Difference between revisions of "Team:Marburg"
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| − | {{Marburg/ | + | {{Marburg/index.css}} |
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| − | + | setTimeout(function () { | |
| − | + | $(".glitch").removeClass("glitch"); | |
| − | + | }, 1500); | |
| − | + | function inViewport(element) { | |
| − | + | var bb = element.getBoundingClientRect(); | |
| − | + | return !(bb.top > (innerHeight - 150) || bb.bottom < 50); | |
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} | } | ||
| + | document.querySelector(".main").addEventListener("scroll", function (event) { | ||
| + | var strain = document.querySelector(".main").querySelector("#strain"); | ||
| + | var toolbox = document.querySelector(".main").querySelector("#toolbox"); | ||
| + | var measurement = document.querySelector(".main").querySelector("#measurement"); | ||
| + | var automation = document.querySelector(".main").querySelector("#automation"); | ||
| + | var awards = document.querySelector(".main").querySelector("#awards"); | ||
| + | strain.style.opacity = inViewport(strain) ? "1" : "0"; | ||
| + | toolbox.style.opacity = inViewport(toolbox) ? "1" : "0"; | ||
| + | measurement.style.opacity = inViewport(measurement) ? "1" : "0"; | ||
| + | automation.style.opacity = inViewport(automation) ? "1" : "0"; | ||
| + | awards.style.opacity = inViewport(awards) ? "1" : "0"; | ||
| + | }) | ||
</script> | </script> | ||
| − | + | <div> | |
| − | <div | + | <div class="hero-wrapper"> |
| − | <div class=" | + | <video playsinline autoplay muted loop> |
| − | < | + | <source src="https://static.igem.org/mediawiki/2019/5/5f/T--Marburg--hero_video.mp4" type="video/mp4"> |
| − | < | + | </video> |
| − | + | <div class="words"> | |
| − | + | <p class="word glitch" data-text="F A S T E S T ."> | |
| − | + | F A S T E S T . | |
| − | + | </p> | |
| − | + | <p class="word glitch" data-text="P H O T O T R O P H I C ."> | |
| − | + | P H O T O T R O P H I C . | |
| − | + | </p> | |
| − | + | <p class="word glitch" data-text="O R G A N I S M ."> | |
| − | + | O R G A N I S M . | |
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</div> | </div> | ||
| + | <img class="bobbel" src="https://static.igem.org/mediawiki/2019/a/a6/T--Marburg--bobbel.svg" | ||
| + | onclick="$('.main').animate({ scrollTop: window.innerHeight - 52 }, 500);"> | ||
</div> | </div> | ||
| − | <div | + | <div class="hero-text"> |
| − | <p> | + | <p class="hero-first"> |
| − | + | By providing the fastest growing phototrophic chassis to the community, we are paving the way for other | |
| + | phototrophic organisms in Synthetic Biology. | ||
</p> | </p> | ||
| − | + | <hr class="line" style="transform: unset;"> | |
| − | + | <p class="hero-second"> | |
| − | <p> | + | We created an easy to use toolbox for <i>Synechococcus elongatus</i> UTEX 2973 to empower rapid design |
| − | + | testing, including genome engineering tools, self-replicating plasmid systems, natural competence and a Golden | |
| − | + | Gate-based part library. By providing the fastest growing phototrophic chassis with a doubling time of <b>under 80 minutes</b> to the community, we are paving the way | |
| − | + | for other phototrophic organisms in Synthetic Biology. | |
| − | + | ||
</p> | </p> | ||
</div> | </div> | ||
| − | < | + | <div> |
| − | <div class=" | + | <div class="box-light"> |
| − | + | <img id="strain" src="https://static.igem.org/mediawiki/2019/9/91/T--Marburg--hero_strain.jpg" | |
| − | + | alt="Strain Engineering Photo" style="margin-top: unset !important;"> | |
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</div> | </div> | ||
| − | </ | + | <div class="box-dark left"> |
| + | <img src="https://static.igem.org/mediawiki/2019/6/6f/T--Marburg--logo_strain.svg" class="logo" | ||
| + | alt="Strain Engineering Logo"> | ||
| + | <h1 class="heading"> | ||
| + | S T R A I N | ||
| + | <br><br> | ||
| + | E N G I N E E R I N G | ||
| + | </h1> | ||
| + | <hr class="line"> | ||
| + | <p class="text"> | ||
| + | We created an "easy to use" phototrophic chassis by restoring the natural competence of <i>S. elongatus</i> | ||
| + | UTEX 2973 in order to enormously simplify the transformation process. We established a genome modification | ||
| + | system via the CRISPR/Cas12a and enabled the usage of self-replicating plasmids overcoming the drawbacks of time | ||
| + | intensive genome integration for genetic design testing. | ||
| + | </p> | ||
| + | </div> | ||
| + | </div> | ||
| + | <div> | ||
| + | <div class="box-light"> | ||
| + | <img id="toolbox" src="https://static.igem.org/mediawiki/2019/c/ca/T--Marburg--hero_toolbox.jpg" alt="Toolbox Photo"> | ||
| + | </div> | ||
| + | <div class="box-dark right"> | ||
| + | <img src="https://static.igem.org/mediawiki/2019/a/a9/T--Marburg--logo_toolbox.svg" class="logo" alt="Toolbox Logo"> | ||
| + | <h1 class="heading"> | ||
| + | T O O L B O X | ||
| + | </h1> | ||
| + | <hr class="line"> | ||
| + | <p class="text"> | ||
| + | We constructed the green expansion, a set of biobricks to accompany our new chassis. It contains the first | ||
| + | MoClo compatible shuttle vector for cyanobacteria. Additionally users can design plasmids for genomic | ||
| + | integrations using novel rationally designed integration sites. To improve standardization in phototrophic | ||
| + | research we deliver standardized measurement entry vectors to test BioBricks in cyanobacteria. | ||
| + | </p> | ||
| + | </div> | ||
| + | </div> | ||
| + | <div> | ||
| + | <div class="box-light"> | ||
| + | <img id="measurement" src="https://static.igem.org/mediawiki/2019/c/c3/T--Marburg--hero_measurement.jpg" | ||
| + | alt="Measurement Photo"> | ||
| + | </div> | ||
| + | <div class="box-dark left"> | ||
| + | <img src="https://static.igem.org/mediawiki/2019/8/8a/T--Marburg--logo_measurement.svg" class="logo" | ||
| + | alt="Measurement Logo"> | ||
| + | <h1 class="heading" style="margin-top: 3em;"> | ||
| + | M E A S U R E M E N T | ||
| + | </h1> | ||
| + | <hr class="line"> | ||
| + | <p class="text"> | ||
| + | Following the call for long needed standardization in the cyanobacterial community, we ventured out to rationalize important measurements hugely affecting the growth of our cultures. As using fluorescence for part characterization proves difficult in self-fluorescent cyanobacteria, we showed that the use of bioluminescence reporters as well as the use of flow cytometry offer promising alternatives to improve these characterizations. | ||
| + | </p> | ||
| + | </div> | ||
| + | </div> | ||
| + | <div> | ||
| + | <div class="box-light"> | ||
| + | <img id="automation" src="https://static.igem.org/mediawiki/2019/f/f9/T--Marburg--hero_automation.jpg" | ||
| + | alt="Automation Photo"> | ||
| + | </div> | ||
| + | <div class="box-dark right"> | ||
| + | <img src="https://static.igem.org/mediawiki/2019/3/3f/T--Marburg--logo_automation.svg" class="logo" | ||
| + | alt="Automation Logo"> | ||
| + | <h1 class="heading"> | ||
| + | A U T O M A T I O N | ||
| + | </h1> | ||
| + | <hr class="line"> | ||
| + | <p class="text"> | ||
| + | The goal of the automation lab was to completely automate the process of cloning using OT-2 Pipetting robots. This was achieved using a state of the art faster-RCNN neural network and a self made camera module and light table for colony picking. We also automated large scale purification of plasmids. Our | ||
| + | software as well as the hardware blueprints are published on GitHub to give everybody access to scalable and affordable automation. | ||
| + | </p> | ||
| + | </div> | ||
| + | </div> | ||
| + | <div id="awards" class="box-light awards" onclick="window.location.href = '/Team:Marburg/MedalCriteria'"> | ||
| + | <h1 class="award-title title">Achievements</h1> | ||
| + | <img class="award" src="https://static.igem.org/mediawiki/2019/a/ae/T--Marburg--medal_bronze.svg"> | ||
| + | <img class="award" src="https://static.igem.org/mediawiki/2019/c/cc/T--Marburg--medal_silver.svg"> | ||
| + | <img class="award" src="https://static.igem.org/mediawiki/2019/6/6f/T--Marburg--medal_gold.svg"> | ||
| + | <img class="award" src="https://static.igem.org/mediawiki/2019/b/bd/T--Marburg--medal_special.svg"> | ||
| + | </div> | ||
</div> | </div> | ||
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</html> | </html> | ||
| − | {{Marburg/ | + | {{Marburg/footer}} |
Latest revision as of 17:00, 11 December 2019
F A S T E S T .
P H O T O T R O P H I C .
O R G A N I S M .
By providing the fastest growing phototrophic chassis to the community, we are paving the way for other phototrophic organisms in Synthetic Biology.
We created an easy to use toolbox for Synechococcus elongatus UTEX 2973 to empower rapid design testing, including genome engineering tools, self-replicating plasmid systems, natural competence and a Golden Gate-based part library. By providing the fastest growing phototrophic chassis with a doubling time of under 80 minutes to the community, we are paving the way for other phototrophic organisms in Synthetic Biology.
S T R A I N
E N G I N E E R I N G
We created an "easy to use" phototrophic chassis by restoring the natural competence of S. elongatus UTEX 2973 in order to enormously simplify the transformation process. We established a genome modification system via the CRISPR/Cas12a and enabled the usage of self-replicating plasmids overcoming the drawbacks of time intensive genome integration for genetic design testing.
T O O L B O X
We constructed the green expansion, a set of biobricks to accompany our new chassis. It contains the first MoClo compatible shuttle vector for cyanobacteria. Additionally users can design plasmids for genomic integrations using novel rationally designed integration sites. To improve standardization in phototrophic research we deliver standardized measurement entry vectors to test BioBricks in cyanobacteria.
M E A S U R E M E N T
Following the call for long needed standardization in the cyanobacterial community, we ventured out to rationalize important measurements hugely affecting the growth of our cultures. As using fluorescence for part characterization proves difficult in self-fluorescent cyanobacteria, we showed that the use of bioluminescence reporters as well as the use of flow cytometry offer promising alternatives to improve these characterizations.
A U T O M A T I O N
The goal of the automation lab was to completely automate the process of cloning using OT-2 Pipetting robots. This was achieved using a state of the art faster-RCNN neural network and a self made camera module and light table for colony picking. We also automated large scale purification of plasmids. Our software as well as the hardware blueprints are published on GitHub to give everybody access to scalable and affordable automation.
Achievements
