To improve, my idea would be to get participants to construct a short genome out of Lego pieces that can be truly “sequenced” by pushing it through some sort of colour sensor or camera apparatus attached to an Arduino inside a future iteration of the trusty SAMTECH Sequencer range.

So I’d like to introduce the Sam and Tom Industrys Legogen Sequenceer(TM) 9001:

The Sequencer

The Lego “Sequenceer” has a tray that holds a small stack of Lego bricks (up to 12). Once loaded, a toothed side to the tray is pulled along a track with a gear attached to a stepper motor that was commandeered from an old Epson printer. An RGB colour sensor sits just over the track on a small acrylic bridge, in very close proximity to the passing bricks. The stepper is programmed to actuate a calibrated number of steps per brick, stopping to allow the RGB sensor to take a reading of a brick’s colour before moving to the next.

Our software then translates these readings to one of the four DNA bases and prints the result on the serial port for the user to see. Once complete, the stepper mode is reversed and the tray is “ejected” such that it protrudes once more out the front of the machine to allow for unloading and loading of the next Lego brick DNA sequence.

The Activity

My previous activity tasked pupils to align pre-constructed Lego DNA sequences to each-other. The goal was to introduce the idea of short read sequencing and how alignment of a short sequence is hard, let alone that of an unexpectedly-long human genome (highest guesses for base pair length were in the thousands). In general, the students enjoyed, but I felt I could massively improve the task if we used Lego for its intended purpose: construction.

This year, after quizzing pupils on what they know about DNA, I once again introduce the concept of using 2×2 blocks of Lego as DNA bases. We have four colours, matching the four bases of DNA we see in our own genomes. I explain that today we will be construct a 10 base DNA sequence, representing the entire genome of a monster, and that we can find out what that genome is with our sequencer.

I designed some official looking forms onto which the pupils could copy out the base calls for each of the ten bases on their monster’s genome, which all happen to be a gene that controls an interesting phenotype:

Last minute prep for my Lego sequencing stand at @AberUni #AberSciWeek #BSW17 pic.twitter.com/TAaI8L8zdZ

— Sam Nicholls (@samstudio8) March 15, 2017

Our young scientists then look at our “monster datasheet”, that decodes each of the genes and what the phenotype for each of the alleles will be:

@ibers_aber @AberCompSci Our monster gene list! pic.twitter.com/s7EK9EMdZQ

— Sam Nicholls (@samstudio8) March 15, 2017

Now given their sequenced monster genome, and the datasheet, we ask the pupils to make their monster’s phenotype a reality – draw their monster to add to our records! The hardest part? Naming their newly discovered monster. Finally they add their name and school, to credit them with their discovery. Here was our first of the day. I stole our department’s stamp for an additional feeling of official-ness:

Our first discovery! THE MEGA COW @samtomindustrys @AberUni @illumina @LEGOeducationUK @AberCompSci @ibers_aber pic.twitter.com/PNadYFpvS0

— Sam Nicholls (@samstudio8) March 15, 2017

Conclusion

I’m so pleased with how this project has turned out. Not only is the sequencer itself the coolest electronics project I’ve had a hand in, but the reaction to the stand on the day from children and adults alike was so positive. The pupils loved the concept: getting to build something with Lego, and then drawing a cool monster that has special powers is clearly a recipe for success. It is rewarding to see that they’ve enjoyed the activity, as well as to know they can now tell you something about DNA and phenotypes too.

This is a significant improvement on my stand from last year. We get to look at bases and DNA, sequencing and importantly, how changes in bases correspond to changes in a phenotype: which makes us who we are. The activity tells much more of a story, rather than just aligning Lego bricks to explain that a hard problem is indeed hard. Here, we get to physically construct a tangible proxy to a genome and find out something about it through an experiment. Finally we add the result to the sum of knowledge of the subject of monsters discovered so far. My wall is now full of unique and interesting monster records, each drawn with some artistic license from the scientist in charge. Though, despite the large sequence space, I was amused to note the bias for 8 legged and 8 eyed monsters due to an environmental pressure for the selection of the T nucleotide: the colour blue.

Once again, I’ve had much more fun than my regular grumpy-self was expecting. I’ve been reminded that public engagement can be very rewarding.

Engage engagement

Some pictures of the stand from the day:

The @samtomindustrys Lego Sequencer is up and running at @AberUni #abersciweek #bsw17 @illumina @LEGOeducationUK @AberCompSci @ibers_aber pic.twitter.com/iQtOqBoOk7

— Sam Nicholls (@samstudio8) March 15, 2017

Some pictures from my Lego Sequencer stand at @AberUni's Science Week today! #BSW17 pic.twitter.com/YLmk83oLLO

— Sam Nicholls (@samstudio8) March 15, 2017

Finally, here are the images from our inaugural Monster Lab:

Monster Lab: Aber Uni Science Week 2017

tl;dr

• Tom and I upgraded the old SAMTECH 9000 to create the Arduino-powered self-scanning Legogen Sequenceer 9001
• Spending the entire day with children continues to be absolutely exhausting
• Public engagement continues to be rewarded
• Lego is an excellent way to introduce the concept of DNA, and genomes
• Blue is a particularly popular favourite colour
• Primary school children aren’t as dumb as everyone says they are
• Packs of genuine 2×2 Lego bricks are really bloody expensive

En route to @UoB_CCB to shout about why metagenomes are so hard pic.twitter.com/wftx6sUhWV

— Sam Nicholls (@samstudio8) November 2, 2016

Metaphorical Metagenomes

I submitted the current working title of my PhD: “Extracting exciting exploitable enzymes from massive metagenomes“, which I think is a rather catchy summary of what I’m working on here. I borrowed the opening slides from my previous talks (this is a cow…) but felt like I needed to try a new explanation of why the metagenome is so difficult to work with. Previously, I’ve described the problem with jigsaw puzzles: i.e. consider many distinct (but visually similar) jigsaws, mixed together (with duplicate and missing pieces). Whilst this is a nice, accessible description that appears to serve well, it tends to leave some listeners confused about my objective, particularly:

• You are recovering whole genomes?
  The jigsaw metaphor doesn’t lend well to the metahaplome and the concept of assembling a jigsaw partially. Listeners assume we want to piece together each of the different jigsaws in our box, whole – presumably because people find those who don’t finish jigsaws terrible.
• We can assemble jigsaws incorrectly?
  Metagenomic assemblies are a consensus sequence of the observed reads. The resulting sequence is unlikely to exist in nature. Whilst we can extend our metaphor to explain that pieces of jigsaws may have the same shape, such that we can put together puzzles that don’t exist, this is not immediately obvious to listeners.

A common analogy for genomic assembly is that of pages shredded from a book. I’ve also previously pitched this at a talk to try and explain metagenomic assembly, but this has some disastrous metaphorical pitfalls too:

• You are recovering whole books?
  Akin to the jigsaw analogy, listeners don’t immediately see why we would only want to assemble parts of a book. What part? A chapter? A page? A paragraph? Which leads to…
• Why are there paragraphs shared between books?
  To describe our problem of recovering genes that appear across multiple species, we must say that we are attempting to recover some shared sequence of words from across many books. This somewhat breaks the metaphor as this isn’t a problem that exists, and so the concept just causes listener confusion, rather than helping them to understand our problem. Whilst we could point out the Bible as an example of a book that has been translated and shared to a point where two copies of the text do feature differences between their passages, we figure it best to avoid conversations about the Bible and shredded books.
• You are assembling words into sentences? The problem is easy?
  DNA has a limited alphabet: A, C, G and T. But books can contain a practically infinite combination of character sequences given their larger alphabets. This larger alphabet makes distinguishing sequence similarity incredibly simple compared to that of DNA. Right now I’m using an alphabet of about 95 characters (upper and lowercase characters, numbers and a subset of symbols) in this post, and although it’s possible that one or more of my sentences could appear elsewhere on the web (unintentionally), the probability of this will be many, many orders of magnitude smaller than that of finding duplication of DNA sequences within and between genomes. Thus by comparing the problem to reconstructing pages from a book, we are at a very real risk of underselling the difficulty of the problem at hand.

Additionally, both analogies fail to explain:

• Intra-object variation
  We must also shoehorn the concept of intraspecies gene variation into these metaphors which turns out rather clunky. We do say that books and jigsaws have misprints and errors, but this doesn’t truly emphasise that there is real variation between instances of the same object.
• What is the biological significance anyway?
  Neither description of the problem comes close to explaining why we’d even want to retrieve the different but similar-looking areas of a jigsaw, or copies of a page or passage shared across multiple books.

Machines and Factories: A new metaphor?

So, I spent some time iterating over many ideas and came up with a new concept of “genes as machines” and “genomes as factories”:

Genes

Consider a gene as a physical machine. It’s configuration is set by altering the state of its switches. The configuration of a machine is akin to a sequence of DNA. It is possible (and even intended) that the machine can be configured in many different ways by changing the state of its switches (like gene variants), but it is still the same machine (the same gene). This is an important concept because we want to describe that a machine can have many configurations (that can do nothing, improve performance, or even break it), whilst still remaining the same machine (i.e. a variant of a gene).

Factories

We can consider a genome as a factory, holding a collection of machines and their configurations:

We can extend this metaphor to groups of factories under a parent organisation (i.e. a species) who can set the configuration of their machines autonomously – introducing intra-species variation as a concept. Additionally we can describe groups of factories under other parent organisations (species) that also deploy the same machine to their own factories, also configuring them differently – introducing not only intra-species variation, but multiple sets of intra-species variants too:

Talk the Talk

Armed with my shiny diagrams and apprehension of my own new metaphor, I pitched it to my listeners as a test and thanked them for their role as guinea pigs to my new attempt at explaining the core concept of the metagenome and its problems.

PREPARE YOURSELVES pic.twitter.com/s1fcccXNgQ

— Sam Nicholls (@samstudio8) November 2, 2016

In general, I felt like the audience followed along with the metaphor to begin with. Given a fictional company: Enzytech and their machine: Awesomease, we could define the metahaplome as the collection of configurations of that Enzytech Awesomease product across multiple factories, under various parent companes (i.e. different genomes, of varying species). However I think the story unravelled when I described the process of actually recovering the metahaplome.

I set the scene: Sam from Enzytech wondered why factories configured their Awesomease differently. Sam figured there must be an interesting meaning to these configurations – do some combinations of switches cause the Awesomease to -ase more awesomely? Thus, Sam approaches each parent company and requests their Enzytech Awesomease configurations. In a surprising gesture of co-operation, the businesses comply and return all their Enzytech Awesomease configurations, for all of their factories. Unfortunately, and perhaps in breach of their own trade secrets, they also submit the configurations of every other machine (gene) in each of their factories (genomes) too:

To make matters worse, the configurations don’t describe the specific factory they are from (i.e. the individual organism), and their returned documents also include incomplete, broken and duplicated configurations. Lost configurations are not submitted.

I think at this point, I was getting too wrapped up in the metaphor and its story. The concept of metaphorical factories submitting bad paperwork to fictional Sam from Enzytech did not have an obvious biological reference point (it was supposed to describe metagenomic sampling). I think with practice, I could deliver this part better such that my audience understands the relevance to biology, but I am not sure it is necessary. Where things definitely did not work was this slide:

“Unfortunately, an Enzytech intern misfiled the paperwork submitted by all of the parent companies’ factories (species and their various genomes), and we could no longer determine which company submitted which configuration. The same clumsy intern then accidentally shredded all of the configurations, too.”

Welp. I am somewhat cringing at the amount of biological shoehorning going on in just one slide. Here I wanted to explain that although my pretty pictures have helpful colour coding for each of the companies (species), we don’t have this in our data. A configuration (gene variant) could come from any factory (genome) in our sample, and there is no way of differentiating them. Although shredding is a (common) reference to short-read sequencing technology, the delivery of this slide feels as clumsy as the Enzytech intern. I think the mistake I have made here was trying to use the same metaphorical explanation for two separate and distinct problems that I face in my work on metagenomes:

• The metahaplome
  We need to clearly define what the metahaplome actually is as it is a term we coined. It is also the objective of my algorithm, and so failing to adequately describe this means it is unclear why this work has a biological relevance (or is worth a PhD).
• Metagenomes, assembly, and short read sequencing
  This final slide attempts to describe metagenomes and sequencing, as shredded paperwork relating to many different genes, from multiple factories that are held by various parent companies, all mixed together. But in fact, for this part of the metaphor it is easier to just say “bits of DNA, from a gene, on multiple organisms, from multiple species in the same environmental sample”…

On this occasion, I believe I managed to explain the metahaplome more clearly to an audience than ever before, though this might be in part because this is my first talk since our pre-print. However, in forcing my new metaphor onto the latter problem (of sequencing), I think I inadvertently convoluted what the metagenome is. So ultimately, I’m not entirely convinced the new metaphor panned out with a mixed audience of expert computer scientists and biologists. That said, I had several excellent questions following the talk, that seemed to imply a deep understanding of the work I presented, so hooray! Regardless of whether I deploy it for my next talk, I think it will still prove a nice way to explain my work to the public at large (who may have no frame of reference to get confused with).

I enjoyed the opportunity to visit somewhere new and speak about my work, especially as my first invited talk outside of Aberystwyth. This is also a reminder that even sharing thoughts and progress on cross-discipline work is hard. It’s a lot of work to come up with a way to get everyone in the audience on the same page; capable of speaking the same language (biological, mathematical and computational terminology) and also give the necessary background knowledge (genomic sequencing and metagenomes) to even begin to pitch the novelty and importance of our own work.

Obligatory proof that people attended:

pic.twitter.com/FFUM8TsLq6

— CCB UoB (@UoB_CCB) November 2, 2016

Obligatory omg my heart rate:

tl;dr

• I was invited to speak at Birmingham, it was nice
• It’s super hard to come up with explanations of your work that will please everyone
• Spending until 4am drawing some rather shiny diagrams is perhaps not the best reason to push forth with a new metaphor that even you feel a little uneasy about
• I continue to speak too bloody quickly
• My body still gives the physiological impression I am doing exercise whilst speaking publicly

How to explain assembly?

I was inspired by Amanda’s activity for prospective students at a visiting day a few weeks prior. To describe the problem of DNA sequence assembly and alignment in a friendly (and quick) way, Amanda had hundreds of small pieces of paper representing DNA reads. The read set was generated with Titus Brown’s shotgunator tool, slicing a few sentences about the problem (meta!) into k-mers, with a few errors and omissions for good measure. Visitors were asked to help us assemble the original sequence (the sentences) by exploiting the overlaps between reads.

I like this activity as it gives a reasonable intuition for how assembly of genomes works, using just scraps of paper. Key is that the DNA is abstracted into something more tangible to newcomers – English words building sentences – which is far simpler to explain and understand, especially in a short time. It’s also quite easy to describe some of the more complicated issues of assembly, namely errors and repeats via misspellings and repeated words or phrases.

You too can experience life as a genome assembler today at the @AberCompSci visit day! #AberVisitDay pic.twitter.com/iZtBC0rSbi

— Sam Nicholls (@samstudio8) February 10, 2016

A problem with pigeonholing college students?

Yet to my surprise, the majority of the compscis-to-be were quite apprehensive of taking on the task at the mere mention of this being a biological problem, despite the fact that sequence alignment can be easily framed as a text manipulation problem. Their apprehension only increased when introduced to Amanda’s genome game; a fun web-based game that generates a small population with a short binary genome whose rules must be guessed before the time runs out. A few puzzled visitors offered various flavours of “…but I’m not here to do biology!”, and one participant backed out of playing with “…but biology is scary and too hard!”. In general the activities had a reasonable reception but visitors appeared more interested in the Arduinos, web games and robots – their comfort zone, presumably.

One need not necessarily be an expert in biology (I’m certainly not) to be able to contribute to the study of computationally framed questions in that field. As mentioned, DNA alignment is effectively string manipulation and those strings could be anything! Indeed this is even demonstrated by our activity using English sentences rather than the alphabet ACGT.

From experience, undergraduates (and apparently college students) appear keen to pigeonhole themselves early (“…dammit Jim I’m a computer scientist not a bioinformatician”) via their prior beliefs to the meaning of “computing”, and their module/A-level choices. I think it is at this stage where subjects outside one’s choices become “scary” and fall outside one’s scope of interest — “…if I wanted to learn biology why would I be doing compsci?”. Yet most jobs from finance to game development will require some domain specific knowledge and reading outside computing, whether its economics, physics or even art and soundscape design.

This is why it is important as a computer science department that we introduce undergraduates to other potential applications of the field. It’s not that we should push students to study bioinformatics over robotics, but that many students can easily go on unaware that computing can be widely applicable to research endeavours in different fields in the first place. Though to combat the “this is not my area” issue, in our department, many assignments have a real-world element, often just tidbits of domain specific knowledge that force students to recognise the need for base understanding of something outside of their comfort zone.

Lego: a unicorn-like universal engagement tool

College students aside, I needed to work out how to engage schoolchildren between the ages of 10-12 with this activity. Scraps of paper would be unlikely to hold the attention of my target age group for long. I needed something more tangible and less fiddly than strips of paper. It was while describing the problem of introducing these “building blocks of nature” to kids in a simple way when the perfect metaphor popped into mind: Lego.

Yes! A 2×2 brick can represent an individual nucleotide, and we can use different coloured bricks to colour code the four nucleotides (and maybe another for “missing” if we’re feeling mean). A small stack of bricks builds a short string of DNA to represent a read. The colour code effectively abstracts away the potentially-confusing ACGT alphabet, making the alignment game easier to play (matching just colours, rather than symbols that need parsing first) and also quite aesthetically pleasing.

The hard part, was sourcing enough Lego. I returned to my parents’ home to dig through my childhood and retrieve years worth of collected pieces, but once back in Aberystwyth I was surprised to find that after sorting through two whole boxes I did not own more than some 100 2×2 bricks (and most were not in colours I wanted). Bricks, it appears, are actually quite hard to come by! I put out a request for help on the Aber Comp Sci Facebook group and a lecturer kindly performed the same sort with his children’s collections. Their collection must have been more substantial and yielded 150-200 bricks in a mix of four colours, saving my stand.

The setup

The activity itself is simple and needs nothing other than some patter, the Lego and a surface for kids to align the pieces on. I spent more time than I would like to admit covering a cardboard box with tinfoil to create the SAMTECH SEQUENCER 9000 (described by Illumina as “shiny”), a prop to contextualise the problem: we can’t look at whole genomes, only short pieces of it that need assembly.

Of course, we’d need some read sets. To make these, I divided the available bricks into two piles, Nathan and I then each ad-libbed sliding k-mers of length 5 (i.e. each stack would have stacks with overlaps of length 4, 3, 2 and 1 coloured brick – which each had their own overlaps…) to build up an arbitrary genome to recover. Simple!

Running the activity

Once doors opened, there was no shortage of children wanting to try out the stand. I think the mystery of the tinfoil box and the allure of playing with Lego was enough to grab attention, though Nathan (my lovely assistant) and I would flag down passers-by if the table was free. Pupils were encouraged to visit as many activities as possible by means of a questionnaire, on which each stand posed a scientific question that could be answered by completing that particular stand’s activity. Unfortunately for us, our stand’s question was not included on the questionnaire (I guess we submitted it too late) but luckily, we found pupils were keen to write down and find an answer to our “bonus question” after all.

We quickly developed a double-act routine; opening by quizzing our aligners on what they knew about DNA, which was typically not much, though it was nice to hear that the majority were aware that “it’s inside us”. Interestingly, of the pupils who responded in the positive to being asked what DNA was, their exposure was primarily from television – specifically when used for identification of criminals. Nathan would then explain that if we wanted to look at somebody’s DNA, we would take a sample from them and process it with the shiny tinfoil sequencer. This special machine would apply some magic science and produce short DNA reads that had to be pieced back together to recover the whole genome.

At this point we’d invite participants to open the lid of the sequencer and take out a batch of reads (of a possible two sets) for assembly. We’d explain the rules and show some examples of a correct alignment: sequences of matching runs of colour between two or more Lego stacks. Once they got the hang of it, we’d leave them to it for a little while. The two sets meant that we could split larger groups into pairs or triplets to ensure that everybody had a chance to make some successful alignments.

As the teams came to finishing alignment of the most obvious motifs (Nathan and I both accidentally made a few triplets of colours that resembled well known flags in our read sets – which was handy), progress would begin to slow and a few more difficult or red-herring reads would be left over, and Nathan or I would start narrating the problem, asking teams if this had been more difficult than expected. I don’t think any team agreed that the activity had been easy! We used this as an opportunity to interrupt the game to frame how complicated assembly is for real sequences and reveal the answer to our question.

The debrief

This was my favourite part, I’d hold up one of the Lego stacks and pull it apart. “Each of these bricks is a single base, stacked together they make this read which tells us a what a small part of a much longer genome looks like”. I’d then ask how long they imagine a whole human genome might be. Answers most frequently ranged between 100 – 1000, a minority guessed between 4 – 15. No pupil ventured guesses beyond a million. For the very small guesses, I’d assemble a Lego stack of that length and ask if they still thought the differences between us all could be explained by such a short genome – nobody changed their mind³.

The look on their faces when I revealed it was actually three billion made the entire activity worth it. If we had enough Lego to build a genome, it would be 28,800km tall and stretch into space far beyond where global positioning satellites are in orbit. I’d explain that when we do this for real, the stacks aren’t five bases long, but more like a hundred, and instead of the handful of reads we had in our tinfoil sequencer, there were millions of reads to align and assemble. They’d gasp and look around at each-other’s faces, equally stunned. We even had some teachers dumbfounded by this reveal. “This is why computers are now so important in biology, this would be impossible otherwise!”. We’d clear up any last questions or confusions and thank them for playing.

Some observations

I would not consider our first group a rallying success. I was not ready for how difficult assembly of a set of unique 5-mers would be. The group had significant trouble recovering the genome and as it turned out, Nathan and I did too. The situation had not been helped by the fact that the group had also taken a mix of reads from both batches in the tinfoil sequencer. As it turns out, even trivial assembly is really hard. I could tell the kids were somewhat disappointed and the difficulty of the game had hampered their enjoyment. We recovered by wowing them with facts about the human genome and they asked some good questions too. Once they left the table, Nathan began the patter with the next group as I hurriedly worked to reduce the number of red-herring reads and recycle the bricks to create duplicate reads which allowed groups to make progress more quickly at the beginning (and effectively turned difficulty into a ramp, rather than uniformly hard to play). This improved further games considerably.

I was surprised how happily the pupils were to append our fairly long question to an already quite lengthy questionnaire, and how keen they were to find the answer, too. Not a single pupil was put off from our activity at the mention of biology, DNA or even unfamiliar terminology like “sequencer”, or “read”. Fascinatingly, Amanda also ran the aforementioned genome game and it was a hit. I guess primary school students are just open to a very wide definition of science and are yet to pigeonhole themselves? Activities like this at an early age have the potential to massively influence how our next generation of scientists see science as a large collaborative effort, skills can be transferred and shared to solve important and interesting questions. The pupils simply had no idea that computers could be used like this, for science, let alone biologically inspired questions.

In general the activity went down very well, the kids seem to get the concept very quickly and also understood the (albeit naive) parallel to DNA. I think they genuinely learned a thing or two (the human genome is big!) and enjoyed themselves. I’m pleased that we managed to draw and keep attention to our stand, given we were wedged between a bunch of old Atari consoles and a display of unmanned aerial vehicles.

I was definitely surprised at how much I enjoyed running the stand too. I’m not overly fond of children and was expecting to have to put on a brave face to deal with tiny disinterested people in assorted bright sweaters all day. Yet all but one or two pupils were happy to be here, incredibly enthusiastic to learn, asked great questions (sometimes incredibly insightful questions) and genuinely had a nice time and thanked us for it. Enjoyment aside, I took the second day off as I’d also found running the activity over and over, oddly draining.

Future activities

If I were to run this again, I’d like to make it a little more interactive and ideally give players a chance to actually use Lego for its intended purpose: building something. Thankfully at our stand, students were not particularly disappointed when our rules stated that couldn’t take the reads apart, or put them together (i.e. couldn’t actually play with the Lego…). To improve, my idea would be to get participants to construct a short genome out of Lego pieces that can be truly “sequenced” by pushing it through some sort of colour sensor or camera apparatus attached to an Arduino inside a future iteration of the trusty SAMTECH Sequencer range. Some trivial software would then give the player some sort of monster to name⁴, print off and call their own.

To run the activity again in its current form, I think I’d need to have more Lego. However, it turns out that packs of 2×2 bricks in one colour are widely available on eBay and Amazon, though aren’t actually that much cheaper than ordering via the “Pick a Brick” service on the canonical Lego website. I’ve ordered a few packs (at an astonishing £0.12 per brick) as I would like to try and run this activity at other events to spread the sheer joy that bioinformatics can bring to one’s afternoon.

To give the current version of the game a little more of a goal, it would have been ideal to explain the concept of a genomic reference and have the players align the reads to that (as well as eachother), in effect this would have been like solving the edges of a jigsaw and given a sense of quick progress (which means fun) and also afford us the opportunity to explain more of the “real science” behind the game. To make the game more difficult, we could have properly employed “missing bases” and the common issues that plague assembly including repeats (which is easier to explain with a reference), as well as errors. After the first group at the Science Fair, I quickly removed the majority of sneaky errors as it made the game too “mean” (where Nathan or I had to explain “No that one doesn’t go there!” too frequently).

Some proof what I did public engagement⁵

The @AberCompSci exhibits at science week. Robots, holograms, drones and … Lego? (With @samstudio8) pic.twitter.com/adWOjcQ0yf

— Hannah Dee (@handee) March 15, 2016

DNA alignment explained by @SirSharpest with the aid of @samstudio8‘s shiny sequencer. #BSW16 pic.twitter.com/8a7FTXFSwo

— Aber Uni Comp. Sci. (@AberCompSci) March 15, 2016

tl;dr

• Actual Lego bricks are hard to come by (unless you just buy them)
• Typical ten year olds are not as dumb or as apathetic to science as one might expect
• Assembly is actually pretty hard
• Engaging with children with science is exhausting but surprisingly rewarding
• Acquire more Lego
• It’s very hard to tinfoil a cardboard box nicely