Biopunks help solve structure of key viral enzyme

I've been meaning to blog about the program FoldIt for some time.  It's a program out of David Baker's lab (link to University of Washington) where users can manipulate protein structures to improve folding but in a unique twist, the improvement is 'scored', just like a game.  A problem is posed on the website and thousands of players compete to see who can find the best solution. They also have regular competitions to see how well players can predict the structure of a protein from just the amino acid sequence (the Holy Grail of the protein folding world).  I've played on and off for a couple of years and it's pretty fun... but it's a lot like my day job, so if I take the time to play a video game it is usually something like Rock Band.  Still, FoldIt is a pretty easy game to play (the number of rules is limited and the GUI is very intuitive) so the real challenge is in the player's ability to use logic and their skill in 3D visualization. I'll have a more detailed post about it and its less-familiar cousin EteRNA (the RNA equivalent of FoldIt) later. When you are ready to play, click here (it's free, easy, and fun... really!)

This week in the journal Nature Structural and Molecular Biology (I have had several rejection letters from this fine journal) is an article (abstract) describing the use of FoldIt to solve a scientific problem at least ten years old!  Crystallographers have been trying to determine the structure of the monomeric form of MPV protease. MPV stands for Mason Pfizer Virus, a virus that causes an AIDS-like syndrome in monkeys.  Although it is not HIV per se, the protease is a key enzyme in the production of a mature virus and so developing drugs using this structure could be useful in developing an HIV therapy.  This protein was one of those rare cases where good crystals could be grown but interpreting the x-ray data back into the final 3D picture of the protein could not be done. So, researchers looked to crowd-sourcing as a means to solve this problem.  For three weeks, FoldIt players had the chance to optimize a 3D model, based on an NMR structure of the monomer.  Teams competed to see who could come up with the best solution (based on how well the different structural pieces of the protein fit together).  After all that tweaking and optimizing, more than a million different models were created.  Crystallographers used the best of these models as a starting point and one of them successfully generated a solution to the x-ray data using molecular replacement.

Who were the people who provided the key to success?  The top groups were listed as co-authors by their team name (FoldIt Contenders Group and FoldIt Void Crushers Group) and by a few handles listed in the article (spvincent, grabhorn, mimi) so not much is known about these folks, but I'll bet many of them had no knowledge of crystallography or biochemistry and probably had no clue what this enzyme did.  However, the fact that thousands of players worked together on this problem and were ultimately successful highlights the power of crowd-sourcing and that top-flight science can be accomplished through a "game".  I think this paper will also motivate others, both players and scientists, to leverage the power of biopunks!

Preliminary functional annotation of O104:H4 genes/proteins by Era7 Bioinformatics

Just a quick follow-up to my last post... here is a pdf of a paper (link) from the Oh No Sequences group at Era7 Bioinformatics that lists the full functional annotation of O104:H4.  It represents an amazing amount of work and is a great reference for anyone studying the O104:H4 strain. Major kudos to the ONS group!!

Using O104:H4 EHEC data... an example

I've had a few requests for an example of how to work with the new EHEC data.  I agree it can be very overwhelming to have  hundreds of Megabytes of genomic data, so here is a fairly simple example of what one might do and what you might encounter.  Suppose you had a drug (antibody, peptide, small molecule) and you knew it hit a protein called EprK.  EprK is an approximately 250 amino acid protein that is part of the Type III Secretion System (T3SS).  The T3SS is the cell-surface protein complex that attaches the pathogenic bacteria to the host cells.  Blocking proteins like EprK is one possible way to prevent EHEC pathogens from attacking normal cells and causing disease. Your drug works on other EHEC strains (such as O157:H7, the strain responsible for the 2006 outbreak in the US) but will it work on O104:H4?  Testing it directly is the best way to know, but obtaining the new strain is likely to be very difficult.  Another option is to go to the sequence data. 

I went to one of the sites that has the new sequence information (based on 'crowdsourcing' from various labs) on O104:H4 (I used the oh no sequences blog -- the blog for the R&D section of era7 bioinformatics) and found the identifier code for the EprK protein (here's the link).  Some of the data has been annotated based on sequence homology and EprK was one that has been identified.  Using this code, I found the DNA sequence and copied it to the clipboard.  Then I went to the NCBI website (link) and pasted the DNA sequence into the search box to do a BLAST search of all microbial genomes that have been sequenced.  There were dozens of hits, and nearly all of them were EprK proteins from various strains.  I found the O157:H7 strain and the alignment is impressive.  More than 95% of the DNA bases are identical between the two, suggesting that the two proteins are very similar. I've included the BLAST results of my search below using O104:H4 EprK (Query, top strand) and it's alignment with O157:H7 EprK (bottom strand). So, your drug probably works on the new strain too.  If you want the amino acid sequence of the O104:H4 strain, simply take the DNA sequence to ExPaSy (link) and translate it.  It actually took me a bit to get the protein sequence because there is a frameshift mutation in the O104:H4 sequence read.  If you scroll down to my alignment and find the part highlighted in red, you will see there is an extra adenosine (an 'A' base) in the O104 sequence.  This throws off the protein translation.  I assume it is a mis-read in the O104 sequence (a common mistake when the sequencing machine reads through a string of the same base) and deleted it when I translated from DNA to protein.  The resulting amino acid sequence (pasted below) is very similar to EprK from other EHEC strains.  I'll double check this and follow up with them.

     Anyhow, I don't think there is a structure for the EprK protein, but if there was, you could use the existing structure as a model and make the amino acid changes seen in the O104:H4 strain to give you a decent starting point for the structure-based design of new drugs.

 Find a pathogenic protein of interest and try this yourself... it's not too hard.  When the topic of EHEC comes up at the next party, you can impress your friends by saying you blasted several virulence factors and found them to be quite similar/different from strains of previous outbreaks.  I would do this myself but, oddly enough, I don't get invited to parties anymore.  Anyhow, as a final disclaimer... although I have tried to be careful please verify anything I have posted before use.

Query  1       GTTGAGGATGAATATAACTAATTGGATCATATATAATCTTTCTTAGGGCAAGATTCATAA 

               |||||||||||||||||||||||||| |||||||||||||||||||||||||||||||||

Sbjct  443403  GTTGAGGATGAATATAACTAATTGGAGCATATATAATCTTTCTTAGGGCAAGATTCATAA 

Query  61      CGCTCTCATATGTCTACTTAATTTTCAACCTGACTAAATTAGTTAGAATGGCCCTATACT 

               || |||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Sbjct  443343  CGTTCTCATATGTCTACTTAATTTTCAACCTGACTAAATTAGTTAGAATGGCCCTATACT  443284

Query  121     TCCATAACAGCCAGCAAGTCGCTACGGATATTAATGCAAGTAAGATAGAAACCGGCATAG 

               ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Sbjct  443283  TCCATAACAGCCAGCAAGTCGCTACGGATATTAATGCAAGTAAGATAGAAACCGGCATAG  443224

Query  181     CCTTATCATAAGCAAAAACAGGTTCGCTAATTTCATATGTTGGTGCTTGCTCAATAATGT 

               ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Sbjct  443223  CCTTATCATAAGCAAAAACAGGTTCGCTAATTTCATATGTTGGTGCTTGCTCAATAATGT  443164

Query  241     CTCTTCGTTTTGACAATACAACAGAAATATTTTCATATTGTACGCTTGCAGAGCTATTAA 

               ||||||||||||||||||||||||||||||| |||||||||||||||||| | |||||||

Sbjct  443163  CTCTTCGTTTTGACAATACAACAGAAATATTCTCATATTGTACGCTTGCAAAACTATTAA  443104

Query  301     CAATAAATCTCTTGATATCATTTATTTTTATTTCTGGGTTGATATCTTTTTCATATACTG 

               ||||||||||||| || |||||||||||||||||||| ||||||||||||||||||||||

Sbjct  443103  CAATAAATCTCTTTATGTCATTTATTTTTATTTCTGGATTGATATCTTTTTCATATACTG  443044

Query  361     CAAGTACAGAAATATGAATTGGTAAAGCAGTTTTACCACTATCGCCATTATCAACATCGT 

               ||||||||||||||||||||||||||||||||||||||||||||||| ||||||||||||

Sbjct  443043  CAAGTACAGAAATATGAATTGGTAAAGCAGTTTTACCACTATCGCCAGTATCAACATCGT  442984

Query  421     AACTAACATGTACTCTCGAAGAAATAATGCCATCCATAATTTTGAGAGATTGCTCTAACC 

               |||||||||||||||||||||||| ||| |||||||||||||||||||||||||||||||

Sbjct  442983  AACTAACATGTACTCTCGAAGAAACAATACCATCCATAATTTTGAGAGATTGCTCTAACC  442924

Query  481     GCTGCTCAATAGCAGAATATAGCCTTGCTTTTTCCGCTCGTGGAGATGAAAACGAGTGCA 

               ||||||||||||||||||||||||||||||||||||||||||||||||||| ||||||||

Sbjct  442923  GCTGCTCAATAGCAGAATATAGCCTTGCTTTTTCCGCTCGTGGAGATGAAA-CGAGTGCA  442865

Query  541     TCTGCAGGGAACATCTGCGATATTTGAATATCAGGCTTACCCGGTAGATTGTAGATTTTT 

               |||||||||||||||||||||||||||||||||||||||||||| |||||||||||||||

Sbjct  442864  TCTGCAGGGAACATCTGCGATATTTGAATATCAGGCTTACCCGGGAGATTGTAGATTTTT  442805

Query  601     AGCCAATCCACCGCAGAAGCAAAATCCGTTGGTTCGACAAATATTGAAAATCCTGTTTTG 

               ||||||||||||||||||||||||||||||||||| ||| | ||||| ||||| ||||||

Sbjct  442804  AGCCAATCCACCGCAGAAGCAAAATCCGTTGGTTCAACATAGATTGAGAATCCAGTTTTG  442745

Query  661     CCTTGATCCTTCTTTTCAGCATTAATATTATGTCTTTGTAAAACAGCAAGGACATCATTA 

               ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Sbjct  442744  CCTTGATCCTTCTTTTCAGCATTAATATTATGTCTTTGTAAAACAGCAAGGACATCATTA  442685

Query  721     GCTTGCTGTTGATCAAGATGGTTCAATAATTCCTGCTGCTTGCAGCCGCACAACAGCAGG 

               ||||||||||||||||||||||||| ||||||||||||||||||||||||||||||||||

Sbjct  442684  GCTTGCTGTTGATCAAGATGGTTCAGTAATTCCTGCTGCTTGCAGCCGCACAACAGCAGG  442625

Query  781     ATAAACAATAATA  793

               |||||||||||||

Sbjct  442624  ATAAACAATAATA  442612


Predicted amino acid sequence for O104:H4 EprK protein, (corrected for gap): 

L L F I L L L C G C K Q Q E L L N H L D Q Q Q A N D V L A V L Q R H N I N A E K K D Q G K T G F S I F V E P T D F A S A V D W L K I Y N L P G K P D I Q I S Q M F P A D A L V S S P R A E K A R L Y S A I E Q R L E Q S L K I M D G I I S S R V H V S Y D V D N G D S G K T A L P I H I S V L A V Y E K D I N P E I K I N D I K R F I V N S S A S V Q Y E N I S V V L S K R R D I I E Q A P T Y E I S E P V F A Y D K A M P V S I L L A L I S V A T C W L L W K Y R A I L T N L V R L K I K

Rapid characterization of the EHEC outbreak by “crowdsourcing”

Now we’ll be moving from papayas and fish to something a bit more sinister: EHEC O104:H4.  That is the name of the E. coli responsible for the recent German outbreak.  When a new outbreak begins sickening patients, researchers all around the world are mobilized to try and characterize the pathogen.  Since new strains often have similarity to well-understood strains, one of the critical first steps is to sequence parts the genome.  When SARS was first flaring up in Asia, I worked for a sizable biotech company focused on developing drugs for viral diseases.  Very early data suggested that the SARS virus may have a similar pathogenesis to related coronaviruses, particularly with regard to viral entry.  However, we couldn’t design drugs to combat SARS until we had the DNA sequence for that part of the genome.  Once that became available, I used our in-house analysis software to design the initial set of lead drugs and we were off and running.

                Sequencing the entire genome is extremely time consuming, but BGI (formally known as the Beijing Genomics Institute) is utilizing ‘crowdsourcing’ to help assemble the EHEC genome faster (here’s the press release).  Using open source software, Twitter feeds (@BGI_Events), and several sites for uploading data, they hope to pull together data from researchers around the world in an organized, efficient manner.   Here’s the bioproject link for this work at NCBI (link).  This exchange of data is great for biopunks because one can analyze the data almost in real time and there is a significant potential for finding interesting and important aspects of the EHEC strain, based on sequence similarities/differences with other strains.  Mike the Mad Biologist had a blog post a couple days ago that offers a glimpse of the type of analysis people are doing (link).  The more eyes there are on the data, the quicker the strain can be characterized and as I have mentioned before, the potential of using ‘citizen scientists’ or ‘crowdsourcing’ for efforts of this type are enormous.  With the advent of rapidly accessible data, and the power of on-line DNA analysis tools, the gap between the scientist and everybody else has never been smaller.

Using DNA 'barcodes' to combat fish fraud



Snapper fillets



Now that Memorial Day has passed, we tend to do a lot more grilling here in the Dark Lab.  The weather here in SoCal falls into a predictable perfection and any given evening is perfect for throwing something on the grill.  So, I head out to my local grocery store and look for a nice fish… snapper maybe?  Looking at the package, it’s definitely a fish but is it really snapper?  I can’t tell.  In fact, studies show that up to 70% of fish sold as snapper is actually something else.  The FDA tries to monitor fish but they are probably more focused on safety rather than accuracy.  However, there has been a lot of press lately about mislabeling of fish.  Last week, the New York Times ran an article (link) with some shocking statistics about how frequently fish are mislabeled.  According to a report by the non-profit group Oceana (2.3 Mb pdf here), for every three packs of fish you buy, one of them will be wrong. 

Oceana references a number of scientific studies, including a paper by Wong and Hanner (abstract), who use a PCR-based approach to analyze the DNA sequences of fish in the marketplace.  They found that some substitutions are obvious fraud.  For example, fish labeled as red snapper (sold at $3 per pound) was actually redfish (that would cost 72 cents a pound).  Fish labeled as white tuna sushi was actually tilapia.  These are flagrant mistakes, and it is not at all clear whether this is done on purpose or is the product of the complex network of processors and middle-men that are required to bring a fish out of the sea and to your dinner plate.  However, some mistakes are less apparent… for example, Atlantic halibut was labeled as Pacific halibut.  No big deal, right?  What if you knew that Atlantic halibut was endangered?  Would you still buy it? This type of mislabeling suggests some fishermen may be catching more than their quota of threatened or endangered fish and packaging them as something else.  Another recent article goes into more detail about the social and financial implications of fish fraud (abstract).

Want to know what fish you are buying?  It’s a great DIYbio project.  If your hackerspace has the ability to do DNA sequencing (or you can send sequencing samples via the hack shack) then checking your fish can be pretty easy.  You will probably want to sequence several spots in the genome and will need sequencing primers for each (which are cheap and easy to design).  Once you have the DNA sequence from your fish, you can use an online tool called Blast (link) to search the genome database for your sequence and it will tell you what species it is from.  If you already know the sequence (from the primer design, for example), then you can simply align the correct sequence with your fish’s DNA and see if you get a perfect match.  This method will give you a pretty good idea if you have the right fish as long as there are differences in the DNA sequence between the various species.  Sometimes, they can be very similar.

If you have access to a hackerspace with a PCR machine (and the reagents!) and a way to run an agarose (DNA) gel, there are several other options.  You can do an AFLP analysis, which is a very sensitive way to look for polymorphisms (changes) in DNA.  A recent paper by Maldini (abstract) outlines the approach and applies it to identifying fish.  They claim that even closely related fish can be identified.  Another PCR-based option is to amplify a gene using a species-specific primer.  In this case, you see good amplification (ie, a band on a gel) only when the DNA of that species is present.  Two advantages with the PCR approach are that you don’t need much DNA and it doesn’t need to be all that pure (both are big advantages for the biohacker). One thing you will need is a set of PCR primers for the species of fish you are buying.  I hope that someday these will also be readily available in any decent hackerspace, but until then, you will have to get them yourself.  The Wong and Hammer paper has some primers listed and primers for key genes from the most common types of market fish are freely available on the internet.  If they can’t be found directly, you can also design them from the fish’s genome.  Genbank (link) has some of this information but another good source is the website for the Fish Barcode of Life (link).  This great organization is trying to catalog all fish, including those we eat.  Eventually, they will have links to the genome of every fish so you can use that for primer design.  As an added DIYbio bonus, they are also looking for additional data from people like YOU!  Not with the DNA sequencing, but with the development of range maps that indicate where the different species of fish are found.  This is a great opportunity for all you fishermen out there (go here to see how you can report a sighting).  It’s also a way for biopunks to make important contributions to this effort while doing a little home-based food surveillance. 

So, did you notice?  The snapper picture is mislabeled... it's actually tilapia. At least you didn't pay 10 bucks for this blog post.

Biopunks in USA Today

There’s a pretty nice blurb on the DIYbio movement in today’s USA Today.  It covers the basics and highlights a few of the controversies but doesn’t describe many practical applications.  It touches on DNA sequencing and open access to lab equipment and basic molecular biology tools (expression vectors and strains, common reagents, etc) and hints at one cool application (the blue yogurt).  Nice plug for BioCurious (a hackerspace in the Bay area) and the OpenPCR machine.  They even talk about the risk of making “unstoppable Franken-microbes”.  Not that we would ever do such a thing in the Dark Lab, but I do know of three fictional teen biopunks who had a basement experiment go horribly wrong…

You can read the USA Today article here but you’ll have to wait awhile to read what happened to the teens.

What's in YOUR papaya?



Papaya: The fruit with it's
own symposium



Some people out there are concerned over whether the food they are eating has been genetically modified (GM).  We don’t worry much about that in the Lab since there is nothing even remotely genetic about Twinkies or Mountain Dew.  Still, I mentioned before that a hackerspace was a great place to do some food surveillance.  During my research into traditional methods (PCR detection, etc), I ran across an article published a few years ago that described a really simple method to test for GM Papayas.  If you are a big papaya eater, this could be important, but the method could also be more general.

Papaya are susceptible to a nasty virus called Papaya Ringspot Virus (PRSV) which creates havoc in the fields.  Long ago, scientists found that by introducing a gene for the coat protein of this virus into the papaya genome, they could block the virus from attacking the plant.  However, another gene comes along for the ride – namely, a gene used for genetic selection in the lab.  When scientists introduce a gene into a plant cell, they often use a reporter to show them which clones successfully received the gene and which do not.  This marker is an enzyme called beta-glucuronidase (GUS).  What makes this such a good indicator of a GM papaya is that non-transgenic papaya do not have this enzyme, so if you can detect this in your fruit, it has to be transgenic.  One way to this, of course, is to use DNA primers specific for GUS and use PCR to try and amplify the DNA specific for the enzyme.  You could also try to sequence the DNA extracted from the papaya and look for the sequence for GUS (or any other inserted gene, such those for the PRSV coat proteins).  The complete genome of one type of GM papaya (the SunUp variety) was published in 2008 (Nature abstract) and is freely available at GenBank (link).  Others have also been subsequently published.



X-Gluc: A GUS substrate



Fortunately, for papaya lovers, there is an easier way.  5-bromo-4-chloro-3-indoyl glucuronide (X-Gluc) is a substrate for GUS and gets converted to glucuronic acid and a precipitate which happens to be blue.  Therefore, a very easy way to look for the presence of the enzyme is to screen directly with X-Gluc.  You don’t even have to purify DNA!  Simply take the seeds from the fruit of interest and smash them up really well.  Then incubate them in the presence of X-Gluc for about a day and if you see blue, you’ve got a GM papaya!  Here is a link to a recent article that describes the general idea (abstract).  In principle, if your hackerspace has access to X-Gluc, testing for the presence of GM papaya is extremely easy.  Scientists have used this same method to track cross-pollination between GM plants and non-transgenic neighbors.  One example was reported at the International Symposium on Papaya (abstract).  Yes, a papaya conference… and I thought I got a lot of grief for doing phage display.  Anyhow, I’ll have another post or two about this type of food monitoring and pretty soon you’ll be just like the USDA – only without the bureaucracy and bad jokes.

Backyard Biotechs: One step up from the hacker



A chemical that catches iron



I’ve had several readers comment to me something along these lines: “Yeah, it would be really cool to discover a drug or something, but Merck isn’t going to develop a drug from a hacker.”  Maybe not, but check out this article from the June issue of The Atlantic describing a couple of California biotechs who are taking the virtual approach to drug development (link).  FerroKin is developing an orally available iron chelator (a chemical that traps and removes iron) as a treatment for iron overload in patients requiring recurrent blood transfusions.  The need for this type of drug is pretty high in this patient population but the overall market size is small.  Big Pharma won’t touch something like this because it wouldn’t be worth it to them.  However, to a small, virtual biotech this represents a significant opportunity.  Synthesizing iron chelators requires some sophisticated chemical equipment but I think it could be done relatively easily in a chemical hackerspace by experienced chemists (I would probably blow up half the lab trying to get the hot water on).  Furthermore, designing these drugs is fairly straightforward as well.  The iron that needs to be bound is in the blood (this makes it WAY easier since the drug doesn’t have to go into cells) and the rules about metal chelation are pretty well understood.  The odds of finding compounds for lead development are quite good.  FerroKin (link) raised some venture capital money to do preclinical and early clinical trials (all outsourced to contract research companies so FerroKin didn’t do any of it on their own) and the results so far appear promising.

I could definitely see this happening more frequently.  The process might even be similar to trying to publish a young adult novel about some biopunks.  Something gets tested at a hackerspace and looks promising.  After the intellectual property rights are secured, the inventor shops it around to ‘agents’ who are actively looking for scientific projects of that type.  Agents sign the investor and then pitch the package to virtual biotechs for possible development.  It won’t be easy at all as the bar will be very high, but a similar process is happening right now at Big Pharma (except that the projects are coming from academic labs and not amateur scientists).  I look forward to the day when all the starving writers trying to write The Next Great Novel will be fighting for sofa space at the coffee shop with the starving drug hunters trying to design The Next Big Drug.

Hack-shacks, biopunks, and the next revolution

Imagine this is 1976 (try to block out the bell-bottoms and disco music), and I wrote a blog about a couple of pimple-faced teens who had built this swell micro-computer in their garage.  They put the thing in a box and bring it to a local do-it-yourself computing club where a bunch of other computer junkies say things like “that’s totally rad, man”.  Yup, they’re computer hackers and two guys like this went on to start Apple Computer.  For all of us, life was never the same again.  Now fast-forward 35 years. 

The iPhone's great-great-grandad

The DIY biotechnology (ie, biohacking) movement has been growing for a few years but is still very much in its infancy. I remember going to a local community college with my dad and seeing him load the punched card machine to get a computer the size of a room do a routine calculation.  I could probably do that on my cell phone now.  The concept of a world wide web with a blogging program that could transmit my drivel around the world instantly was so far beyond what was possible at that time.  We are currently in the “computers as big as a room” phase with biohacking.  Doing projects is cumbersome.  Some ideas are still beyond the scope of current technology.  But it will not be long before the biological equivalent of the PC will be developed and it will rock the world.

Will biopunks cure cancer? Unlikely. But the discovery of new genetic targets for attacking cancer or infectious diseases is a good niche for garage drug hunters.  Most of the protein engineering work I do could be done at home with free software on the web, so the theoretical design of biologic drugs is also likely within reach.  Another likely application is the development of diagnostic kits.  With the big push into personalized medicine, I see huge opportunities there.  A biohacker can search the literature for biomarkers for a particular disease, or even try to find them on their own.  A few proof-of-concept experiments done in a well-equipped hack-shack (more on these in a future post) and you could have a prototype diagnostic kit.  Would you spend a couple hundred bucks for a toaster-sized cube that monitors the detailed health profile of your family?  I would.  Exposure to a cold virus? You know it on Day 1. Infection brewing?  You know about it before it even gets sore, plus you also know what type of infection you have and the resistance profile.  Want to make sure you’re your food is all-natural and pathogen-free?  No problem.  Monitor metabolic pathways for signs of imbalance (very early indicators of a number of diseases)?  We need to learn more about these pathways, but in principle this is also very possible.  A primitive version of this type of device could probably be made today.

The OpenPCR machine: The great-great grandad of ????

How about less practical gadgets like the iPod?  Designer plants?  Plants with leaves that glow in the dark?  Well within reach.  Carrots that taste like cotton candy? Probably doable.  How about genetic genealogy?  How fun would it be to try and find DNA samples from long-lost relatives (hair strands?) and map genetic contributions from them.  Kids might even be into collecting genes.  Pick up a leaf or a feather or a dead bug and process the DNA.  Compare genes, find rare and unusual enzymes, make trading cards.  It could be bigger than Pokemon!  Ok, maybe not, but still, somebody out there is going develop these things and the next Apple Computer is going to be born.  Check out the OpenPCR that is being developed by a couple of early biohackers (link)… could this be Apple 1?

Someday I’ll be drinking a glass of cab and telling my granddaughter that back in the old days, I had a whole group of scientists with a lab full of expensive equipment trying to identify and target genes. And it took years. Blah, blah, blah… and she’ll wander off and play with the genetically engineered mouse she made from a kit under the soft light of a bioluminescent tree.