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Because there was no data from the wet lab we assumed that all the parameters in the system were random. | Because there was no data from the wet lab we assumed that all the parameters in the system were random. | ||
The parameters are all obtained by <a class="tooltip"> latin hypercube<span class="tooltiptext" style="width:700px;"> Latin hypercube is a statistical method to get random numbers from a box of n by n numbers. For example x = 4 with x is divisions and n = 2 with n is number of samples. You will obtain a box with 4 square times 2 square, give you 24 random numbers. For each parameter one number out of this box is randomly chosen. </span> </a> samples.<br> | The parameters are all obtained by <a class="tooltip"> latin hypercube<span class="tooltiptext" style="width:700px;"> Latin hypercube is a statistical method to get random numbers from a box of n by n numbers. For example x = 4 with x is divisions and n = 2 with n is number of samples. You will obtain a box with 4 square times 2 square, give you 24 random numbers. For each parameter one number out of this box is randomly chosen. </span> </a> samples.<br> | ||
− | <p onclick="javascript:ShowHide('HiddenDiv1')" style="border: 1px solid gray;"> | + | <p onclick="javascript:ShowHide('HiddenDiv1')" style="border: 1px solid gray;">Equations</p> |
<div class="mid" id="HiddenDiv1" style="display: none; border: 1px solid gray;"> | <div class="mid" id="HiddenDiv1" style="display: none; border: 1px solid gray;"> | ||
− | Equations for the different systems | + | Equations for the different systems<br> |
− | Quorum sensing system | + | Quorum sensing system <br> |
<b>ODEs</b><br> | <b>ODEs</b><br> | ||
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<b>Subpopulation system</b><br> | <b>Subpopulation system</b><br> | ||
− | <b>RFP </b> | + | <b>RFP </b><br> |
d[RFP]dt=α10[λ cl]k9+[λ cl]*k10k10+[434 cl-LVA]-β[RFP] (8) <br> | d[RFP]dt=α10[λ cl]k9+[λ cl]*k10k10+[434 cl-LVA]-β[RFP] (8) <br> | ||
Revision as of 11:50, 9 October 2016
Overview
In light of our guiding principles specificity, regulation and biocontainment, we modelled four different aspects of BeeT. The modelling work can inform and improve wet-lab experiments, providing a more robust and well rounded final product. Another facet is to assess the optimal application strategy for our project. We asked ourselves; what critical parts of our system can benefit the most from an interplay between modelling and experimental work? These considerations led us to ask the following questions;
- How can we assure optimal toxin production using quorum sensing and sub populations?
- What are important parameters for the killswitch to function optimally?
- Will BeeT be capable of surviving in sugar water?
- What is the best application strategy for BeeT?
Quorum Sensing
For the final product, BeeT, we intend to use toxins produced by Bacillus thuringiensis also called BT toxins. However, these toxins are also harmful to our chassis and would result in a reduction of toxin production. To counteract this effect we envision the use of quorum sensing that activates BT toxin production only when there is a large quantity of BeeT present. Synchronization of BT toxin production across the entire population would result in BeeT only producing a single burst of BT toxin before dying to its effects. Ideally, BeeT is able to produce BT toxin over a long period and dramatically improving effectiveness. To accomplish this we need multiple sub populations of BeeT, some producing BeeT while others are recuperating. This project is ideal for dynamic modelling as it represents a complex system with tune-able parameters, each parameter set can produce dramatically different population dynamics.
Introduction
For the iGEM project a toxin producing system has been made. We wanted to create a system where bacteria can produce toxin in waves and hereby create different cell populations. With the use of quorum sensing and the subpopulation system, as shown in Figure 1, we expect to find different cell populations.
Methods
During the research Matlab version R2016a has been used.
Because there was no data from the wet lab we assumed that all the parameters in the system were random.
The parameters are all obtained by latin hypercube Latin hypercube is a statistical method to get random numbers from a box of n by n numbers. For example x = 4 with x is divisions and n = 2 with n is number of samples. You will obtain a box with 4 square times 2 square, give you 24 random numbers. For each parameter one number out of this box is randomly chosen. samples.
Equations