Difference between revisions of "Team:ETH Zurich/Modeling"

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<h2> Modules </h2>
 
<h2> Modules </h2>
 
<p>One advantage of our system is its modularity. The two modules are almost independent since only one species LuxR connects both ends. The modules can therefore be used again in other systems. </p>
 
<p>One advantage of our system is its modularity. The two modules are almost independent since only one species LuxR connects both ends. The modules can therefore be used again in other systems. </p>
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<h2> Goals </h2>
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<li> Studying the fold-change sensor.</li>
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<li> Check different approaches for the control of the quorum sensing module</li>
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<li> Determine conditions in which our system works as an AND-gate</li>
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<li> Implementing a reaction-diffusion model to feature biological properties of our system</li>
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<h2>AND-gate</h2>
 
<h2>AND-gate</h2>
 
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Revision as of 10:28, 18 September 2015

"What I cannot create I do not understand."
- Richard Feynmann

Modeling

Overview

Our system consists of a signalling chain, with a lactate sensor triggering the amplification of AHL for neighboring cells to finally produce a fluorescent signal. We divided the model into two modules which were modelled and tested separately before being merged into a single model of the whole system.

Microbeacon design

  1. Lactate module
  2. AHL module

Each module was then first evaluated at the single cell level using MATLAB, in order to evaluate the initial states, the steady states and to study the dynamics of the system. Then, the model was implemented in COMSOL multiphysics to characterize the spatial and temporal behavior.

Modules

One advantage of our system is its modularity. The two modules are almost independent since only one species LuxR connects both ends. The modules can therefore be used again in other systems.

Goals

  • Studying the fold-change sensor.
  • Check different approaches for the control of the quorum sensing module
  • Determine conditions in which our system works as an AND-gate
  • Implementing a reaction-diffusion model to feature biological properties of our system
  • AND-gate

    GFP concentration over time in four cases: A) Cancer cell, bound E. coli B)Cancer cell, unbound E. coli C) Normal cell, bound E. coli D) Normal cell, unbound E. coli

    Conclusions

    We characterized our system by simulating its modules separately and together through a series of increasingly-complex models. We show that under certain parameters, our lactate module is able to produce LuxR such that the lactate production input signal is amplified. Simulation of the AHL module with a simplified compartment model and with a more accurate reaction-diffusion model show that degradation of AHL by the E. coli does not significantly delay the self-activation of our LuxI and AHL production feedback loop. Instead, riboregulation of the LuxR promoter to prevent leaky expression of LuxI is sufficient to prevent this self-activation within the timescale of our experiment. This demonstrates the viability of our system as a specific CTC detection system utilizing these two general cancer markers.

    We would like to thank our sponsors