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− | + | <div style="width:100%;color:white;text-align: center;padding-top:550px;padding-bottom:0px;background-image:url('https://static.igem.org/mediawiki/2018/c/c2/T--Toronto--Drylab-banner-mk3.jpg'); background-color:rgba(0,0,0,0.0); background-repeat: no-repeat; background-size: cover;"> | |
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− | + | <h1 style="background-color:rgba(0,0,0,0.0); color:white;font: bold 40px/80px Helvetica, Sans-Serif; -webkit-text-stroke-width: 2px; -webkit-text-stroke-color: black; font-size: 90px;">Drylab</h1> | |
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+ | <p style="color: rgba(255,255,255,1.0); font-size: 20px; line-height:20px; text-align: center;"> </br> Our goal in the dry lab this year was to create four different models that allow our wet lab team to characterize their results, and allow future researchers to benchmark their results creating standard measures in the field of cellular flotation. First, we created a generic differential bioreactor model that allowed our team to predict the effectiveness of our <i>E. coli</i> cells to clean waste-waters if coupled with any surface binding method. </br> </br> We performed a complete sensitivity analysis on this model to allow future researchers to reuse this model with completely different parameters, strains of bacteria and object of waste. Then we created an algorithm that can track cellular flotation from frame to frame, and characterize exactly how the cells float; previously, we could only tell whether they floated or not. This coupled with our ODE buoyancy model allows us to define a maximum carrying capacity for each strain. Both of these models allowed our team to benchmark their results and will allow future researchers to quantify the performance of flotation as well. | ||
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+ | <h1 class="textArial" style="text-align: center;"><a href="https://2018.igem.org/Team:Toronto/Model"> | ||
+ | <img src="https://static.igem.org/mediawiki/2018/c/c8/T--Toronto--virtual-lab.svg" alt="icon1" style="width:200px;"> </br> | ||
+ | Model</a></h1> | ||
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Latest revision as of 23:42, 17 October 2018
Drylab
Our goal in the dry lab this year was to create four different models that allow our wet lab team to characterize their results, and allow future researchers to benchmark their results creating standard measures in the field of cellular flotation. First, we created a generic differential bioreactor model that allowed our team to predict the effectiveness of our E. coli cells to clean waste-waters if coupled with any surface binding method. We performed a complete sensitivity analysis on this model to allow future researchers to reuse this model with completely different parameters, strains of bacteria and object of waste. Then we created an algorithm that can track cellular flotation from frame to frame, and characterize exactly how the cells float; previously, we could only tell whether they floated or not. This coupled with our ODE buoyancy model allows us to define a maximum carrying capacity for each strain. Both of these models allowed our team to benchmark their results and will allow future researchers to quantify the performance of flotation as well.