Difference between revisions of "Team:Virginia/Collaborations"

 
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      Top filler<br/> write about our cool collabs here! <br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>
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       <div class="dict">temporary<span><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/2/25/Carboxysome_and_bacterial_microcompartments.jpg/800px-Carboxysome_and_bacterial_microcompartments.jpg"/>Bacterial microcompartments (BMCs) are organelle-like structures, consisting of a protein shell that encloses enzymes and other proteins. BMCs are typically about 40–200 nanometers in diameter and are entirely made of proteins. The shell functions like a membrane, as it is selectively permeable.</span></div> filler
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      <br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/>temporary filler<br/> temporary <div class="dict">filler<span>DNA origami is the nanoscale folding of DNA to create non-arbitrary two- and three-dimensional shapes at the nanoscale. The specificity of the interactions between complementary base pairs make DNA a useful construction material, through design of its base sequences. DNA is a well-understood material that is suitable for creating scaffolds that hold other molecules in place or to create structures all on its own.</span></div><br/>  
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            <div>Collaborations</div>
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          <div class="sectionTitle" id="Section 1">Modelling Collaboration with UChicago</div>
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                Our modeling team orchestrated a collaboration with the University of Chicago modeling team focused on enzyme concentration optimization. Their team created an R-based software which utilizes reaction parameters from a multi-enzyme pathway to find the most efficient enzyme stoichiometry. Through many zoom meetings to share reaction parameters and debug syntax, their software demonstrated the estimated increase in yield of our system with optimized enzyme concentrations (seen below). According to their model, concentrations in our optimized system are 2.05e-07, 4.74e-05, 6.43e-05, and 7.07e-05 M which correspond to ACS, ACC, 4CL, and STS enzymes respectively.<b> Our project allows for direct control of these enzyme ratios due to the zinc-finger domain patterns apparent in our DNA scaffolds. Therefore, we will use these findings in our future DNA scaffold assemblies to further test the efficiency of the Manifold system.</b> This collaboration heightened our knowledge of our own models, while providing UChicago further software checks for them to determine the accuracy of their optimization models.
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              <b>Fig 1.</b> Comparative resveratrol concentrations over time between current and optimized enzyme concentration systems. This graph was developed through the enzyme concentration software generated by the University of Chicago iGEM Team. The red curve utilizes the Manifold enzyme concentrations (1:1:1:1 ratio for the 4 enzymes) for output of the resveratrol concentration over time whereas the blue curve describes the optimized enzyme concentrations (2.05e-07, 4.74e-05, 6.43e-05, and 7.07e-05 M which correspond to ACS, ACC, 4CL, and STS enzymes respectively) effect on resveratrol yield over time. Note both curves utilized the same parameters which were used in the modelling portion of our project.
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          <div class="sectionTitle" id="Section 2">iGEM Resource Page</div>
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              In the age of Covid, we’ve implemented a variety of methods to maintain our team’s workflow and implement the device we've detailed, and through some extra work, we've managed to ensure communication through collaboration. For nearly all of our efforts, we’ve sought out new, digital resources that allow us to work together in real time. This work became the basis for what we call the iGEM Resource Page. After several talks with Nemanja Stijepovic and Leandros Tsiotos of the Video and Remote Technologies Committee, we created a compilation of all the relevant resources and tools iGEM teams should know about. We presented our original list in the Global Meet up, sharing the tools we had found most useful and how they allowed us to adapt to a summer spent outside the lab. Following this presentation, we distributed a survey where other iGEM teams could respond and tell us about the resources they found most useful.<b> We have organized and outlined all the resources in one document, talking about function and accessibility,pros and cons, and similar alternatives. We hope this document proves useful to future teams as they continue to work either online or in person.</b> You can see this document in our <a href="https://2020.igem.org/Team:Virginia/Contribution">Contributions</a> page, to be viewed or downloaded.
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Latest revision as of 05:35, 18 December 2020

Manifold

Index:
Collaborations
Modelling Collaboration with UChicago
Our modeling team orchestrated a collaboration with the University of Chicago modeling team focused on enzyme concentration optimization. Their team created an R-based software which utilizes reaction parameters from a multi-enzyme pathway to find the most efficient enzyme stoichiometry. Through many zoom meetings to share reaction parameters and debug syntax, their software demonstrated the estimated increase in yield of our system with optimized enzyme concentrations (seen below). According to their model, concentrations in our optimized system are 2.05e-07, 4.74e-05, 6.43e-05, and 7.07e-05 M which correspond to ACS, ACC, 4CL, and STS enzymes respectively. Our project allows for direct control of these enzyme ratios due to the zinc-finger domain patterns apparent in our DNA scaffolds. Therefore, we will use these findings in our future DNA scaffold assemblies to further test the efficiency of the Manifold system. This collaboration heightened our knowledge of our own models, while providing UChicago further software checks for them to determine the accuracy of their optimization models.

Fig 1. Comparative resveratrol concentrations over time between current and optimized enzyme concentration systems. This graph was developed through the enzyme concentration software generated by the University of Chicago iGEM Team. The red curve utilizes the Manifold enzyme concentrations (1:1:1:1 ratio for the 4 enzymes) for output of the resveratrol concentration over time whereas the blue curve describes the optimized enzyme concentrations (2.05e-07, 4.74e-05, 6.43e-05, and 7.07e-05 M which correspond to ACS, ACC, 4CL, and STS enzymes respectively) effect on resveratrol yield over time. Note both curves utilized the same parameters which were used in the modelling portion of our project.
iGEM Resource Page
In the age of Covid, we’ve implemented a variety of methods to maintain our team’s workflow and implement the device we've detailed, and through some extra work, we've managed to ensure communication through collaboration. For nearly all of our efforts, we’ve sought out new, digital resources that allow us to work together in real time. This work became the basis for what we call the iGEM Resource Page. After several talks with Nemanja Stijepovic and Leandros Tsiotos of the Video and Remote Technologies Committee, we created a compilation of all the relevant resources and tools iGEM teams should know about. We presented our original list in the Global Meet up, sharing the tools we had found most useful and how they allowed us to adapt to a summer spent outside the lab. Following this presentation, we distributed a survey where other iGEM teams could respond and tell us about the resources they found most useful. We have organized and outlined all the resources in one document, talking about function and accessibility,pros and cons, and similar alternatives. We hope this document proves useful to future teams as they continue to work either online or in person. You can see this document in our Contributions page, to be viewed or downloaded.