Difference between revisions of "Team:Heidelberg/Model"

Line 14: Line 14:
 
     https://static.igem.org/mediawiki/2017/3/38/T--Heidelberg--2017_Background_Owl.jpg|blue|
 
     https://static.igem.org/mediawiki/2017/3/38/T--Heidelberg--2017_Background_Owl.jpg|blue|
  
−
     {{Heidelberg/abstract|https://static.igem.org/mediawiki/2017/8/88/T--Heidelberg--2017_modelling-graphical-abstract.svg|
+
     {{Heidelberg/abstract|https://static.igem.org/mediawiki/2017/a/ad/T--Heidelberg--Team_Heidelberg_2017_modeling_graphical_abstract.jpg|
 
         Successful <i>in vivo</i> 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 and labor efficient QC/monitoring as well as inducer/inhibitor concentrations required to express the required mutagenic polymerases.  Finally, we provide a web-based, fully interactive modeling platform that not only informed our wet lab experiments, but enables future iGEM teams to efficiently build on our work.
 
         Successful <i>in vivo</i> 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 and labor efficient QC/monitoring as well as inducer/inhibitor concentrations required to express the required mutagenic polymerases.  Finally, we provide a web-based, fully interactive modeling platform that not only informed our wet lab experiments, but enables future iGEM teams to efficiently build on our work.
 
     }}
 
     }}

Revision as of 15:31, 1 November 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 and labor efficient QC/monitoring as well as inducer/inhibitor concentrations required to express the required mutagenic polymerases. Finally, we provide a web-based, fully interactive modeling platform that not only informed our wet lab experiments, but enables future iGEM teams to efficiently build on our work.
Card image cap

Phage titer

Simulations of phage and E. coli 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.

Card image cap

Interactive Webtools

Use the interactive tools to simulate the conditions you are interested in and explore how the combined experimental parameters influence experimental outcomes.

Card image cap

Mutagenesis Induction

Model the glucose and arabinose concentration to make sure mutagenesis plasmids are sufficiently induced to get optimal mutagenesis conditions for both PREDCEL and PACE.

Analytic Model

Glucose Tool

Card image cap

Lagoon Contamination

Check if lagoons are vulnerable to contamination by microorganisms under given experimental conditions.

Analytic Model

Card image cap

Mutation Rate Estimation

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 money when sequencing.

Analytic Model

Card image cap

Medium Consumption

Calculate the amount of medium needed for a PACE experiment, see how medium consumption can be reduced when experimental parameters are optimized.

Analytic Model

Card image cap

Structure Changes

To assert potential effects of mutations we performed Molecular Dynamics Simulations

}}