Difference between revisions of "Team:Heidelberg/Model"

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                 Simulations of phage and <i>E. coli</i> titer support both PREDCEL and PACE by helping to choose a set of experimental parameters that is both efficient in terms of directed evolution and in terms of usability.
 
                 Simulations of phage and <i>E. coli</i> titer support both PREDCEL and PACE by helping to choose a set of experimental parameters that is both efficient in terms of directed evolution and in terms of usability.
 
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                 {{Heidelberg/panelelement|Induction|https://static.igem.org/mediawiki/2017/4/48/T--Heidelberg--2017_mutagenesis-induction-logo.png|induction|https://2017.igem.org/Team:Heidelberg/Model/Mutagenesis_Induction|{{#tag:html|
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                 {{Heidelberg/panelelement|Mutagenesis Induction|https://static.igem.org/mediawiki/2017/4/48/T--Heidelberg--2017_mutagenesis-induction-logo.png|induction|https://2017.igem.org/Team:Heidelberg/Model/Mutagenesis_Induction|
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                Model the glucose concentration to make sure mutagenesis plasmids are sufficiently induced to get optimal mutagenesis conditions for both PREDCEL and PACE.
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    Modeling|
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    Overview|
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    {{Heidelberg/abstract|https://static.igem.org/mediawiki/2017/8/88/T--Heidelberg--2017_modelling-graphical-abstract.svg|
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        Successful in vivo directed evolution by PREDCEL and PACE requires the thorough consideration of experimental parameters, e.g. phage propagation times, culture dilution rates and inducer/inhibitor concentrations. We employed extensive ODE-based and stochastic modeling to identify the most sensitive parameters and adapt our experiments accordingly. First, we calibrated our models using phage propagation experiments from our wet lab complemented with literature data. Simulations showed that the phage titer is highly sensitive to culture dilution rates. We simulated batch times and transfer volumes for PREDCEL and corresponding flow rates for PACE to determine optimized conditions for gene pool selection while avoiding phage washout. We also estimated phage titer monitoring intervals for cost/labor efficient QC as well as inducer/inhibitor concentrations required to express the required mutagenic polymerases. Finally, provide a web-based, fully interactive modeling platform, not only extensively employed by our wet lab, but highly informs future iGEM teams building on our work.
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                {{Heidelberg/panelelement|Phage titer|https://static.igem.org/mediawiki/2017/2/2b/T--Heidelberg--2017_phage-titer-logo.png|phagetiter|https://2017.igem.org/Team:Heidelberg/Model/Phage_Titer|
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                Simulations of phage and <i>E. coli</i> titer support both PREDCEL and PACE by helping to choose a set of experimental parameters that is both efficient in terms of directed evolution and in terms of usability.
 +
                }}
 +
                {{Heidelberg/panelelement|Mutagenesis Induction|https://static.igem.org/mediawiki/2017/4/48/T--Heidelberg--2017_mutagenesis-induction-logo.png|induction|https://2017.igem.org/Team:Heidelberg/Model/Mutagenesis_Induction|
 
                 Model the glucose concentration to make sure mutagenesis plasmids are sufficiently induced to get optimal mutagenesis conditions for both PREDCEL and PACE.
 
                 Model the glucose concentration to make sure mutagenesis plasmids are sufficiently induced to get optimal mutagenesis conditions for both PREDCEL and PACE.
                <br>
 
                See the <a href="https://2017.igem.org/Team:Heidelberg/Model/Induction">Interactive Webtool</a> built for regular use.}}
 
 
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Revision as of 17:17, 27 October 2017

Modeling
Overview
Successful in vivo directed evolution by PREDCEL and PACE requires the thorough consideration of experimental parameters, e.g. phage propagation times, culture dilution rates and inducer/inhibitor concentrations. We employed extensive ODE-based and stochastic modeling to identify the most sensitive parameters and adapt our experiments accordingly. First, we calibrated our models using phage propagation experiments from our wet lab complemented with literature data. Simulations showed that the phage titer is highly sensitive to culture dilution rates. We simulated batch times and transfer volumes for PREDCEL and corresponding flow rates for PACE to determine optimized conditions for gene pool selection while avoiding phage washout. We also estimated phage titer monitoring intervals for cost/labor efficient QC as well as inducer/inhibitor concentrations required to express the required mutagenic polymerases. Finally, provide a web-based, fully interactive modeling platform, not only extensively employed by our wet lab, but highly informs future iGEM teams building on our work.
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Modeling
Overview
Successful in vivo directed evolution by PREDCEL and PACE requires the thorough consideration of experimental parameters, e.g. phage propagation times, culture dilution rates and inducer/inhibitor concentrations. We employed extensive ODE-based and stochastic modeling to identify the most sensitive parameters and adapt our experiments accordingly. First, we calibrated our models using phage propagation experiments from our wet lab complemented with literature data. Simulations showed that the phage titer is highly sensitive to culture dilution rates. We simulated batch times and transfer volumes for PREDCEL and corresponding flow rates for PACE to determine optimized conditions for gene pool selection while avoiding phage washout. We also estimated phage titer monitoring intervals for cost/labor efficient QC as well as inducer/inhibitor concentrations required to express the required mutagenic polymerases. Finally, provide a web-based, fully interactive modeling platform, not only extensively employed by our wet lab, but highly informs future iGEM teams building on our work.
{{{2}}}