5 ms·

For the last year, I've been self-learning synthetic biology by working to become one of the few people to have ever genetically modified fungi in a diy lab env

For the last year, I've been self-learning synthetic biology by working to become one of the few people to have ever genetically modified fungi in a diy lab environment[1][2][3]. It occurred to me that genetic engineering is largely a process of hacking biosynthetic pathways; that is, we take heterologous genes from organisms which have evolved novel biochemical pathways and we put them into other organisms that are really good at biosynthesizing those compounds at-scale (like Saccharomyces cerevisiae aka yeast). One thing I've observed is there seems to be no universal well-annotated structured database for biosynthetic pathways. Updates and additions to known pathways are published unstructured in papers, often in graphical form, presenting even more challenging for structuring the data. Were this to exist, it would be possible to build an app that let you easily design DNA plasmids for testing all kinds of incredible biosynthesis experiments. For instance, it would be able to select an organism [E. coli], enter your starting substrate [glucose], select a desired metabolite [psilocybin] and out would come a DNA sequence for a plasmid that contains are the necessary parts, promoters, genes and terminators for transforming E. coli to produce psilocybin. In my opinion, such a tool could profoundly impact science, industry and human well-being. [1] http://everymanbio.com/ http://everymanbio.com/ [2] https://www.instagram.com/everymanbio/ https://www.instagram.com/everymanbio/ [3] https://youtube.com/everymanbio https://youtube.com/everymanbio

ddxxdd 5y ago
Before I start binging on every single aspect of your social media, I gotta know: Have you tried applying your hobby towards reducing the cost of healthcare in America? Could you somehow make monoclonal antibodies in your petri dishes? Could you diagnose what kind of flu virus you have? Because the phrase "self-learning synthetic biology" immediately rang a bell inside my mind that screamed "small business dedicated to cheaper healthcare", and is something that I have considered dedicating my free time towards.
- joshuamcginnis 5y ago
  > Have you tried applying your hobby towards reducing the cost of healthcare in America? That isn't my stated goal, but I would imagine that sharing how to genetically engineer an incredibly powerful organism to express heterologous genes for less than $3000 in a kitchen lab could open up many possibilities! > Could you diagnose what kind of flu virus you have? Is it possible to extract DNA from a bodily fluid, perform PCR and read the sequence results to determine flu variant? I'd have to check on if there is a known DNA sequence that barcodes flu viral variants, but presuming there is, then yes - all of this is possible from a home lab currently.
  - ta988 5y ago
    Yes with a nanopore device that's doable if you take a sample that has a high enough viral charge. You may evem be able to get reads without amplification.
  - Metacelsus 5y ago
    Influenza is an RNA virus so you'll need to reverse transcribe it.
- funklute 5y ago
  > Have you tried applying your hobby towards reducing the cost of healthcare in America? This isn't so much a technological problem, but more a politics/incentives issue, as evidenced by the comparably lower cost of healthcare in Europe. Startups might be able to make small reductions in the cost, but truly solving the issue requires re-organising the entire healthcare system. > Could you somehow make monoclonal antibodies in your petri dishes? This wouldn't be particularly useful. In order to use it in humans, you need a ton of quality control and regulatory approvals, and this is where a lot of the manufacturing cost crops up.
zaking17 5y ago
agreed! a couple things seem to be holding this back: - the academic publishing model incentivizes groups to build their own tools - the small-ish market for something like this has kept commercial software from taking off (Genomatica started by building a tool like this in the early 2000s, before pivoting to bioprocess development) - it's really hard to specify pathways in concrete physical terms. Even a chemical like glucose is actually a collection of pseudo-isomers (alpha & beta D-glucose). And try firmly defining a "gene" in your database! that being said, there's a ton of work going on in this field and many cool projects to follow: https://dd-decaf.eu https://dd-decaf.eu, https://biocyc.org https://biocyc.org, http://bigg.ucsd.edu http://bigg.ucsd.edu, http://metanetx.org http://metanetx.org
- joshuamcginnis 5y ago
  The database would definitely need to define some boundaries and limitations, but I still think there is much opportunity in coalescing well-defined metabolic and genetic data and empowering folks to generate feasible genetic constructs.
  - zaking17 5y ago
    You might also want some pathways to be pre-validated to work together in certain cellular contexts, like they have been doing with the BioBricks project
    - joshuamcginnis 5y ago
      Absolutely. This is probably the best and only way to start.
dekhn 5y ago
for the database, KEGG Pathways comes to mind immediately: https://www.genome.jp/pathway/map01100 https://www.genome.jp/pathway/map01100 many papers mention KEGG and use its references and strucfture as resources. https://molecular-cancer.biomedcentral.com/articles/10.1186/1476-4598-5-55?optIn=false https://molecular-cancer.biomedcentral.com/articles/10.1186/... (I'm not saying there's anything special about this paper in particular)
- amelius 5y ago
  Is KEGG open access?
  - jerven 5y ago
    No, https://www.kegg.jp/kegg/legal.html https://www.kegg.jp/kegg/legal.html
ravila4 5y ago
There are several databases that annotate metabolic pathways, although you are right that we lack a comprehensive integrated data source. Some of the larger ones: Reactome: https://reactome.org/ https://reactome.org/ KEGG: https://www.kegg.jp/kegg/pathway.html https://www.kegg.jp/kegg/pathway.html Wikipathways: https://www.wikipathways.org/index.php/WikiPathways https://www.wikipathways.org/index.php/WikiPathways BioCarta: https://www.hsls.pitt.edu/obrc/index.php?page=URL1151008585 https://www.hsls.pitt.edu/obrc/index.php?page=URL1151008585 SMPDB: https://smpdb.ca/ https://smpdb.ca/ ConsensusPathDB: http://cpdb.molgen.mpg.de/ http://cpdb.molgen.mpg.de/ Regarding the issue of of parsing pathway figure data from published papers: pathway-figure-ocr (https://github.com/hiplot/pathway-figure-ocr/ https://github.com/hiplot/pathway-figure-ocr/) is a project by the developers of Wikipathways which is trying to solve that issue.
- joshuamcginnis 5y ago
  That's a great list, but it's not just about annotated metabolic pathways; there also needs to be information about the gene(s) that have been characterized as being involved in those pathways, along with information about the source organisms and potential target organisms for production. Same with respect to substrates needed for intermediary pathways.
  - alevskaya 5y ago
    KEGG is a very useful index into known genes for this, I used it all the time when mining for possible enzymes in the past.
    - joshuamcginnis 5y ago
      Agreed! And if it were complemented with added information about the organisms transcription machinery (promoters, terminators, etc), it could empower folks to generate DNA to test a new or modified metabolic pathway in a manner that's never been tried before.
  - sjg007 5y ago
    Gene Ontology (GO) is what you are looking for. KEGG is also useful. Welcome to systems biology. Mapping transcriptional networks/pathways is more challenging and a major focus of current research across all model organisms.
refurb 5y ago
It’s probably helpful to know that these biochemical maps aren’t complete. It the best set of pathways that we can pull together based on current knowledge. Your idea of a program where you enter starting point and desired product is great in theory, but it’s similar to saying “oh, creating DropBox is easy, it’s just an interface linked to Amazon Cloud”. It’s missing about 99% of the stuff in the background nobody sees.
- ta988 5y ago
  Thank you, I keep having to explain to programmers that get excited about doing biology discoveries in short periods that you can't achieve that the way you achieve a prototype of a web service in a weekend... They often have the impression they are the ones doing the most complex thing in the world... And they think they understand the necessary bricks after a few hours of reading... Ahhh hubris
  - vikramkr 5y ago
    The fundamental disconnect seems to be that programming is working with defined languages and environments created by humans with tools created by humans to make further programming by humans easier. Biology is the result of billions of years of natural selection and random processes on hyper complicated chemical pathways. Something as simple as the epigenetics of expressing a gene - if its energetically unfavorable or even toxic, in many systems some sort of downregulation such as DNA methylation is going to occur and provide a competitive advantage in the culture. That's a single gotcha of thousands. Obviously, engineering biology in a robust and reliable way is quite doable, but, ya know, ya gotta know what you're doing and know what you're getting into.
    - refurb 5y ago
      Programming is a great analogy to biology if: - there is no documentation to start with - you can only learn the language through trial and error and development of crude frameworks that are accurate 75% of the time - you have no ability to look at the environment or operating system beyond “trying things” and making assumptions based on results - the environment itself resists most attempt at modifying it through feedback mechanisms (but you can’t see these either, just the effects) - no two computers + environments are identical. Code you write will mostly work in different computers, but occasionally won’t and sometimes will just brick (kill) the computer - when you learn a rule about changing a variable to produce Y affect, that rule doesn’t apply in 9 out of 10 scenarios
gwerbret 5y ago
As has been pointed out by others, there already exist databases that annotate many key metabolic pathways; however, I think the reason the database you describe doesn't exist is that there's a LOT more very customized work in recreating synthetic pathways in cells. Minimally, as you say, you'll produce plasmids that code for all the proteins you need. In practice, factors affecting your design decisions include, but are not limited to: * What your ultimate goals are (proof of concept versus lab/industrial-scale synthesis) * Your synthetic vehicle, i.e. in which organism you will be recreating this pathway * Whether the relevant pathway/genes already exist in some form in your vehicle, and may be affected by an introduction of foreign genes * How you will detect expression of your foreign proteins (the fact that your genes are expressed doesn't mean the corresponding proteins are produced, and if you can't confirm protein expression, the inevitable troubleshooting will be five kinds of hell) * What kinds of promoters you'll need to drive gene expression * How stable the proteins in this pathway are, and if you need to modify them to increase stability * Whether your proteins, after being produced, need extra tweaks (post-translational modifications, in the parlance) in order to work * How much DNA you'll need * How you'll deliver the DNA, i.e. what size/kind of delivery vector * How you'll make the delivery vector * Whether your proteins will fold properly once produced in the vehicle, and whether they will localize to the right parts of the cell * Whether your proteins are toxic to your vehicle cell *etc.
yolosholo 5y ago
What resources are you using to self-learn synthetic biology? I'm also interested in the topic. I'm looking for something more practical and hands on at this point. I was planning to start my home late in the next month or so, and start with some basics bacteria projects. So far I found the following to be useful: 1. EDX principals of synthetic biology: https://www.edx.org/course/principles-of-synthetic-biology https://www.edx.org/course/principles-of-synthetic-biology 2. Coursera systems biology: https://www.coursera.org/specializations/systems-biology https://www.coursera.org/specializations/systems-biology 3. Coursera Industrial biotechnology https://www.coursera.org/learn/industrial-biotech https://www.coursera.org/learn/industrial-biotech 4. SBOL https://sbolstandard.org https://sbolstandard.org 5. This GitHub: https://github.com/websemantics/awesome-synthetic-biology https://github.com/websemantics/awesome-synthetic-biology 6. https://barricklab.org/twiki/bin/view/Lab https://barricklab.org/twiki/bin/view/Lab 7. Synthetic biology primer (book)
- joshuamcginnis 5y ago
  I use two microbiology and mycology books I bought on OfferUp for $10, YouTube and papers. The vast amount of my learning comes from breaking larger technical goals into a series of smaller hands-on projects and then troubleshooting my way through. It's hard to give general advice on synbio since it's such a broad field and my journey principally involves a focus on fungi. In my case, I learned to grow all kinds of mushrooms and fungus at home. That lead me to learning DNA barcoding of fungi which has now lead me to DNA transformation of fungi. Feel free to send me more info about the direction you want to head in and I'd be happy to give you some feedback.
beowulfey 5y ago
Check out BRENDA [1]. It's the best one I've found for annotated pathways. With regards to your tool idea, it is still very expensive to generate DNA from scratch. Designing a theoretical plasmid won't get you all the way there if you can't source the parent DNA sequence. We usually pay ~$0.30/base for DNA synthesis. Plus, I'm not sure what the metabolic pathway of psilocybin is but you may need several plasmids to recreate the whole thing in E. coli (generally limited to ~10-15 KB). An interesting idea but definitely no small feat of engineering. [1] https://www.brenda-enzymes.org/pathway_index.php https://www.brenda-enzymes.org/pathway_index.php
jerven 5y ago
I would have a look at Rhea. Which is not a pathway database but a which chemical reactions are out there in biology. This can be combined with the data in UniProt to build metabolic pathways. And then at metanetx.org to build draft metabolic models. We recently had a collaboration with AWS Open Data+Neptune to show how easy it is to combine Rhea,ChEBI and UniProt in your own database. [1] https://www.rhea-db.org https://www.rhea-db.org [2] https://www.metanetx.org https://www.metanetx.org [3] https://academic.oup.com/bioinformatics/article/36/6/1896/5613180 https://academic.oup.com/bioinformatics/article/36/6/1896/56... [4] https://aws.amazon.com/blogs/industries/exploring-the-uniprot-protein-knowledgebase-with-aws-open-data-and-amazon-neptune/ https://aws.amazon.com/blogs/industries/exploring-the-unipro...

ddxxdd 5y ago

Before I start binging on every single aspect of your social media, I gotta know: Have you tried applying your hobby towards reducing the cost of healthcare in America? Could you somehow make monoclonal antibodies in your petri dishes? Could you diagnose what kind of flu virus you have? Because the phrase "self-learning synthetic biology" immediately rang a bell inside my mind that screamed "small business dedicated to cheaper healthcare", and is something that I have considered dedicating my free time towards.

joshuamcginnis 5y ago
> Have you tried applying your hobby towards reducing the cost of healthcare in America? That isn't my stated goal, but I would imagine that sharing how to genetically engineer an incredibly powerful organism to express heterologous genes for less than $3000 in a kitchen lab could open up many possibilities! > Could you diagnose what kind of flu virus you have? Is it possible to extract DNA from a bodily fluid, perform PCR and read the sequence results to determine flu variant? I'd have to check on if there is a known DNA sequence that barcodes flu viral variants, but presuming there is, then yes - all of this is possible from a home lab currently.
- ta988 5y ago
  Yes with a nanopore device that's doable if you take a sample that has a high enough viral charge. You may evem be able to get reads without amplification.
- Metacelsus 5y ago
  Influenza is an RNA virus so you'll need to reverse transcribe it.
funklute 5y ago
> Have you tried applying your hobby towards reducing the cost of healthcare in America? This isn't so much a technological problem, but more a politics/incentives issue, as evidenced by the comparably lower cost of healthcare in Europe. Startups might be able to make small reductions in the cost, but truly solving the issue requires re-organising the entire healthcare system. > Could you somehow make monoclonal antibodies in your petri dishes? This wouldn't be particularly useful. In order to use it in humans, you need a ton of quality control and regulatory approvals, and this is where a lot of the manufacturing cost crops up.

zaking17 5y ago

agreed! a couple things seem to be holding this back: - the academic publishing model incentivizes groups to build their own tools - the small-ish market for something like this has kept commercial software from taking off (Genomatica started by building a tool like this in the early 2000s, before pivoting to bioprocess development) - it's really hard to specify pathways in concrete physical terms. Even a chemical like glucose is actually a collection of pseudo-isomers (alpha & beta D-glucose). And try firmly defining a "gene" in your database! that being said, there's a ton of work going on in this field and many cool projects to follow: https://dd-decaf.eu https://dd-decaf.eu, https://biocyc.org https://biocyc.org, http://bigg.ucsd.edu http://bigg.ucsd.edu, http://metanetx.org http://metanetx.org

joshuamcginnis 5y ago
The database would definitely need to define some boundaries and limitations, but I still think there is much opportunity in coalescing well-defined metabolic and genetic data and empowering folks to generate feasible genetic constructs.
- zaking17 5y ago
  You might also want some pathways to be pre-validated to work together in certain cellular contexts, like they have been doing with the BioBricks project
  - joshuamcginnis 5y ago
    Absolutely. This is probably the best and only way to start.

dekhn 5y ago

for the database, KEGG Pathways comes to mind immediately: https://www.genome.jp/pathway/map01100 https://www.genome.jp/pathway/map01100 many papers mention KEGG and use its references and strucfture as resources. https://molecular-cancer.biomedcentral.com/articles/10.1186/1476-4598-5-55?optIn=false https://molecular-cancer.biomedcentral.com/articles/10.1186/... (I'm not saying there's anything special about this paper in particular)

amelius 5y ago
Is KEGG open access?
- jerven 5y ago
  No, https://www.kegg.jp/kegg/legal.html https://www.kegg.jp/kegg/legal.html

ravila4 5y ago

There are several databases that annotate metabolic pathways, although you are right that we lack a comprehensive integrated data source. Some of the larger ones: Reactome: https://reactome.org/ https://reactome.org/ KEGG: https://www.kegg.jp/kegg/pathway.html https://www.kegg.jp/kegg/pathway.html Wikipathways: https://www.wikipathways.org/index.php/WikiPathways https://www.wikipathways.org/index.php/WikiPathways BioCarta: https://www.hsls.pitt.edu/obrc/index.php?page=URL1151008585 https://www.hsls.pitt.edu/obrc/index.php?page=URL1151008585 SMPDB: https://smpdb.ca/ https://smpdb.ca/ ConsensusPathDB: http://cpdb.molgen.mpg.de/ http://cpdb.molgen.mpg.de/ Regarding the issue of of parsing pathway figure data from published papers: pathway-figure-ocr (https://github.com/hiplot/pathway-figure-ocr/ https://github.com/hiplot/pathway-figure-ocr/) is a project by the developers of Wikipathways which is trying to solve that issue.

joshuamcginnis 5y ago
That's a great list, but it's not just about annotated metabolic pathways; there also needs to be information about the gene(s) that have been characterized as being involved in those pathways, along with information about the source organisms and potential target organisms for production. Same with respect to substrates needed for intermediary pathways.
- alevskaya 5y ago
  KEGG is a very useful index into known genes for this, I used it all the time when mining for possible enzymes in the past.
  - joshuamcginnis 5y ago
    Agreed! And if it were complemented with added information about the organisms transcription machinery (promoters, terminators, etc), it could empower folks to generate DNA to test a new or modified metabolic pathway in a manner that's never been tried before.
- sjg007 5y ago
  Gene Ontology (GO) is what you are looking for. KEGG is also useful. Welcome to systems biology. Mapping transcriptional networks/pathways is more challenging and a major focus of current research across all model organisms.

refurb 5y ago

It’s probably helpful to know that these biochemical maps aren’t complete. It the best set of pathways that we can pull together based on current knowledge. Your idea of a program where you enter starting point and desired product is great in theory, but it’s similar to saying “oh, creating DropBox is easy, it’s just an interface linked to Amazon Cloud”. It’s missing about 99% of the stuff in the background nobody sees.

ta988 5y ago
Thank you, I keep having to explain to programmers that get excited about doing biology discoveries in short periods that you can't achieve that the way you achieve a prototype of a web service in a weekend... They often have the impression they are the ones doing the most complex thing in the world... And they think they understand the necessary bricks after a few hours of reading... Ahhh hubris
- vikramkr 5y ago
  The fundamental disconnect seems to be that programming is working with defined languages and environments created by humans with tools created by humans to make further programming by humans easier. Biology is the result of billions of years of natural selection and random processes on hyper complicated chemical pathways. Something as simple as the epigenetics of expressing a gene - if its energetically unfavorable or even toxic, in many systems some sort of downregulation such as DNA methylation is going to occur and provide a competitive advantage in the culture. That's a single gotcha of thousands. Obviously, engineering biology in a robust and reliable way is quite doable, but, ya know, ya gotta know what you're doing and know what you're getting into.
  - refurb 5y ago
    Programming is a great analogy to biology if: - there is no documentation to start with - you can only learn the language through trial and error and development of crude frameworks that are accurate 75% of the time - you have no ability to look at the environment or operating system beyond “trying things” and making assumptions based on results - the environment itself resists most attempt at modifying it through feedback mechanisms (but you can’t see these either, just the effects) - no two computers + environments are identical. Code you write will mostly work in different computers, but occasionally won’t and sometimes will just brick (kill) the computer - when you learn a rule about changing a variable to produce Y affect, that rule doesn’t apply in 9 out of 10 scenarios

gwerbret 5y ago

As has been pointed out by others, there already exist databases that annotate many key metabolic pathways; however, I think the reason the database you describe doesn't exist is that there's a LOT more very customized work in recreating synthetic pathways in cells. Minimally, as you say, you'll produce plasmids that code for all the proteins you need. In practice, factors affecting your design decisions include, but are not limited to: * What your ultimate goals are (proof of concept versus lab/industrial-scale synthesis) * Your synthetic vehicle, i.e. in which organism you will be recreating this pathway * Whether the relevant pathway/genes already exist in some form in your vehicle, and may be affected by an introduction of foreign genes * How you will detect expression of your foreign proteins (the fact that your genes are expressed doesn't mean the corresponding proteins are produced, and if you can't confirm protein expression, the inevitable troubleshooting will be five kinds of hell) * What kinds of promoters you'll need to drive gene expression * How stable the proteins in this pathway are, and if you need to modify them to increase stability * Whether your proteins, after being produced, need extra tweaks (post-translational modifications, in the parlance) in order to work * How much DNA you'll need * How you'll deliver the DNA, i.e. what size/kind of delivery vector * How you'll make the delivery vector * Whether your proteins will fold properly once produced in the vehicle, and whether they will localize to the right parts of the cell * Whether your proteins are toxic to your vehicle cell *etc.

yolosholo 5y ago

What resources are you using to self-learn synthetic biology? I'm also interested in the topic. I'm looking for something more practical and hands on at this point. I was planning to start my home late in the next month or so, and start with some basics bacteria projects. So far I found the following to be useful: 1. EDX principals of synthetic biology: https://www.edx.org/course/principles-of-synthetic-biology https://www.edx.org/course/principles-of-synthetic-biology 2. Coursera systems biology: https://www.coursera.org/specializations/systems-biology https://www.coursera.org/specializations/systems-biology 3. Coursera Industrial biotechnology https://www.coursera.org/learn/industrial-biotech https://www.coursera.org/learn/industrial-biotech 4. SBOL https://sbolstandard.org https://sbolstandard.org 5. This GitHub: https://github.com/websemantics/awesome-synthetic-biology https://github.com/websemantics/awesome-synthetic-biology 6. https://barricklab.org/twiki/bin/view/Lab https://barricklab.org/twiki/bin/view/Lab 7. Synthetic biology primer (book)

joshuamcginnis 5y ago
I use two microbiology and mycology books I bought on OfferUp for $10, YouTube and papers. The vast amount of my learning comes from breaking larger technical goals into a series of smaller hands-on projects and then troubleshooting my way through. It's hard to give general advice on synbio since it's such a broad field and my journey principally involves a focus on fungi. In my case, I learned to grow all kinds of mushrooms and fungus at home. That lead me to learning DNA barcoding of fungi which has now lead me to DNA transformation of fungi. Feel free to send me more info about the direction you want to head in and I'd be happy to give you some feedback.

beowulfey 5y ago

Check out BRENDA [1]. It's the best one I've found for annotated pathways. With regards to your tool idea, it is still very expensive to generate DNA from scratch. Designing a theoretical plasmid won't get you all the way there if you can't source the parent DNA sequence. We usually pay ~$0.30/base for DNA synthesis. Plus, I'm not sure what the metabolic pathway of psilocybin is but you may need several plasmids to recreate the whole thing in E. coli (generally limited to ~10-15 KB). An interesting idea but definitely no small feat of engineering. [1] https://www.brenda-enzymes.org/pathway_index.php https://www.brenda-enzymes.org/pathway_index.php

jerven 5y ago

I would have a look at Rhea. Which is not a pathway database but a which chemical reactions are out there in biology. This can be combined with the data in UniProt to build metabolic pathways. And then at metanetx.org to build draft metabolic models. We recently had a collaboration with AWS Open Data+Neptune to show how easy it is to combine Rhea,ChEBI and UniProt in your own database. [1] https://www.rhea-db.org https://www.rhea-db.org [2] https://www.metanetx.org https://www.metanetx.org [3] https://academic.oup.com/bioinformatics/article/36/6/1896/5613180 https://academic.oup.com/bioinformatics/article/36/6/1896/56... [4] https://aws.amazon.com/blogs/industries/exploring-the-uniprot-protein-knowledgebase-with-aws-open-data-and-amazon-neptune/ https://aws.amazon.com/blogs/industries/exploring-the-unipro...