Difference between revisions of "Team:ECUST/Iron Sensing"

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<h1 class="box-heading">The fur-lac-cecropin AD system</h1>
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<p>&nbsp;&nbsp; To understand, predict and ultimately control the behavior of our engineered microbial group effect, we have developed dynamic model of the system, based on transerential equations which describe and integrate the individual processes. This model involves several entities going from the molecular level (genes, RNAs, proteins, and metabolites) up to the cellular and population levels, distinct intracellular and extracellular compartments, and a wide range of biological and physical processes (transcription, translation, signalling, growth, transusion, etc). Here we can show the concentrate of DspB and Enterobactin produced by our engineered bacteria and the biofilm and rust removing time through calculating.</p>
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<p>In order to measure the ability of engineered bacteria to kill iron bacteria, we modeled the production of the cecropin AD and the sterilizing ability of the cecropin. We want to model the fur-box fell the concentrate of Fe2+ to drive the LacI to reverse the signal to produce the cecropin AD which play a bactericidal effect. So our model used in the project includes four parts: the iron sensor, the inverter system, the sterilizing system and the chelator system.</p>
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<p>The first part focuses on the sensor system to find the Fe2+ needed by our system. And the strength of the best promoter with the fur-box of the three kinds of fur-box was determined. Second, the inverter system our team implemented in the bacteria focused on the cecropin AD produced with time. The third part modeled the sterilization rate of the cecropin AD. The last part integrate those previous parts leading to the cecropin production and sterilization. The model show the time it takes for our bacteria system to work. </p>
 
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<p>&nbsp;&nbsp;Naturally, when there is a certain amount of HSL in the environment, HSL complex with afeR proteins and bind to afeR promoter which regulate positively the genes downstream (as shown on the Figure 1) and on that our sensing system relies to produce DspB and enterobactin.</p>
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<h2>1.1 Introduction</h2>
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<p>We first modeled the sensing system using ODEs with the help of experimental results to determine one of our parameters ki1.We had three kinds of fur-box designs. We model our three kinds of fur-box (shown in the figure 2) to find the optimal fur-box and the strength of the promoter with the best kind of fur-box. We finally corrected our model through the experiments. We make this framework like figure 1.</p>
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<p>(1) the iron-FUR complex formation:</p>
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Revision as of 13:43, 17 October 2018

Cecropin AD production and sterilizing ability model

The fur-lac-cecropin AD system

In order to measure the ability of engineered bacteria to kill iron bacteria, we modeled the production of the cecropin AD and the sterilizing ability of the cecropin. We want to model the fur-box fell the concentrate of Fe2+ to drive the LacI to reverse the signal to produce the cecropin AD which play a bactericidal effect. So our model used in the project includes four parts: the iron sensor, the inverter system, the sterilizing system and the chelator system.

The first part focuses on the sensor system to find the Fe2+ needed by our system. And the strength of the best promoter with the fur-box of the three kinds of fur-box was determined. Second, the inverter system our team implemented in the bacteria focused on the cecropin AD produced with time. The third part modeled the sterilization rate of the cecropin AD. The last part integrate those previous parts leading to the cecropin production and sterilization. The model show the time it takes for our bacteria system to work.

1.Sensor model

1.1 Introduction

We first modeled the sensing system using ODEs with the help of experimental results to determine one of our parameters ki1.We had three kinds of fur-box designs. We model our three kinds of fur-box (shown in the figure 2) to find the optimal fur-box and the strength of the promoter with the best kind of fur-box. We finally corrected our model through the experiments. We make this framework like figure 1.

1.2 Methods and materials:

1.2.1 The dynamic simulation of sense iron to FBS:

(1) the iron-FUR complex formation:

$$2\cdot FUR+2\cdot Fe\leftrightarrow Fe_{2}FUR_{2}$$

3.Goals

  Our goal of this model is to create a generic quorum sensing model so that:

  • We can determine the effect of afeR promoter and predict the production of DspB and enterobactin.

  • We can predict hao long our engineered bacteria would take to remove the biofilm and rust.

4.Materials and Methods

4.1 HSL Transfer

  HSL is produced by iron bacterias and realeased into the water environment. So the first step of our sensing is HSL transfering into our engineered E.coli from the water. And a passive transusion model is used for this process that the transfer rate of HSL can be described as this:

  • KHSL,W-C : transfer coefficient through the membrane (s−1)

  • We can predict hao long our engineered bacteria would take to remove the biofilm and rust.

4.2 AfeR-HSL Complexation

  AfeR is produced by engineered E.coli and functions in cell and its concentration is obtained approximating the number of protein per cell, using the E.coli concentration (cell/L) and the Avogadro number.

  The AfeR-HSL complexation is simply formed that way:

  Assuming kinetics of AfeR-HSL complexation complexation is fast compared to the rest of the system, we assumed that the free and complexed forms are at equilibrum.

  • K eq, AfeR-HSL : equilibrum constant of the AfeR-HSL complexation (mol/L)

4.3 DspB Production

  The production of the DspB from the DspB gene includes transcription and translation after activation. In addition, we should also consider its transport and degradation.

4.3.1 DspB Gene Activation

  This process is modeled using a Michaelian formalism depending on its activator (AfeR-HSL complexation) concentration. The promoter strength is also taken into account.

  • DspB DNA,0/cell : total number of DspB DNA per cell

  • DspB DNA/cell : number of activated DspB DNA per cell

  • K a, AfeR-HSL : activation constant of the AfeR-HSL complexation (mol/L)

  • k p, afeR : afeR promoter influence

4.3.2 DspB Transcription

  The DspB transcription depends on the transcription rate of the strain and the length of the DspB gene. The Avogadro number is used to express the transcription velocity in molar concentration in one cell per time unit.

  • ktranscript : E.coli transcription rate (nucleotides/s)

  • RNA polymerase/gene: number of RNA polymerase per gene

  • DNA length (DspB): number of nucleotides on the DspB gene

  • V intracell: volume of a bacterial cell (L)

  For the convenience of mathematical operation, we merged the ktranscript、RNA polymerase/gene and "V" intracell to a constant.

4.3.3 DspB Translation

  The DspB translation depends on the translation rate of the strain, the mRNA length and the quantity of mRNA. The translation velocity is expressed in molar concentration in one cell per time unit.

  • ktranslation : E.coli translation rate (nucleotides/s)

  • Ribosomes/RNA: number of ribosomes per mRNA

  • RNA length (DspB): number of nucleotides on the DspB mRNA

  • [DspB mRNA] : DspB mRNA concentration in one E.coli cell

  For the convenience of mathematical operation, we merge the ktranslation and Ribosomes/RNA and to a constant.

4.3.4 Degradation

  Some of the DspB protein and mRNA are degraded. A degradation constant is used to model the degradation velocity.

  • Kdeg,DspB: DspB degradation constant (s−1)

  • Kdeg,DspB mRNA: DspB mRNA degradation constant (s−1)

4.3.5 DspB Transfer

  DspB protein needs to be transferred to the water environment to function. This process is taken into account through a passive transusion model.

  • KDspB,C-W : transfer coefficient through the membrane (s−1)

4.4 Biofilm Removel

  The biofilm is removed by the DspB and the process is modeled assuming a Michaelis-Menten kinetics.

  • kcat,DspB : catalytic constant of the DspB enzyme (s −1)

  • "KM,D" : Michaelis constant of the DspB enzyme (mol/L)

4.5 EntE Production

  We treat enterobactin enzymes gene cluster as a whole gene (EntE gene). The production of the enterobactin enzymes from the EntE gene includes transcription and translation after activation. In addition, we should also consider its degradation. Because the enterobactin enzymes function in the cell, we don't need to consider its transport to the water environment.

4.5.1 EntE Gene Activation

  This process is modeled using a Michaelian formalism depending on its activator (AfeR-HSL complexation) concentration. The promoter strength is also taken into account.

  • EntE DNA,0/cell : total number of EntE DNA per cell

  • EntE DNA/cell : number of activated EntE DNA per cell

  • K a, AfeR-HSL : activation constant of the AfeR-HSL complexation (mol/L)

  • k p, afeR : afeR promoter influence

4.5.2 EntE Transcription

  The EntE transcription depends on the transcription rate of the strain and the length of the EntE gene. The Avogadro number is used to express the transcription velocity in molar concentration in one cell per time unit.

  • "EntE DNA,/cell" : number of EntE gene per cell

  • ktranscript : E.coli transcription rate (nucleotides/s)

  • RNA polymerase/gene: number of RNA polymerase per gene

  • DNA length (EntE): number of nucleotides on the EntE gene

  • Vintracell : volume of a bacterial cell (L)

  For the convenience of mathematical operation, we merged the ktranscript、RNA polymerase/gene and "V" intracell to a constant.