Difference between revisions of "Team:Rice/Modeling"

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  <h3>project description what is this i dont know</h3>
 
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  <p> The photoacoustic effect describes the conversion of electromagnetic energy to mechanical energy, namely, that an object absorbing non-ionizing laser pulses experiences local thermal expansions, and vibrates with frequencies in the ultrasonic range which may be detected. Imaging based on this effect yields high contrast from the optical component, and high resolution from the acoustic component (1). For biomedical purposes, users of this technique take advantage of endogenous and exogenous contrast agents to obtain physiological information from the biological tissue, endogenous examples including oxy- and deoxy-hemoglobin to determine blood flow speed (2). The bacterial pigment Violacein (Vio) has been reported to be an effective contrast agent under this technique (3). Furthermore, previous iGEM teams have developed and optimized a biosynthesis pathway for this pigment (4, 5).
 
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This team seeks to build upon, and move forward from, these past investigations and develop a biosensor in E. coli to produce Violacein in the presence of significant concentrations of biomarkers for disease; naturally, the team’s search for potential biomarkers will be for those which may pass through an animal’s or human’s gastrointestinal tract. This team considers the additional design aspect of logic gates to modulate the specificity for our system. Alongside Violacein, this team will experiment with similar genetic circuits using the fluorescent protein iRFP. For the purposes of testing the resultant system(s), this team has made an arrangement with a group at MD Anderson who can introduce our bacteria into mice, and who have photoacoustic imaging equipment to image the bacteria within the gastrointestinal tracts of the mice.</p>
 
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<br>
 
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<p>
 
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References
 
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Jun X, Junjia Y, and Lihong VW. “Photoacoustic Tomography: Principles and Advances.” Progress in Electromagnetics Research 147:1-22, 2014.
 
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Fang H, Maslov K, and Wang LV. “Photoacoustic Doppler Effect from Flowing Small Light-Absorbing Particles.” Physical Review Letters 99:184501, 2007.
 
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Yuanyuan J, et al. “Violacein as a Genetically-Controlled, Enzymatically Amplified and Photobleaching-Resistance Chromophore for Optoacoustic Bacterial Imaging.” Nature.com. Nature Publishing Group. 19 June 2015. Web. 18 May 2016.
 
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E. Chromi. 2009. (18 May 2016; https://2009.igem.org/Team:Cambridge)
 
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USCF iGEM 2012. 2012. (18 May 2016; https://2012.igem.org/Team:UCSF)
 
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  <div class='h1'>Introduction</div>
  
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<!--
+
  <div class='para'>
 +
  Violacein is a fluorescent reporter with anticancer activity (Ref) that has been
 +
  used in several other igem projects (Cambridge 2009, Slovenia 2010, Johns Hopkins 2011,
 +
  UCSF 2012). Although it would be a good pigment candidate for our project, it has a
 +
  complex synthetic pathway requiring five specialized enzymes, and oxygen (Fig 1.)
 +
  (Michael E. Lee et al, 2013). It also presents multiple off-path reactions that can
 +
  reduce the efficiency of the pathway. Before building constructs to use for violacein
 +
  production, we needed to find a way to determine which promoters to use for the five
 +
  genes involved in the pathway. Although there are studies focused on the optimization
 +
  of the production of violacein, none of the studies gives a biochemical model of the
 +
  rates of the reactions that take place in the bacteria (Ref).
 +
  </div>
  
  
 +
  <div class='h1'>Objective</div>
  
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<div class="column full_size">
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<div class='para'>
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Create a biochemical model of the violacein production based on the synthetic
 +
pathway and violacein production data from bacteria with different promoters
 +
for each of the five genes involved in the pathway.
 +
</div>
  
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<p>Tell us about your project, describe what moves you and why this is something important for your team.</p>
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  <div class='h1'>Model assumptions</div>
  
 +
<div class='para'>
 +
<ol>
 +
<li>The rate of dilution of the enzymes and the intermediaries is much greater than
 +
its degradation (for example by ubiquitination for the proteins or by conversion
 +
to products not included on the pathway)</li>
 +
<li>There is no saturation of the enzymes and all the reactions will follow the law
 +
of mass action</li>
 +
<li>Independence of external factors such as oxygen and NADH in the reactions</li>
 +
<li>None of the reactions are reversible</li>
 +
</ol>
  
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<h5>What should this page contain?</h5>
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We use the mass action kinetics because this type of equation
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<ul>
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only requires one parameter for reaction and is less susceptible to overdosing
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<li> A clear and concise description of your project.</li>
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−
<li>A detailed explanation of why your team chose to work on this particular project.</li>
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−
<li>References and sources to document your research.</li>
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<li>Use illustrations and other visual resources to explain your project.</li>
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</ul>
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−
 
+
  
 
</div>
 
</div>
  
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<div class="column full_size" >
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<div class='h1'>Model Building Process</div>
  
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<h5>Advice on writing your Project Description</h5>
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<div class='para'>
 +
<div class='h3'>1. Modeling Promoter Strength</div>
 +
Because a major goal of the model is to predict the effects of the selection of
 +
promoters on the final production of violacein, we decided to find a way to
 +
characterize promoters first. To simplify the computation, we used the promoter
 +
strength as a single standard to characterize the promoters. Moreover, we assumed
 +
the degradation rate of proteins only depends on the growth rate of E.coli. Then,
 +
every enzyme has the same degradation rate. The bacteriophage T7 promoter
 +
has been widely used for protein expression and purification (J. Andrew Jones
 +
et al., 2013), so we used data of five mutant T7 promoters to create a
 +
proof-of-concept model. If this model is functional, we can implement the same
 +
modeling technique to the promoters we are working with.
  
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<p>
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The five mutant T7 promoters have distinct promoter strength over time after
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We encourage you to put up a lot of information and content on your wiki, but we also encourage you to include summaries as much as possible. If you think of the sections in your project description as the sections in a publication, you should try to be consist, accurate and unambiguous in your achievements.  
+
induction. The experimental data are shown in the figure below.
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</p>
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<p>
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<br><br>
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Judges like to read your wiki and know exactly what you have achieved. This is how you should think about these sections; from the point of view of the judge evaluating you at the end of the year.
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<img src="https://static.igem.org/mediawiki/2016/3/32/Promoter_Strengh_vs_Time_paper.png">
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</p>
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</div>
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<br><br>
  
 +
The first step of our model is to describe the rate of change of enzymes based
 +
on promoter strength. Here we assumed that the enzyme production rate is
 +
directly proportional to strength of the promoter. Therefore, we were
 +
able to use a mass-action kinetics equation of promoters to describe
 +
the enzyme concentration. The equation is shown below:
  
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<div class="column half_size" >
+
<br><br>
 +
<img src="Promoter Equation.JPG">
 +
<br><br>
  
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<h5>References</h5>
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In this equation, Ai is the concentration of enzyme i, ki­ is the
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<p>iGEM teams are encouraged to record references you use during the course of your research. They should be posted somewhere on your wiki so that judges and other visitors can see how you thought about your project and what works inspired you.</p>
+
production rate of each  enzyme i, kd is the degradation rate of all
 +
enzymes, and t is time. By solving this equation, we derived the
 +
equation of enzyme concentration against time.
  
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</div>
+
<br><br>
 +
<img src="Promoter ODE.JPG">
 +
<br><br>
  
 +
Since we assumed that the promoter strength is proportional to the promoter
 +
concentration, we can use the equation to fit our data using least
 +
squares method. The regression lines are overlaid on the data.
  
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<div class="column half_size" >
+
<br><br>
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<h5>Inspiration</h5>
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<img src="Fitted Lines of Promoter Strength vs Time.png">
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<p>See how other teams have described and presented their projects: </p>
+
<br><br>
 +
 
 +
In the plot, circles represent data from paper. (J. Andrew Jones et al., 2013). The solid lines are regression lines. In general the regression lines are able to capture the change of strength of each enzyme over time. In this way, the parameters are determined. The table below lists the parameter values.
 +
 
 +
<br><br>
 +
<img src="Promoter Strength Fit Parameters.png">
 +
<br><br>
 +
 
 +
In the table, ki­ (i = 1,2,3,4,5) are the production rate coefficients of promoter I (i = 1,2,3,4,5), and kd is the degradation rate coefficient of all promoters.
  
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<ul>
 
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<li><a href="https://2014.igem.org/Team:Imperial/Project"> Imperial</a></li>
 
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<li><a href="https://2014.igem.org/Team:UC_Davis/Project_Overview"> UC Davis</a></li>
 
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<li><a href="https://2014.igem.org/Team:SYSU-Software/Overview">SYSU Software</a></li>
 
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</ul>
 
 
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Revision as of 03:14, 19 October 2016


Introduction
Violacein is a fluorescent reporter with anticancer activity (Ref) that has been used in several other igem projects (Cambridge 2009, Slovenia 2010, Johns Hopkins 2011, UCSF 2012). Although it would be a good pigment candidate for our project, it has a complex synthetic pathway requiring five specialized enzymes, and oxygen (Fig 1.) (Michael E. Lee et al, 2013). It also presents multiple off-path reactions that can reduce the efficiency of the pathway. Before building constructs to use for violacein production, we needed to find a way to determine which promoters to use for the five genes involved in the pathway. Although there are studies focused on the optimization of the production of violacein, none of the studies gives a biochemical model of the rates of the reactions that take place in the bacteria (Ref).
Objective
Create a biochemical model of the violacein production based on the synthetic pathway and violacein production data from bacteria with different promoters for each of the five genes involved in the pathway.
Model assumptions
  1. The rate of dilution of the enzymes and the intermediaries is much greater than its degradation (for example by ubiquitination for the proteins or by conversion to products not included on the pathway)
  2. There is no saturation of the enzymes and all the reactions will follow the law of mass action
  3. Independence of external factors such as oxygen and NADH in the reactions
  4. None of the reactions are reversible
We use the mass action kinetics because this type of equation only requires one parameter for reaction and is less susceptible to overdosing
Model Building Process
1. Modeling Promoter Strength
Because a major goal of the model is to predict the effects of the selection of promoters on the final production of violacein, we decided to find a way to characterize promoters first. To simplify the computation, we used the promoter strength as a single standard to characterize the promoters. Moreover, we assumed the degradation rate of proteins only depends on the growth rate of E.coli. Then, every enzyme has the same degradation rate. The bacteriophage T7 promoter has been widely used for protein expression and purification (J. Andrew Jones et al., 2013), so we used data of five mutant T7 promoters to create a proof-of-concept model. If this model is functional, we can implement the same modeling technique to the promoters we are working with. The five mutant T7 promoters have distinct promoter strength over time after induction. The experimental data are shown in the figure below.



The first step of our model is to describe the rate of change of enzymes based on promoter strength. Here we assumed that the enzyme production rate is directly proportional to strength of the promoter. Therefore, we were able to use a mass-action kinetics equation of promoters to describe the enzyme concentration. The equation is shown below:



In this equation, Ai is the concentration of enzyme i, ki­ is the production rate of each enzyme i, kd is the degradation rate of all enzymes, and t is time. By solving this equation, we derived the equation of enzyme concentration against time.



Since we assumed that the promoter strength is proportional to the promoter concentration, we can use the equation to fit our data using least squares method. The regression lines are overlaid on the data.



In the plot, circles represent data from paper. (J. Andrew Jones et al., 2013). The solid lines are regression lines. In general the regression lines are able to capture the change of strength of each enzyme over time. In this way, the parameters are determined. The table below lists the parameter values.



In the table, ki­ (i = 1,2,3,4,5) are the production rate coefficients of promoter I (i = 1,2,3,4,5), and kd is the degradation rate coefficient of all promoters.