Latest revision as of 02:58, 22 October 2009

Dry Lab Hub

Welcome to the Dry Lab!
The goal of the Dry Lab has been to support the Wet Lab by answering questions of interest. The primary function of the models is to instruct the data analysis once results are gathered from the lab. Conclusions from the models aim to provide the team with a functional understanding of the system as well as design considerations that should be addressed in the genetic constructs. We have implemented several models, explaining the behaviour of different parts of the system. Below is a summary:

Modelling in this project has been important in defining the "Engineering constraints" of the project, and in particular, we have focused on quantifiying the temporal control.

Click on the links to find out more!

Autoinduction

Protein Production

Drug Kinetics

Genome Deletion

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 =[[Image:II09_DryLabThumb.png|80px]]<font size='5'><b>Dry Lab Hub</b></font>=
 Welcome to the Dry Lab!
+<br>
+The goal of the Dry Lab has been to support the Wet Lab by answering questions of interest. The primary function of the models is to instruct the data analysis once results are gathered from the lab.
+Conclusions from the models aim to provide the team with a functional understanding of the system as well as design considerations that should be addressed in the genetic constructs. We have implemented several  models, explaining the behaviour of different parts of the system. Below is a summary:<br>
+<!--
-The goal of the Dry Lab has been to support the Wet Lab by answering questions of interest.
+--[[User:Mabult|Mabult]] 17:05, 17 October 2009 (UTC) Do not forget that your models instruct the data analysis- it is actually their primary function-->
-Conclusions from the models aim to provide the team with a functional understanding of the system as well as design considerations that should be addressed in the genetic constructs.
-In the Dry Lab, we have implemented several  models, explaining the behaviour of different parts of the system. This section will use provide a summary of the models. The conclusions drawn can also be found here.
+[[Image:II09_drylabhub2.png|720px]]
+<!--
+*<b>Autoinduction:</b>
+**This section is responsible for switching on the encapsulation process when glucose in the medium is used up by the cell culture.
+** The goal of the model is to quantify this switch point from a primary to a secondary carbon source and provide an understanding of the diauxie phenomenon.
+*<b>Protein Production:</b>
+** Production of the drug of interest is controlled by the input amount of IPTG in the system. Prior IPTG induction, production of the drug of interest is repressed by LacI.
+** The goal of the model is to account for factors that may enhance/reduce the output yield of our drug of interest.
+*<b>Drug Kinetics:</b>
+** The drugs we are producing (PAH, cellulase, opiorphin) are enzymes. In order to detect how much drug has been produced, an understanding of their enzymatic activity is required.
+** The goal of the model is to better understand the drugs of interest produced using the relationships derived by Michaelis-Menten.
+*<b>Genome Deletion:</b>
+** Genome deletion is induced by an increase in temperature. Different temperatures relate to different restriction enzyme concentrations, so here we explore the possible effects of temperature on cell death.
+** The goal of the model is to explore a possible relationship between live cells and dead cells over time at different culture temperatures.
+-->
-Modelling in this project has been important in defining the "Engineering constraints" of the project, and in particular, we have focused in quantifiying the [https://2009.igem.org/Team:Imperial_College_London/Temporal_Control temporal control] aspects of our system. This page provides an overview of the models we have implemented.
-===Autoinduction===
-[[Image:II09_diauxi_illust.jpg|300px|right]]
-Starting the encapsulation process is related to diauxic growth of bacteria under the presence of two carbon sources.  2 models from literature have been discussed and implemented in this section. More information about each can be found in the extra tabs. After analysis of the models, we picked one of them as the best representation for our data. The sections below will provide a summary about each model, and at the end, discuss which one was picked and why. The diauxic growth phenomenon is tricky to understand, and the models we have chosen illustrate different approaches that groups have taken to explain what happens when bacteria switch to feeding from a primary carbon source to a secondary carbon source.
-====Goal of the models====
+Modelling in this project has been important in defining the "Engineering constraints" of the project, and in particular, we have focused on quantifiying the [https://2009.igem.org/Team:Imperial_College_London/Temporal_Control temporal control].
-*Provide understanding of the diauxic growth phenomenon
+<!--  --[[User:Mabult|Mabult]] 17:10, 17 October 2009 (UTC) repeating yourself here !!!-->
-*Find out the time taken to consume glucose in the medium to characterize the CRP promoter.
-<html><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Autoinduction"><img style="vertical-align:bottom;" width=50px align="left" src="http://i691.photobucket.com/albums/vv271/dk806/II09_Learnmore.png"></a></html>&nbsp; <b><i>About Autoinduction</i></b><br><br>
-===Protein Production===
-Based on the module 1 genetic circuit, a LacI-IPTG inducible promoter is responsible for kickstarting the production of the drug.
-* In the absence of IPTG, LacI represses the production of the drug (Cellulase or PAH)
-* When IPTG is introduced, the LacI repressing pathway is “de-repressed”, and some output protein is produced.
-[[Image:II09_NoIPTG_yesIPTG.jpg|350px|right]]
 <!-- [[Image:m1.fgc.JPG | 400px.jpg]]-->
-====Our goals====
-The modelling aims to provide an overview and better understanding of the M1 system’s function  by:
-*Characterizing the system.
-*Modeling to account for several factors that may reduce/hinder the production of  the protein drug such as:
-**Lac promoter leakiness
-**IPTG toxicity
-**Stability of output protein
-This module is an integral part of the design, as large-scale commercialization of the drug of interest depends on finding the optimal conditions for protein production. We implemented a system of differential equations, having made some assumptions and predictions about how the system will behave.
-<html><a href="https://2009.igem.org/Team:Imperial_College_London/Drylab/Protein_production/Analysis"><img style="vertical-align:bottom;" width=50px align="left" src="http://i691.photobucket.com/albums/vv271/dk806/II09_Learnmore.png"></a></html>&nbsp; <b><i>About the model assumptions and predictions!
-</i></b><br><br>
+<!--
 <html><center><a href="https://2009.igem.org/Team:Imperial_College_London/Genetic_Circuit"><img width=150px src="https://static.igem.org/mediawiki/2009/d/d9/II09_Temp_ArrowLeft.png"></a>
 <a href="https://2009.igem.org/Team:Imperial_College_London/Ethics"><img width=150px src="https://static.igem.org/mediawiki/2009/c/ce/II09_Temp_ArrowRight.png"></a>
 </html>
+-->
 Click on the links to find out more!

Team:Imperial College London/Drylab

From 2009.igem.org

Latest revision as of 02:58, 22 October 2009

Dry Lab Hub