Sweetie DNA and Schoolkids: Genes and DNA for Year 3s

I volunteer with the STEM Ambassador programme in the north of England, and in preparation for a talk / hands-on session I was giving at a local primary school last week, I went in search of visual aids for DNA. The main focus of the event at the school was helping the kids of three Year 3 classes build models of DNA out of sweets (as described in this Guardian article). Before we got stuck into the gummy bears and liquorice, I wanted to give them a short introduction to DNA. I had discovered a lovely pattern for crocheting DNA, which I followed the night before the event, which worked out great (you can see the results in my other blog post about the crocheted DNA itself). After asking them to pass the “DNA” around and take a look, I got started on my talk.

I used the slides below to give them something to look at while we chatted about DNA. Getting them to try to pronounce “Deoxyribonucleic acid” was hilarious for all of us and got them engaged in what I was saying from the start.

After giving them an introduction, I stopped at slide 7 and showed them the sweetie DNA that I had made with my son over the weekend in my best “Here’s one I prepared earlier” style. They were very excited to be using sweets to build their models – I hope they were allowed to eat them at the end of the day!

They were already sitting about 5 to a table, so we handed out enough materials that each table could make one model. The sugar phosphate backbone was strawberry liquorice with sherbet inside, and the As, Ts, Cs, and Gs were gummy bears. They all worked together really well. The gummy bears were very colorful but quite firm, so it took quite a bit of effort for the kids to push them onto the cocktail sticks / toothpicks. However, we had only one poked palm (that I was aware of) – the kids were pretty dexterous. The kids made beautiful models, and it was loads of fun helping them. They were quite keen to show us adults their handiwork, too. They were rightly proud of their sweet-based masterpieces.

Once they finished building their models, we had just a few minutes left, so I showed them slides 8 and 9, which talked about putting genes from one organism in another. I told them to imagine me tearing out a recipe for fluorescence from the jellyfish recipe book and stuffing it into a bacteria’s recipe book. Then, you could create fluorescent bacteria! Slide 9 is a picture of an agar plate of fluorescent bacterial colonies with a difference: the researchers had made a beach scene with them! So, I asked the kids to draw “bacterial colony”pictures with chalk on black paper. They loved that as well: volcanoes, cheetahs, eagles, sharks, and more. One scientific soul even drew DNA and the bacteriophage from slide 2!

The kids were engaged throughout, providing loads of good answers to my questions and asking fantastic questions themselves. I visited 3 different classrooms, and they all showed such an interest in science. 8 is a fabulous age – all curiosity and interest. Thanks very much to the lovely teachers and staff, and of course to the schoolkids; it was fun hanging out with you all! …and thanks for letting me use your pictures of my visit. Thanks also to the STEM Ambassador programme for both organizing this visit and providing the sweets!

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Adventures in Crocheted DNA

I made some crocheted DNA this week, and I was so impressed with both the free pattern I found, and the result, that I thought I should share my experiences here.

IMG_20170313_201224993 (1)

I volunteer with the STEM Ambassador programme in the north of England, and in preparation for a talk / hands-on session I was giving at a local primary school, I went in search of visual aids for talking about DNA. I was already planning to help the kids of three Year 3 classes build models of DNA out of sweets (as described in this Guardian article), but before we got stuck into the gummy bears and liquorice, I wanted to give them a short introduction to DNA. (I go into more detail about the actual presentation I gave, as well as how the sweetie DNA turned out, in my related post on Genes and DNA for Year 3s.)

So, how do you make crocheted DNA? Well, I had a vague recollection of a DNA scarf pattern that I had come across some time ago (and you can try your hand at too), but I knew that would take too long to make. Also, it didn’t really have the 3-dimensional look I was going for. The scarf is gorgeous and scientifically accurate, but it isn’t much better than a drawing or a video from the perspective of the kids; it doesn’t show them the shape of a double helix.

My Googling then took me to the Wunderkammer blog by Jessica Polka, where she had posted this free pattern for crocheted DNA. It was another happy convergence (as was true with the scarf) of science and wool. You should all visit Jessica’s blog post as it goes into detail about how, if you’re right handed, you end up with a left-handed helix for your crocheted DNA if you follow her pattern. While I appreciate chirality, I went with the simpler left-handed helix for my work this week.

Jessica, however, went the extra mile and crocheted both left handed and backwards with her right hand! I admire her dedication, but I didn’t have that kind of time. I thought it might be useful for others to see the fruits of my labor, and provide a few helpful details on the pattern for others.

Firstly, the original pattern allows you to choose your own length of DNA, which is helpful. However, I had no idea how long it would end up, so for other people looking to make this pattern, I made an initial chain of length 50. As you can see from the picture at the top of the page, the resulting length of DNA was about twice the length of a crochet hook, or about 30 cm, give or take. Your work will be longer than that if you pull it tight (as the natural double helix shape contracts the length somewhat), and shorter if you have an 8-year-old squashing it as small as she can in order to mimic how the DNA is stored in the nucleus 🙂

An important note at this stage is that the pattern is American, and if you’re used to reading UK patterns please replace any reference to “single crochet” with “double crochet”.

After you create the chain and start on the single crochets, then you start to see the single spiral / helix forming:

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It’s really quite magical, and I don’t mind saying I felt weirdly happy watching the spiral slowly (but neatly) curl behind the active part of the work. However, I didn’t really believe the second row of single crochets would work as nicely as the first – I figured some fiddling would be required. However, even the second (and final) row spiralled neatly behind the “active site” (I know, I should have been a comedian! Ha ha), as you can see from this picture, where the completed (left-hand) double helix is on the right of the image, and the incomplete single spiral on the left:

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Finally, I didn’t tidy away the ends of the yarn on either side of the completed work as it 1) allowed for a useful place to hold the DNA while twirling it, creating a pleasing spin to watch, and 2) it was just about right to tie the ends together and make a circle of DNA should you so desire!

It only took about 30 minutes (including interruptions). I gave the DNA to the school at the end of the STEM event, as the kids seemed enthralled by it. The major and minor “grooves” were clear – clear enough that I was able to point them out to 7 and 8 year olds, who were able to understand the difference. I was also able to flatten it and show its similarities with the “ladder” diagram that I had up on a slide to show them how they were going to build their sweetie DNA.

Kids playing with Crocheted DNA
Kids playing with Crocheted DNA

I made it almost as an afterthought, yet it was so beautifully tactile when held and elegant when spun that the kids really enjoyed it. One girl in particular kept on spinning and spinning it, making the 30 minutes of my effort well worthwhile. I’ll definitely be making more whenever I run a similar event in future. Perhaps I should start making a full set of human “chromosomes”? But what colors should I choose for each one? Thoughts in the comments please!

The NormSys registry for modeling standards in systems and synthetic biology

COMBINE 2016

Martin Golebiewski

http://normsys.h-its.org . NormSys covers the COMBINE standards, but they have plans to extend it to further modelling standards. Each standard has multiple versions/levels, and trying to figure out which standard you need to use can be tricky. Normsys provides a summary of each standard as well as a matrix summarizing each of the biological applications that are relevant in this community. Each standard has a detailed listing of what it supports and what it doesn’t with respect to annotation, supported math, unit support, multiscale models, and more.

There are also links to the specification and webpage for the standard as well as publications and model repository records. They also have information on how a given standard may be transformed into other standards. Information on related software is also available. Additional matrices describe what formats are available as input and output wrt format transformation.

NormSys is an information resource for community standards in systems biology. It provides a comparison of their main characteristics and features, and classifies them by fields of application (with examples). Transformation between standards is available, as well as bundled links to corresponding web resources, direct links to validation tools, and faceted browsing for searching with different criteria. The initial focus is on commonly-used standards (e.g. COMBINE) and related efforts.

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!

SED-ML support in JWS Online

COMBINE 2016

Martin Peters

In an ideal world, you should be able to reproduce any simulation published in a paper. This does happen for some models. You have a website which links the paper from JWS and data from the FAIRDOM hub. Then you can tweak the parameters of a published model and see how the results change. This means that there is a SED-ML database as part of JWS online. Once you’ve made your modifications, you can then re-submit the model back to JWS.

You can also export the COMBINE archive you’ve created in the course of this work and take it away to do more simulations locally. Currently, only time-course simulation is supported (to keep the computational time as low as possible). Excel spreadsheets are used instead of NuML. Further, there is support only for 2d plots. However, they have achieved their goal of being able to go from a paper to the actual simulation of a model from that paper in one click.

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!

The ZBIT Systems Biology Software and Web Service Collection

COMBINE 2016

Andreas Draeger

In systems biology, people want to perform dynamic simulations, steady-state analyses and others. SBML is the format to use for the model, but you also need a data structure for use in the software, and as such they developed jSBML.

People build models from KEGG, textbooks and more. They try to rebuild KEGG diagrams in CellDesigner, which is very time consuming. Is there a better way to do this? And, indeed, there are even difficulties with this manual method, as some reaction participants present when you study the record aren’t visible in the associated diagram (e.g. the addition of ATP), which can cause issues for novices. Therefore they developed KEGGtranslator to convert KEGG pathways to various file formats. Another way to add a data source to your model is through BioPAX2SBML. Additionally, they’ve created ModelPolisher which can augment models with information from the BiGG database, which is available as a command-line tool and as a web service. For dynamic simulation, they have a tool called SBMLSquezer, which generates kinetic equations automatically and also reads information from SABIO-RK.

This system was applied to all networks in KEGG. They use SBMLsimulator to run the simulations. They’ve developed a documentation system called SBML2LaTeX which helps people document their models.

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!

COMBINE 2016 Day 3: SigNetSim, A web-based framework for designing kinetic models of molecular signaling networks

COMBINE 2016

 

Vincent Noel

He was asked to develop a web tool which would be easy for biologists and students, but which could use a parallel simulated annealing algorithm and perform model reduction. He used Python to write the core library and the web interface, with some parts of the library in C. In this software, an SBML model is read in and a symbolic math model is built. It is compatible with SBML up to version of L3V1. The integration is performed using C-generated code, which can be executed in parallel. To perform integration for systems of ODEs or DAEs, the software uses the Sundials library. To perform model fitting, the software uses simulated annealing. It also has some compatibility with Jupyter, mainly to allow the symbolic math model to be able to be worked with directly.

SigNetSim’s web interface uses the Django framework with the Bootstrap front end. There is also a simple DB backend for storing experimental data for these models. The library and web interface will be on github, and the paper should be submitted in the next few months. http://cetics.butantan.gov.br/signetsim

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!

COMBINE 2016 Day 3: Modelling ageing to enhance a healthy lifespan

COMBINE 2016

Daryl Shanley

Age is a major risk factor for chronic disease, and chronic diseases are the major cause of death and disability in the world, estimated at around 70% (WHO 2005). Molecular damage is the underlying factor in all of these (DNA damage (cancer), dementia and more). Ageing results from the accumulation of molecular damage. There is an irreversible accumulation of macromolecular damage, even though we have ameliorating systems such as the antioxidant systems, some damage escapes repair and builds up. Levels of oxidised protein, mutational frequency in nuclear DNA and mutational frequency in mDNA all increase exponentially with age. This underlying damage gives rise to cellular senescence. Cells which go into a permanent state of cell cycle arrest are called senescent, and they secrete a number of chemicals into the surrounding environment. The number of these cells increase with age. If you remove these senescent cells (e.g. from mice) there is a definite survival enhancement, though we don’t really understand why. So, although overall there aren’t many of them, they do seem to have quite an impact.

The good news is that there is plasticity in ageing. For instance, caloric restriction in mice does allow them to live longer (almost double). In part, this is due to them overeating if they’re allowed to free eat, but these undernourished mice aren’t healthy – they’re infertile, for example (it’s not a “natural state”). Mutations that bring longer life are in genes associated with nutrition – they’re signalling to the organism that there is less food available. This signal is somehow reducing molecular damage. However, it’s hard to test this in humans…

If we build models of known mechanisms, we can explore interventions, and with known interventions we can explore mechanisms. With a lot of background information, we can use the models to optimise synergy/antagonism, dose and timing. Ageing is caused by multiple mechanisms, and most damage increases exponentially – can the cycle be slowed or broken – there is an implication of positive feedback.

After existing knowledge and data has been used to create a calibrated model, then we perform sensitivity analysis and validate the model. Once all that has been done, then you can start using the model to make the predictions you’d like to see. It’s a long journey for a single model! They’ve created a set of Python modules for COPASI called PyCoTools, which allows you to compare models by generating other alternative models based on a starting model.

They are using a systems approach to model the development of the senescent phenotype with a view to find interventions to prevent progression and reverse the phenotype. They’d already been working on the processes involved in this with earlier models of insulin signalling, stress response, DNA damage, mitochondrial dynamics and ROSs.

Bringing all of these models together into an integrative dynamic model for cellular senescence is just the first task; they also needed to create an independent in vitro data set for estimating the integrated model parameters. This data was then used to fit their model. They had to infer what was going on inside the mitochondria, by inferring the internal states for ‘new’ and ‘old’ mitochondria. Then the model was used to make interventions for improving mito function and its phenotype, especially via combinations that would be difficult to perform in the lab.

If you reduce ROS in the model, it has an impact on the entire network. The results can be used to inform later experimental designs. Then there was in vitro confirmation of increased mitochondrial membrane potential during ROS inhibition. The model matched initially, but at a later date it diverged from the lab. When you go back and look at the cells, you find that there was very little movement among the senescent cells, which hampers autophagy. This is why the autophagy/mitophagy was predicted in the model, but wasn’t being seen in the lab. It’s a quality of the senescent cell which is blocking their removal from the cell. Mitochondrial dynamics are reduced over time, driven by an inability to remove the network of dysfunctional mitochondria.

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!