Team:SCUT/Modeling/Oscillation

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   In the deterministic model, the concentration of the constitutively produced LuxR protein R is assumed constant.<br>
   In the deterministic model, the concentration of the constitutively produced LuxR protein R is assumed constant.<br>
   In the first two equations, the Hill function<br>
   In the first two equations, the Hill function<br>
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   <img style="width:200px;height:50px;margin:10px 40px;"src="#"/><br>
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   <img style="width:200px;margin:10px 40px;"src="https://static.igem.org/mediawiki/igem.org/f/f5/Scut_equation09.png"/><br>
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   describes the delayed production of corresponding proteins, it depends on the past concentration of the internal AHL, <img style="width:130px;height:25px;"src="#"/>. The delayed reactions include the complex processes (transcription, translation, maturation, etc.), leading to formation of functional proteins.
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   describes the delayed production of corresponding proteins, it depends on the past concentration of the internal AHL, <img style="height:20px;"src="https://static.igem.org/mediawiki/igem.org/a/ad/Scut_equation10.png"/>. The delayed reactions include the complex processes (transcription, translation, maturation, etc.), leading to formation of functional proteins.
   </p>
   </p>
   <ol class="word">
   <ol class="word">
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   <li> The factor <img style="width:100px;height:25px;"src="#"/> describes slowing down of protein synthesis at high cell density d due to lower nutrient supply and high waste concentration.</li>
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   <li> The factor <img style="height:20px;"src="https://static.igem.org/mediawiki/igem.org/4/49/Scut_equation11.png"/> describes slowing down of protein synthesis at high cell density d due to lower nutrient supply and high waste concentration.</li>
   <li>γ<sub><i>x</i></sub> describes enzymatic degradation of proteins and AHL by proteases inside of the cell due to their degradation tags(modeling these processes by Michaelis-Menten kinetics).</li>
   <li>γ<sub><i>x</i></sub> describes enzymatic degradation of proteins and AHL by proteases inside of the cell due to their degradation tags(modeling these processes by Michaelis-Menten kinetics).</li>
   <li>D describes diffusion of AHL by external fluid flow. The cell density (d) determines the amount of external AHL and thus affects the AHL decay rate.</li>
   <li>D describes diffusion of AHL by external fluid flow. The cell density (d) determines the amount of external AHL and thus affects the AHL decay rate.</li>
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   <p class="text"><strong>Parameters set:</strong><br>
   <p class="text"><strong>Parameters set:</strong><br>
   We use the following experimentally relevant scaled parameters in most of simulations.<br>
   We use the following experimentally relevant scaled parameters in most of simulations.<br>
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   <img style="width:600px;height:750px;margin:10px 30px;"src="#"/><br><br>
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   <img style="width:600px;margin:10px 40px;"src="https://static.igem.org/mediawiki/igem.org/e/e5/Scut_flame02.png"/><br><br>
   <strong>Result:</strong><br>
   <strong>Result:</strong><br>
   </p>
   </p>
   <div class="imgC">
   <div class="imgC">
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   <img style="width:500px;height:400px;"src="#"/>
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   <img style="width:500px;"src="https://static.igem.org/mediawiki/igem.org/a/ab/Scut_model10.jpg"/>
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   <p class="tag"style="width:600px;"><strong>Figure 2.</strong>The simulation result show that a typical time series of concentrations of LuxI (cyan circles), AiiA (blue circles), internal AHL (green line) and external AHL (red line). LuxI and AiiA closely track each other, and are anti-phase with the concentrations of external and internal AHL.
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   <p class="tag"style="width:600px;"><strong>Figure 2.</strong> The simulation result show that a typical time series of concentrations of LuxI (cyan circles), AiiA (blue circles), internal AHL (green line) and external AHL (red line). LuxI and AiiA closely track each other, and are anti-phase with the concentrations of external and internal AHL.
   </p>
   </p>
   </div>
   </div>
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   <p onclick="scroll_1()"style="padding:2px 0;font-size:14px;">Deterministic model..</p>
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   <p onclick="scroll_2()"style="padding:2px 0;">degrade-and-fire..</p>
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   <p onclick="scroll_3()">Reference</p>
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   <p class="text"><strong>Background: Delayed Feedback Models</strong><br>
   <p class="text"><strong>Background: Delayed Feedback Models</strong><br>
   The delayed negative feedback-only (NFB) oscillator is modeled by two reactions describing delayed birth and death processes for repressor protein. The rates K<sub>+</sub> and K<sub>-</sub> for production and degradation of repressor σ at the time t are<br>
   The delayed negative feedback-only (NFB) oscillator is modeled by two reactions describing delayed birth and death processes for repressor protein. The rates K<sub>+</sub> and K<sub>-</sub> for production and degradation of repressor σ at the time t are<br>
-
   <img style="width:250px;height:300px;margin:10px 20px;"src="#"/><br>
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   <img style="width:250px;margin:10px 20px;"src="https://static.igem.org/mediawiki/igem.org/f/f3/Scut_equation12.png"/><br>
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   Where the subscript τ here and below denotes the value of a variable taken at time τ before the current time t, <img style="width:150px;height:25px;"src="#"/>. Notice that a molecular derivation of the function F(ψ) may provide a form such as <img style="width:150px;height:25px;"src="#"/> instead of <img style="width:80px;height:25px;"src="#"/> , but we use the latter for simplicity. Equations (1)-(3) can be simulated with a modified Gillespie algorithm, described previously.<br>
+
   Where the subscript τ here and below denotes the value of a variable taken at time τ before the current time t, <img style="height:20px;"src="https://static.igem.org/mediawiki/igem.org/a/a7/Scut_equation13.png"/>. Notice that a molecular derivation of the function F(ψ) may provide a form such as <img style="height:40px;"src="https://static.igem.org/mediawiki/igem.org/0/0e/Scut_equation14.png"/> instead of <img style="height:40px;"src="https://static.igem.org/mediawiki/igem.org/a/a0/Scut_equation15.png"/> , but we use the latter for simplicity. Equations (1)-(3) can be simulated with a modified Gillespie algorithm, described previously.<br>
   We use a similar model for coupled positive-negative feedback (PNFB) which is inspired by our recent experimental study. The rates K<sup><i>(r)</i></sup> and K<sup><i>(a)</i></sup> for repressor and activator, as the form<br>
   We use a similar model for coupled positive-negative feedback (PNFB) which is inspired by our recent experimental study. The rates K<sup><i>(r)</i></sup> and K<sup><i>(a)</i></sup> for repressor and activator, as the form<br>
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   <img style="width:300px;height:500px;margin:10px 20px;"src="#"/><br>
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   <img style="width:400px;margin:5px 20px;"src="https://static.igem.org/mediawiki/igem.org/5/50/Scut_equation16.png"/><br>
   The τ<sub><i>a</i></sub> is the delay time for the production of activator, and f is a parameter representing the strength of positive feedback. The activated rate for repressor production is α, and the corresponding rate is α/f.<br>
   The τ<sub><i>a</i></sub> is the delay time for the production of activator, and f is a parameter representing the strength of positive feedback. The activated rate for repressor production is α, and the corresponding rate is α/f.<br>
   The effective treatment of dilution with a first order dilution rate β in the above models warrants a brief discussion. Dilution in the context of gene circuits is a reduction of the intracellular concentration of a species due to an increase in cell volume. For the systems, the  β-terms for the degradation rates adequately model the effect of dilution.
   The effective treatment of dilution with a first order dilution rate β in the above models warrants a brief discussion. Dilution in the context of gene circuits is a reduction of the intracellular concentration of a species due to an increase in cell volume. For the systems, the  β-terms for the degradation rates adequately model the effect of dilution.
   </p>
   </p>
   <p class="text"><strong>The delay-differential equation:</strong><br>
   <p class="text"><strong>The delay-differential equation:</strong><br>
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   <img style="width:150px;height:60px;margin:10px 30px;"src="#"/><br>
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   <img style="height:80px;margin:10px 30px;"src="https://static.igem.org/mediawiki/igem.org/5/57/Scut_equation17.png"/><br>
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   describe oscillations of individual biopixel as a combined effect of production and delayed autorepression of the colony-averaged LuxI concentration X and its enzymatic degradation by CloXP (second term). The first (production) term describes both delayed auto-repression of LuxI and its delayed activation by H<sub>2</sub>O<sub>2</sub> proportional to its concentration P. Subscripts τ<sub>1</sub> and τ<sub>2</sub> indicate the delayed concentrations, <img style="width:100px;height:25px;"src="#"/> and <img style="width:100px;height:25px;"src="#"/>. The dynamics of P is described by the equation<br>
+
   describe oscillations of individual biopixel as a combined effect of production and delayed autorepression of the colony-averaged LuxI concentration X and its enzymatic degradation by CloXP (second term). The first (production) term describes both delayed auto-repression of LuxI and its delayed activation by H<sub>2</sub>O<sub>2</sub> proportional to its concentration P. Subscripts τ<sub>1</sub> and τ<sub>2</sub> indicate the delayed concentrations, <img style="height:20px;"src="https://static.igem.org/mediawiki/igem.org/b/b8/Scut_equation18.png"/> and <img style="height:20px;"src="https://static.igem.org/mediawiki/igem.org/6/61/Scut_equation19.png"/>. The dynamics of P is described by the equation<br>
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   <img style="width:110px;height:40px;margin:10px 20px;"src="#"/><br>
+
   <img style="width:180px;margin:10px 20px;"src="https://static.igem.org/mediawiki/igem.org/c/cd/Scut_equation20.png"/><br>
   Where the three terms describe the basal and induced production and degradation of H<sub>2</sub>O<sub>2</sub>. We use the following dimensionless parameters for most of our simulations:<br>
   Where the three terms describe the basal and induced production and degradation of H<sub>2</sub>O<sub>2</sub>. We use the following dimensionless parameters for most of our simulations:<br>
   </p>
   </p>
   <div class="imgC">
   <div class="imgC">
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   <img style="width:600px;height:750px;"src="#"/>
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   <img style="width:600px;"src="https://static.igem.org/mediawiki/igem.org/7/76/Scut_flame03.png"/>
   </div>
   </div>
   <p class="text"><strong>Result:</strong>
   <p class="text"><strong>Result:</strong>
   </p>
   </p>
   <div class="imgC">
   <div class="imgC">
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   <img style="width:700px;height:400px;"src="#"/>
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   <img style="width:700px;"src="https://static.igem.org/mediawiki/igem.org/a/a8/Scut_model03.jpg"/>
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   <p class="tag"><strong>Figure 3.</strong></p>
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   <p class="tag"><strong>Figure 3.</strong> The simulation result of oscillaiton through "degrade-and-fire" model</p>
   </div>
   </div>
   <p class="text">At the same time, the model is able to capture the alternating large and small amplitude oscillations observed in the ON/OFF biosensor. This behavior is seen when C<sub>0</sub> is increased 2-fold, capturing the decreased level of LuxR where it is the limiting factor for the oscillations.<br>
   <p class="text">At the same time, the model is able to capture the alternating large and small amplitude oscillations observed in the ON/OFF biosensor. This behavior is seen when C<sub>0</sub> is increased 2-fold, capturing the decreased level of LuxR where it is the limiting factor for the oscillations.<br>

Latest revision as of 03:23, 28 September 2013

Deterministic model of oscillator

Background:
We derive the set of delay-differential equation model for intracellular concentrations of LuxI (I), AiiA (A), internal AHL (Hi), and external AHL (He).

In the deterministic model, the concentration of the constitutively produced LuxR protein R is assumed constant.
In the first two equations, the Hill function

describes the delayed production of corresponding proteins, it depends on the past concentration of the internal AHL, . The delayed reactions include the complex processes (transcription, translation, maturation, etc.), leading to formation of functional proteins.

  1. The factor describes slowing down of protein synthesis at high cell density d due to lower nutrient supply and high waste concentration.
  2. γx describes enzymatic degradation of proteins and AHL by proteases inside of the cell due to their degradation tags(modeling these processes by Michaelis-Menten kinetics).
  3. D describes diffusion of AHL by external fluid flow. The cell density (d) determines the amount of external AHL and thus affects the AHL decay rate.
  4. The last term in equation for He describes the diffusion of external AHL.

Parameters set:
We use the following experimentally relevant scaled parameters in most of simulations.


Result:

Figure 2. The simulation result show that a typical time series of concentrations of LuxI (cyan circles), AiiA (blue circles), internal AHL (green line) and external AHL (red line). LuxI and AiiA closely track each other, and are anti-phase with the concentrations of external and internal AHL.

Deterministic model..

degrade-and-fire..

Reference

"degrade-and-fire" model

Our simplified model is based on apreviously described model for the oscillator. In addition to the H2O2 produce reactions reflected in that model. From the biochemical reactions, we derived a set of delay differential equations to be used as our model. These delayed reactions mimic the complex cascade of processes (transcription, translation, maturation, etc.) leading to formation of functional proteins.

Why should we simplify the model of oscillation
Deterministic model of oscillator requires a high cost of numerical method. And it’s hard to simulate the model when the oscillating colonies across a microfludic array.
To model the oscillating colonies across a microfluidic array, we set up a simplified "degrade-and-fire" model.

Background: Delayed Feedback Models
The delayed negative feedback-only (NFB) oscillator is modeled by two reactions describing delayed birth and death processes for repressor protein. The rates K+ and K- for production and degradation of repressor σ at the time t are

Where the subscript τ here and below denotes the value of a variable taken at time τ before the current time t, . Notice that a molecular derivation of the function F(ψ) may provide a form such as instead of , but we use the latter for simplicity. Equations (1)-(3) can be simulated with a modified Gillespie algorithm, described previously.
We use a similar model for coupled positive-negative feedback (PNFB) which is inspired by our recent experimental study. The rates K(r) and K(a) for repressor and activator, as the form

The τa is the delay time for the production of activator, and f is a parameter representing the strength of positive feedback. The activated rate for repressor production is α, and the corresponding rate is α/f.
The effective treatment of dilution with a first order dilution rate β in the above models warrants a brief discussion. Dilution in the context of gene circuits is a reduction of the intracellular concentration of a species due to an increase in cell volume. For the systems, the β-terms for the degradation rates adequately model the effect of dilution.

The delay-differential equation:

describe oscillations of individual biopixel as a combined effect of production and delayed autorepression of the colony-averaged LuxI concentration X and its enzymatic degradation by CloXP (second term). The first (production) term describes both delayed auto-repression of LuxI and its delayed activation by H2O2 proportional to its concentration P. Subscripts τ1 and τ2 indicate the delayed concentrations, and . The dynamics of P is described by the equation

Where the three terms describe the basal and induced production and degradation of H2O2. We use the following dimensionless parameters for most of our simulations:

Result:

Figure 3. The simulation result of oscillaiton through "degrade-and-fire" model

At the same time, the model is able to capture the alternating large and small amplitude oscillations observed in the ON/OFF biosensor. This behavior is seen when C0 is increased 2-fold, capturing the decreased level of LuxR where it is the limiting factor for the oscillations.
The simulation result shows that the alternating oscillations vanish when LuxR is restored to its normal level in the model.

Reference:

[1] Danino. T, Mondragon-Palomino, O, Tsimring, L. & Hasty, J. A synchronized quorum of genetic clocks. Nature 463, 326-330 (2010).
[2] Arthur Prindle, Phillip Samayoa, Ivan Razinkov, Tal Danino, Lev S. Tsimring & Jeff Hasty. A sensing array of radically coupled genetic 'biopixels'. Nature 481, 39-44 (2012).
[3] William Mather, Matthew R. Bennett, Jeff Hasty and Lev S. Tsimring. Delay-induced degrade-and-fire oscillations in small genetic circuits. Phys. Rev. Lett. 102, 068105 (2009).