Difference between revisions of "Team:Heidelberg/Model"

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                 Estimate the number of mutated sequences in a PREDCEL or PACE experiment at a given point in time to check for the covered sequence space and to save time and mony when sequencing.
 
                 Estimate the number of mutated sequences in a PREDCEL or PACE experiment at a given point in time to check for the covered sequence space and to save time and mony when sequencing.
 
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                 {{Heidelberg/panelelement|Medium Consumption|https://static.igem.org/mediawiki/2017/1/13/T--Heidelberg--2017_medium_consumption-logo.png|medium|https://2017.igem.org/Team:Heidelberg/Model/Medium_Consumption|
 
                 {{Heidelberg/panelelement|Medium Consumption|https://static.igem.org/mediawiki/2017/1/13/T--Heidelberg--2017_medium_consumption-logo.png|medium|https://2017.igem.org/Team:Heidelberg/Model/Medium_Consumption|
 
                 Calculate the amount of medium needed for a PACE experiment, see how medium consumption can be reduced when experimental parameters are optimised.
 
                 Calculate the amount of medium needed for a PACE experiment, see how medium consumption can be reduced when experimental parameters are optimised.
 
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                {{Heidelberg/panelelement|Lagoon Contamination|https://static.igem.org/mediawiki/2017/8/8e/T--Heidelberg--2017_lagoon_contamination-logo.png|contamination|https://2017.igem.org/Team:Heidelberg/Model/Lagoon_Contamination|
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                Check if lagoons are vulnerable to contamination by microorganisms under given experimental conditions.
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{{Heidelberg/footer}}
 
{{Heidelberg/footer}}

Revision as of 18:06, 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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