Difference between revisions of "Team:NUS Singapore/Collaborations"

 
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<span><img src="https://static.igem.org/mediawiki/2016/1/1e/T--NUS_Singapore--practice.png" style="width: 30px; height: auto; top: 30px; left: 35px; position: absolute;"><h4 style=" left: 75px; top: 20px; position: absolute; font-size: 17px;">NUS_SINGAPORE</h4></span>
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<span><img src="https://static.igem.org/mediawiki/2016/1/1e/T--NUS_Singapore--practice.png" style="width: 30px; height: auto; top: 30px; left: 35px; position: absolute;"><h4 style=" left: 75px; top: 20px; position: absolute; font-size: 17px; color: white;">NUS_SINGAPORE</h4></span>
 
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<li><a class="icon icon-home" id="menu-item-1" href="https://2016.igem.org/Team:NUS_Singapore">Home</a></li>
 
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<li><a class="icon icon-notebook"  id="menu-item-9" onclick="growDiv('9')"  href="https://2016.igem.org/Team:NUS_Singapore/Notebook">RIOTSensor
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<li><a class="icon icon-notebook"  id="menu-item-9" onclick="growDiv('9')"  href="https://2016.igem.org/Team:NUS_Singapore/Notebook">RIOT Sensor
 
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<li><a class="icon icon-notebook" id="menu-item-5" href="https://2016.igem.org/Team:NUS_Singapore/RIOTResponder">RIOTResponder</a></li>
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<li><a class="icon icon-notebook" id="menu-item-5" href="https://2016.igem.org/Team:NUS_Singapore/RIOTResponder">RIOT Responder</a></li>
<li><a class="icon icon-notebook" id="menu-item-5" href="https://2016.igem.org/Team:NUS_Singapore/RIOTInvader">RIOTInvader</a></li>
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<li><a class="icon icon-notebook" id="menu-item-5" href="https://2016.igem.org/Team:NUS_Singapore/RIOTInvader">RIOT Invader</a></li>
 
<li><a class="icon icon-practice" onclick="growDiv('3')" id="menu-item-3" href="#">Human Practices
 
<li><a class="icon icon-practice" onclick="growDiv('3')" id="menu-item-3" href="#">Human Practices
 
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  <a href="https://2016.igem.org/Team:NUS_Singapore/Collaborations" class="list-group-item" style="color: rgb(179, 179, 179);">Melbourne</a>
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  <a href="https://2016.igem.org/Team:NUS_Singapore/Collaborations#MELBOURNE" class="list-group-item" style="color: rgb(179, 179, 179);">MELBOURNE</a>
  <a href="https://2016.igem.org/Team:NUS_Singapore/Collaborations" class="list-group-item" style="color: rgb(179, 179, 179);">Hong_Kong_HKU</a>
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                                      <a href="https://2016.igem.org/Team:NUS_Singapore/Collaborations#HONG_KONG_HKUST" class="list-group-item" style="color: rgb(179, 179, 179);">HONG_KONG_HKUST</a>
  <a href="https://2016.igem.org/Team:NUS_Singapore/Collaborations" class="list-group-item" style="color: rgb(179, 179, 179);">ETH_Zurich</a>
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                                      <a href="https://2016.igem.org/Team:NUS_Singapore/Collaborations#HONG_KONG_HKU" class="list-group-item" style="color: rgb(179, 179, 179);">HONG_KONG_HKU</a>
 
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<header class="major special">
 
<header class="major special">
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<a name="MELBOURNE"></a>
 
<h2>Collaborations</h2>
 
<h2>Collaborations</h2>
 
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</br></br>
 
In exchange for our assistance with the diffusion dynamics of their system, the Melbourne team assisted us with validating the diffusion solver of the RIOT Model’s inner model component. We provided a test case where lactate was released from the center of the bacteria, with an impermeable bacteria membrane. The Melbourne team showed that the concentration field within the bacteria homogenized within 200 milliseconds (see Figure below), which is what one would expect. This validated the diffusion component of our inner model.
 
In exchange for our assistance with the diffusion dynamics of their system, the Melbourne team assisted us with validating the diffusion solver of the RIOT Model’s inner model component. We provided a test case where lactate was released from the center of the bacteria, with an impermeable bacteria membrane. The Melbourne team showed that the concentration field within the bacteria homogenized within 200 milliseconds (see Figure below), which is what one would expect. This validated the diffusion component of our inner model.
</p></br>
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</p>
 
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<p style="text-align: center;"><img src="https://static.igem.org/mediawiki/2016/8/85/T--NUS_Singapore--collab4.png" style="width: 100%; height:auto;"/></p>
<p style="text-align: center;">Figure: Plot of lactate concentration field in the bacteria in cylindrical coordinates, with respect to time  (plotted by Melbourne team using MATLAB).</p>
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<p style="text-align: center;">Figure: Plot of lactate concentration field in the bacteria in cylindrical coordinates, with respect to time  (plotted by Melbourne team using MATLAB).</p></br>
 
<header>
 
<header>
 
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<table>
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<th colspan="2" style="text-align: center;">University of Melbourne</th>
 +
</tr>
 +
<td>Age distribution</td>
 +
<td style="width: 60%;"><img src="https://static.igem.org/mediawiki/2016/a/a0/T--NUS_Singapore--collab5.png" style="width: 100%; height:auto;"/></td>
 +
<tr>
 +
<td>Receptivity to RIOT System
 +
</td>
 +
<td style="width: 60%;"><img src="https://static.igem.org/mediawiki/2016/e/e1/T--NUS_Singapore--collab6.png" style="width: 100%; height:auto;"/></td>
 +
</tr>
 +
<tr>
 +
<td>Safety perception of RIOT System
 +
</td>
 +
<td style="width: 60%;"><img src="https://static.igem.org/mediawiki/2016/d/d5/T--NUS_Singapore--collab7.png" style="width: 100%; height:auto;"/></td>
 +
</tr>
 +
<tr>
 +
</tr>
 +
</table>
 +
</p>
 +
<a name="HONG_KONG_HKUST"></a>
 
<p>The survey results from Australia illustrated in the above table shows that 32.8% of participants were very receptive to the RIOT System (rating it a ‘5’), despite many of them believing that it is not completely safe - only 6% rated safety with a ‘5’. For more details on the analysis of these results, please refer to the Human Practices (International Outreach).</p>
 
<p>The survey results from Australia illustrated in the above table shows that 32.8% of participants were very receptive to the RIOT System (rating it a ‘5’), despite many of them believing that it is not completely safe - only 6% rated safety with a ‘5’. For more details on the analysis of these results, please refer to the Human Practices (International Outreach).</p>
 
<hr>
 
<hr>
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<p class="medium">RIOT Lactate Sensor</p>
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</a>
</header>
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<p class="medium">Hong_Kong_HKUST </p>
<p align=justify>The RIOT Model was implemented on the lactate sensor system. The full lactate sensor system requires a total of 9 different substances: lactate, pyruvate, LldR with O1O2 promoter, LldR bound to lactate, mRNA of product and product. A series of three reactions are modelled in the system.
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</header>        
</p><br/><br/>
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 +
<p>
 +
We assisted Hong_Kong_HKUST with their model, and they helped us to validate our RIOT Sensor in return.
  
<header>
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</p></br>
 +
<header>
 
<!--h3></h3-->
 
<!--h3></h3-->
  
<p class="medium-small">1) Lactate-pyruvate interconversion</p>
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<p class="medium-small">Modelling </p>
</header>        
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</header>  
<p align=justify>Lactate is constantly being converted into pyruvate and vice versa within the bacteria. According to the work of Hakala, Glaid and Schwert (1995) , the interconversion process can be modelled as follows:
+
<p>The Hong_Kong_HKUST team worked on the tri-stable switch, which required the full treatment of the RIOT Model system. Their intra-bacterial tri-stable switch responds to inducer concentrations in the bacterial environment. As such, the diffusion of the inducers in the environment were modelled in the outer model, and the inducers enter the bacteria at each outer point via free diffusion through the bacterial membrane.
 +
 
 +
</br></br>
 +
Upon implementation, the RIOT Model was sent to the Hong_Kong_HKUST team to experiment with.
 
</p>
 
</p>
<p style="text-align: center;"><img src="https://static.igem.org/mediawiki/2016/1/15/T--NUS_Singapore--modeling4.png" style="width: 40%;"></p>
 
<p>where [Py], [L], [O] and [OR] refers to the concentrations of pyruvate, lactate, diphosphopyridine nucleotide and reduced diphosphopyridine nucleotide respectively. t stands for time. All of the remaining variables are constants. Hakala, Glaid and Schert (1995) <sup>4</sup>determined these parametric constants to be</p>
 
<p style="text-align: center;"><img src="https://static.igem.org/mediawiki/2016/a/a9/T--NUS_Singapore--modeling5.png" style="width: 100%;"></p>
 
<p>Note the assumptions inherent within the constants.
 
By the conservation of particle number, it is clear that:
 
</p>
 
<p style="text-align: center;"><img src="https://static.igem.org/mediawiki/2016/c/c5/T--NUS_Singapore--modeling6.png" style="width: 35%;"></p></br></br>
 
  
                                                        <header>
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</br>
 +
<header>
 
<!--h3></h3-->
 
<!--h3></h3-->
  
<p class="medium-small">2) Lactate interaction with LldR</p>
+
<p class="medium-small">Validation of RIOT Sensor </p>
</header>        
+
</header>  
<p>LldR represses the production of alanine racemase inside the bacteria when bound to either O1 or O2 promoter sites. However, upon being bound to lactate LldR will cease blocking the O1/O2 promoter sites. This interaction can be modelled by:
+
<a name="HONG_KONG_HKU"></a>
 +
<p>The Hong_Kong_HKUST team kindly helped us to measure the sfGFP expression under the control of different lactate sensors over a range of lactate concentrations. However, the bacteria were unable to grow and the blank media had very high readings. Therefore, we could not use these results to compare with ours.
 
</p>
 
</p>
<p style="text-align: center;"><img src="https://static.igem.org/mediawiki/2016/2/2d/T--NUS_Singapore--modeling7.png" style="width: 50%;"></p>
+
</hr>
<p>The disassociation constant of the lldR-lactate complex was taken to be 1000 (μM)<sup>2</sup>. It is believed that the rate reaction constants should be on the order of μM, so all of the relevant constants have been set that.
+
<hr>
</p></br></br>
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<header >
 
+
                                                      <header>
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<!--h3></h3-->
 
<!--h3></h3-->
  
<p class="medium-small">3) Production of alanine racemase</p>
+
<p class="medium">Hong_Kong_HKU </p>
 
</header>         
 
</header>         
<p>Before the production of the alanine racemase can commence, the mRNA sequence of the alanine racemase must first be transcribed. This reaction is modelled by the following equation
+
</p>
+
<p>
<p style="text-align: center;"><img src="https://static.igem.org/mediawiki/2016/1/1c/T--NUS_Singapore--modeling8.png" style="width: 35%;">
+
Hong_Kong_HKU helped us to distribute our survey in Hong Kong.
<p>where [mRNA]  and [P] are the concentrations of the mRNA and freed promoter respectively. k<sub>t</sub> and k<sub>deg</sub> are rate constants of transcription and degradation respectively, both of which are estimated to be 0.1 μM/s. </br></br>
+
The production of alanine racemase is then modelled via
+
  
</p>
+
</p></br>
<p style="text-align: center;"><img src="https://static.igem.org/mediawiki/2016/2/2a/T--NUS_Singapore--modeling9.png" style="width: 35%;">
+
<header>
<p>where [alr] is the concentration of alanine racemase. k<sub>p</sub> is the production rate constant and k<sub>deg2</sub> is the alanine racemase degradation rate constant. Both rate constants are also estimated to be 0.1 μM/s.</br></br>
+
<!--h3></h3-->
Unfortunately, due to time constraints, a bug remains in the model. The von Neumann numerical instabilities inherent within such models are typically problematic. At this moment, the issue lies with the result of a transient solution – transient states of the model can cause numerical singularities quickly. More work must be devoted to deal with this matter.
+
  
</p></br></br>
+
<p class="medium-small">Human Practices: Survey </p>
 +
</header> 
 +
<p>With the help of our collaborators from the Hong_Kong_HKU iGEM team, our survey was distributed in Hong Kong. They distributed our survey in their university, the University of Hong Kong, and to the general public. To cater to language barriers, they also did a Chinese translation of our survey to ensure that the survey participants understood the terminologies used in our survey.
  
<p style="font-size: 0.6em;"> <sup>4</sup>Hakala, Maire T., Andrew J. Glaid, and George W. Schwert. "Lactic dehydrogenase II. Variation of kinetic and equilibrium constants with temperature." Journal of Biological Chemistry 221.1 (1956): 191-210.
 
</p>
 
</hr>
 
<hr>
 
<header>
 
<!--h3></h3-->
 
<p class="medium">RIOT Model Collaboration</p>
 
</header>
 
<p>Given that diffusion and advection are the primary modes of spatial transport in fluids, our RIOT Model tool is very versatile and can be applied to any system that considers bacteria interacting with the environment. Two collaborations have resulted from this RIOT Model tool: one with the Melbourne iGEM team, and another with the HKUST iGEM team.
 
</p><br/></br>
 
<header>
 
<!--h3></h3-->
 
<p class="medium-small">Collaboration with Melbourne iGEM team</p>
 
</header>
 
<p>As part of our collaboration with the Melbourne iGEM team, we implemented an earlier version of the outer model <sup>5</sup> on MATLAB, with the architecture to include their own interaction processes. The Melbourne iGEM team aims to increase the reaction kinetics of the mevalonate pathway using their star peptide.
 
 
</br></br>
 
</br></br>
The implementation of the outer model gave the Melbourne iGEM team insights into the spatial dynamics of the star peptide augmented mevalonate pathway reaction kinetics. More information of the mevanlonate pathway can be found on the Melbourne iGEM Modelling page. The mevalonate pathway and the star peptide enzymes and processes are as illustrated in Figure 3.
+
The raw data obtained from the Hong Kong survey are as follows:
 
</p>
 
</p>
 
+
<p>
<p style="text-align: center;">
+
<table>
<span><img src="https://static.igem.org/mediawiki/2016/3/35/T--NUS_Singapore--modeling10.png" style="width: 49%;"></span>
+
<tr>
<span><img src="https://static.igem.org/mediawiki/2016/e/e9/T--NUS_Singapore--modeling11.png" style="width: 49%;"></span>
+
<th colspan="2" style="text-align: center;">Hong Kong University</th>
 +
</tr>
 +
<td>Age distribution</td>
 +
<td style="width: 60%;"><img src="https://static.igem.org/mediawiki/2016/f/fe/T--NUS_Singapore--collab1.png" style="width: 100%; height:auto;"/></td>
 +
<tr>
 +
<td>Receptivity to RIOT System
 +
</td>
 +
<td style="width: 60%;"><img src="https://static.igem.org/mediawiki/2016/1/14/T--NUS_Singapore--collab2.png" style="width: 100%; height:auto;"/></td>
 +
</tr>
 +
<tr>
 +
<td>Safety perception of RIOT System
 +
</td>
 +
<td style="width: 60%;"><img src="https://static.igem.org/mediawiki/2016/8/80/T--NUS_Singapore--collab3.png" style="width: 100%; height:auto;"/></td>
 +
</tr>
 +
<tr>
 +
</tr>
 +
</table>
 
</p>
 
</p>
<p style="text-align: center;">Figure 3: Mevalonate pathway (left) adapted from <span><a href="https://en.wikipedia.org/wiki/Mevalonate_pathway">https://en.wikipedia.org/wiki/Mevalonate_pathway</a></span> Enzymes attached to and processes involving the star peptide (right).</p>
 
  
<p style="text-align: center;">
+
<p>The survey results from Hong Kong shown in the table above illustrate that 12.5% of surveyees were very receptive to the system (rating with a ‘5’), and 16.7% of them rated safety with a ‘5’. For more details on the analysis of these results, please refer to the Human Practices (International Outreach).</p>
<span><img src="https://static.igem.org/mediawiki/2016/c/c6/T--NUS_Singapore--modeling12.png" style="width: 49%;"></span>
+
</br>
<span><img src="https://static.igem.org/mediawiki/2016/5/5c/T--NUS_Singapore--modeling13.png" style="width: 49%;"></span>
+
</p>
+
<p style="text-align: center;">Figure 4: Sample of the simulation obtained from the collaboration. These concentration fields demonstrate the production and consumption of Acetoacetyl-CoA and HMG-CoA at the relevant nodes.</p>
+
<br/></br>
+
  
<p>After the completion of our combined model, the Melbourne team realized that the contour lines of Figure 4 seems to indicate that the products from one enzyme reaction do not show a clear flow towards the node of the other enzyme reaction, where they will be consumed. Otherwise, the contour lines would trace a pattern that contains both nodes (the contour lines surrounding each node will be joined). This flow was an assumption that the Melbourne team made in the formulation of their project. More simulations are needed to verify the accuracy of that assumption.
 
</p></br></br>
 
 
<header>
 
<!--h3></h3-->
 
<p class="medium-small">Collaboration with Hong_Kong_HKUST iGEM team</p>
 
</header>
 
<p>The Hong_Kong_HKUST team worked on the tri-stable switch, which required the full treatment of the RIOT Model system. Their intra-bacterial tri-stable switch responds to inducer concentrations in the bacterial environment. As such, the diffusion of the inducers in the environment were modelled in the outer model, and the inducers enter the bacteria at each outer point via free diffusion through the bacterial membrane.
 
</br></br>
 
The inner model interaction processes and constants are provided by the HKUST team’s modeller. The three reporter proteins (x,y,z) and the three inducers (i<sub>1</sub>,i<sub>2</sub>,i<sub>3</sub>) interacted through the idealized equations in Figure 5. The implementation of the interaction processes can be found in the “user_bacteria_interactions” function of “user_parameters.c” control script, which can be found in “hkust_collaboration.zip”.
 
</p>
 
<p style="text-align: center;"><img src="https://static.igem.org/mediawiki/2016/d/d6/T--NUS_Singapore--modeling14.png" style="width: 100%;"></p>
 
<p style="text-align: center;">Figure 5: Interaction equations of the HKUST tri-stable switch system.</p>
 
<br/></br></br></br>
 
<p style="font-size: 0.6em;"> <sup>5</sup>Basically, the discretized version of Fick’s second law of diffusion in Cartesian coordinates (differential form). Note that the Forward Time Centered Difference method was employed to compute the time evolution of diffusion in the Melbourne system.
 
</p>
 
<hr/>
 
 
 
                                                        <header>
 
<!--h3></h3-->
 
<p class="medium"><a href="https://static.igem.org/mediawiki/2016/3/3b/T--NUS_Singapore--RIOTSim_Users_Guide.pdf">Click to Read Users' Guide</a></p>
 
</header>
 
 
<hr>
 
<hr>
  

Latest revision as of 17:34, 19 October 2016


Interactive Points | Codrops

Collaborations


Melbourne

We assisted Melbourne with their model, and they helped us to distribute our survey in Australia in return.


Modelling

As part of our collaboration with the Melbourne iGEM team, we implemented an earlier version of the outer model on MATLAB, with the architecture to include their own interaction processes. The Melbourne iGEM team aims to increase the reaction kinetics of the mevalonate pathway using their star peptide.

After the completion of our combined model, the Melbourne team realized that the products from one enzyme reaction on one star peptide node do not show a clear flow towards the node of the other enzyme reaction, where they will be consumed. Otherwise, the contour lines would trace a pattern that contains both nodes (the contour lines surrounding each node will be joined). This flow was an assumption that the Melbourne team made in the formulation of their project. More simulations are needed to verify the accuracy of that assumption.

In exchange for our assistance with the diffusion dynamics of their system, the Melbourne team assisted us with validating the diffusion solver of the RIOT Model’s inner model component. We provided a test case where lactate was released from the center of the bacteria, with an impermeable bacteria membrane. The Melbourne team showed that the concentration field within the bacteria homogenized within 200 milliseconds (see Figure below), which is what one would expect. This validated the diffusion component of our inner model.

Figure: Plot of lactate concentration field in the bacteria in cylindrical coordinates, with respect to time (plotted by Melbourne team using MATLAB).


Human Practices: Survey

With the help of our collaborators from the Melbourne iGEM team, our survey was distributed in Australia via an online link.

The raw data obtained from the Australian survey are as follows:

University of Melbourne
Age distribution
Receptivity to RIOT System
Safety perception of RIOT System

The survey results from Australia illustrated in the above table shows that 32.8% of participants were very receptive to the RIOT System (rating it a ‘5’), despite many of them believing that it is not completely safe - only 6% rated safety with a ‘5’. For more details on the analysis of these results, please refer to the Human Practices (International Outreach).


Hong_Kong_HKUST

We assisted Hong_Kong_HKUST with their model, and they helped us to validate our RIOT Sensor in return.


Modelling

The Hong_Kong_HKUST team worked on the tri-stable switch, which required the full treatment of the RIOT Model system. Their intra-bacterial tri-stable switch responds to inducer concentrations in the bacterial environment. As such, the diffusion of the inducers in the environment were modelled in the outer model, and the inducers enter the bacteria at each outer point via free diffusion through the bacterial membrane.

Upon implementation, the RIOT Model was sent to the Hong_Kong_HKUST team to experiment with.


Validation of RIOT Sensor

The Hong_Kong_HKUST team kindly helped us to measure the sfGFP expression under the control of different lactate sensors over a range of lactate concentrations. However, the bacteria were unable to grow and the blank media had very high readings. Therefore, we could not use these results to compare with ours.


Hong_Kong_HKU

Hong_Kong_HKU helped us to distribute our survey in Hong Kong.


Human Practices: Survey

With the help of our collaborators from the Hong_Kong_HKU iGEM team, our survey was distributed in Hong Kong. They distributed our survey in their university, the University of Hong Kong, and to the general public. To cater to language barriers, they also did a Chinese translation of our survey to ensure that the survey participants understood the terminologies used in our survey.

The raw data obtained from the Hong Kong survey are as follows:

Hong Kong University
Age distribution
Receptivity to RIOT System
Safety perception of RIOT System

The survey results from Hong Kong shown in the table above illustrate that 12.5% of surveyees were very receptive to the system (rating with a ‘5’), and 16.7% of them rated safety with a ‘5’. For more details on the analysis of these results, please refer to the Human Practices (International Outreach).



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