoms at the center of the protein find a lower energy state. This seems to me (not a protein engineer) to be virtually impossible to design a-priori.

jfarlow 8y ago
Except this exact thing was accomplished and published a few months ago [1, 2]. The Baker lab at the University of Washington designed a synthetic fluorescent protein, de novo. [1] Meta-Meta-Article: https://www.bakerlab.org/index.php/2019/01/02/nature-article-ipd-work-voted-2018-readers-choice/ https://www.bakerlab.org/index.php/2019/01/02/nature-article... [2] Actual paper: https://www.nature.com/articles/s41586-018-0509-0 https://www.nature.com/articles/s41586-018-0509-0
- iso1337 8y ago
  eh, it still took ~56 real-world attempts to get two functional designs. That's not even quantifying how strongly they fluoresce, and other qualitative/quantitative aspects (eg, how good of a GFP were these proteins). It's very impressive, and the Baker lab has done amazing stuff, but we are a long ways off from rational design. From the paper: "Synthetic genes encoding the 56 designs were obtained and the proteins were expressed in E. coli. Thirty-eight of the proteins were wellexpressed and soluble; SEC and far-ultraviolet CD spectroscopy showed that 20 were monomeric β-sheet proteins (Supplementary Table 3). Four of the oligomer-forming designs became monomeric upon incorporation of a disulfide bond between the N-terminal 3–10 helix and the barrel β-strands. The crystal structure of one of the monomeric designs (b10) was solved to 2.1 Å, and was found to be very close to the design model (0.57 Å backbone r.m.s.d., Fig. 3c)." "Two of the 20 monomeric designs—b11 and b32—were found to activate DFHBI fluorescence by 12- and 8-fold with binding dissociation constants (KD) values of 12.8 and 49.8 μM, respectively (Extended Data Fig. 6f)"
- etrautmann 8y ago