Team:WHU-China/templates/standardpage modeling

From 2013.igem.org

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<h1 style="color:green;"><strong>Tandem Promoter 总纲</strong></h1></br>
 
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</br>
 
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<strong>目的:</strong>通过tandem promoter组成预测promtoer最终强度。</br>
 
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</br>
 
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promoter的强度由多种因素决定,我们要一一审查这些因素,以确定在tandem promoter中真正能导致promoter强度变化的因素。</br>
 
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</br>
 
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<h1 style="width:65%;"></h1></br>
 
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<strong>部分一、</strong>用荧光强度表达单位细胞体积内GFP数的合理性</br>
 
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我们检测的是荧光强度,在激发光强度一定的情况下荧光强度可以直接体现蛋白质的密度(引用1),荧光蛋白在翻译之后短时间内即开始发光,故荧光可以表
 
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示溶液中GFP密度。</br>
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故有 GFP的密度(荧光)*溶液体积/细胞总体积(OD)=单位细胞体积内GFP数。</br>
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<h1 style="width:60%;font-size:30px;color:green;margin-left:26%;border:0;margin-top:40px;"><b>
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所以荧光可以体现单位细胞体积内GFP数。</br>
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1. Overview</b></h1></br>
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This model aims at predicting the final output of a tandem promoter system, which can be constituted of any number of and any type of sub-promoter(including sub-tandem promoter) in any order and any species. The Key idea of the model is that the strength of a promoter system is proportional to the probability of at least one RNA Polymerase (mentioned as RNAP latter) binding on the promoter. </br></br>
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<h1 style="width:60%;font-size:30px;color:green;margin-left:26%;border:0;margin-top:40px;"><b>
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2. Symbol table, Assumption and reasons.</b></h1></br>
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<img src="https://static.igem.org/mediawiki/2013/f/f2/WHUTable1a.png" />
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<img src="https://static.igem.org/mediawiki/2013/4/47/WHUTable1b.png" />
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<ul><li>
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1.It’s assumed that the promoter strength is measured in the same species, with identical environment and growing stage. This ensure the assumption that the concentration of all subunits of RNAP, all subunits of ribosome, all RNA degradation enzymes, all kind of proteases and all transportation protein are thermodynamically identical. Otherwise, the model may fail to work properly.</li><li>
 +
2.In all measurement, the contexts of the promoter are the same. i.e. same RBS, terminator, protein sequence, up stream element, down stream element and DNA supercoiling. </li><li>
 +
3.All transcriptional factors are not considered in this version of the model, but can be included in the model with some modification to the equations. </li><li>
 +
4.The promoter region is accessible for RNAP(and all kinds of its subunits), which means it’s not in heterochromatin region or any other condition that hamper a normal RNAP-DNA interaction. </li><li>
 +
5.The probability of RNAP binding on the region between two sub-promoter within the tandem promoter system is neglected. As it contributes too little to final ptot. </li><li>
 +
6.The RNAP-DNA binding is assumed to stay on equilibrium in the model. This is reasonable because the open complex formation is a slow rate limiting step of transcription. So in the time scale of open complex formation, RNAP-DNA binding can always reach its equilibrium in neglectable time[1][2]. It’s also observed that the inactive RNAP-DNA complex can be detected on the DNA[3]. </li><li>
 +
7.We assume different RNAP-Promoter complexes have a transcription rate α for simplicity. Because if they do not, the difference of α can be incorporated in pi. For derivation, see section 4.2 and 4.3. </li>
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</ul>
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</br></br></br>
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h1 style="width:60%;font-size:30px;color:green;margin-left:26%;border:0;margin-top:40px;"><b>
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3. Modeling result</b></h1></br>
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We found that the strength of a tandem promoter system can be interpreted by a simple equation:</br>
 +
<img src="https://static.igem.org/mediawiki/2013/6/6f/WHU_2013Equation1.png" align=center />
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</br>Where qi is the probability of a RNAP(with all of its subunit) not forming a RNAP-with complex with the ith sub-promoter, n the number of sub-promoters, j the coordinative factor, and ξ the strength constant.</br>
</br>
</br>
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<h1 style="width:65%;"></h1></br>
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If we define the highest possible expression level of a promoter in certain species is 1. Then the equation 1 become normalized. </br>
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<strong>部分二、</strong>翻译过程</br>
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<img src="https://static.igem.org/mediawiki/2013/7/76/WHU_2013Equation2.png" align=center />
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<h6 style="text-align:center;color:red;">dp/dt=v*m-k.p</h6></br>
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GFP降解取决于protease,可以把protease浓度可视为常量,其降解速率相对转录-翻译的时间尺度更长(引用2 sysbio导论第一章,3 PNAS 2002 用GFP系统
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研究promoter强度 ),故可以假设为一个有较小系数k(depend on protein)的一阶函数(引用4 PNAS 2005)。又mRNA翻译速率取决于mRNA量和
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This model explains 99% of the tandem promoter strength variation caused by</br>
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<ul><li>
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1.number of sub-promoter, </li><li>
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2.kind of sub-promoter, </li><li>
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3.order of sub-promoter . </li></ul></br>
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(With a R-square=0.992 and confidence bond of 95% when fitted with our data)</br>
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<div id="figcontainer" style="width:400px;height:auto;float:right;text-align:left;"><img src="https://static.igem.org/mediawiki/2013/1/13/WHUTpfigure1.png" width=400px />
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<em>
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Figure 1. Model fitting result</br>
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Y-axis represent the normalized promoter strength, X-axis the number of sub-promoter</br>
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The blue dot is data extracted from ref.[4] fig.2, the red line is the prediction made by our model</br>, the red dotted line is the 95% prediction bound</br>
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</em>
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</div>
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Ribosome、a.a.密度(视为常数)。</br>
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</br></br></br></br>
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(wait for latter data) </br></br></br>
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<h1 style="width:60%;font-size:30px;color:green;margin-left:26%;border:0;margin-top:40px;"><b>
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4.Model derivation</b></h1></br>
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The promoter strength may be influenced by various factors. We need to simplify the system into some reasonable toy model by wiping out all relatively trivial factor. </br>
</br>
</br>
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<h1 style="width:65%;"></h1></br>
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<b>
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<strong>部分三、</strong>转录过程</br>
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4.1 Expression level Measurement</b></br>
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<h6 style="text-align:center;color:red">dm/dt=alpha*(RNAP-DNA complex)-lambda*m</h6></br>
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We use the fluorescence strength to indicate the strength of the promoter(Normalized by a inner reference fluorescence protein(FP) - mCherry. Please check details at the experiment part 网址). Because when the exciting light is fixed, the fluorescence is proportional to the concentration of FP. And FP can be lighted up in a short time after they are synthesis.</br>  
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mRNA降解依赖于RNase,而各种RNase浓度可视为常数(引用5 mRNA decay),也可设为一阶函数。mRNA的产生取决于RNAP-DNA结合体进入open
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</br><b>
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complex并启动转录的速率,故是一关于RNAP-DNA结合体浓度的函数。由于RNAP-DNA结合的过程相对Open complex的形成过程快很多,故可以视为在
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4.2 Translation and transcription</b>
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RNAP+DNA=RNAP-DNA总是在转录和翻译的时间尺度上是保持平衡状态(稳态)的(引用2 引用5)。 体外实验也证明,在RNAP与DNA浓度一定的情况下,在
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According to the Central Dogma.</br>
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极短的时间之后mRNA的产生速率(用UTP消耗率监测)是一阶函数(引用5 Open Complex)。而在体内我们亦可视此二量恒定,故RNA的产生速率是
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RNAP-DNA浓度的一阶函数。</br>
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So we can write down the following ODE, which is similar to the equations in [5].</br>
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</br>
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<img src="https://static.igem.org/mediawiki/2013/5/50/WHU2013Equation3-4.png" />
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<h1 style="width:65%;"></h1></br>
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<strong>部分四、</strong>RNAP与DNA结合过程</br>
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因为我们视RNAP+DNA=RNAP-DNA一直保持平衡状态。所以我们只需要研究RNAP结合到promoter的几率即可。</br>
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而RNAP结合到promoter的几率是</br>
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(RNAP结合到promtoer的热力学能的boltzman函数)/(所有可能的结合的热力学能的boltzman函数的和)(引用6
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Promoter model a)</br>
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Where α means the mRNA producing constant, λ the mRNA degradation constant, v the protein synthesizing, k the protein degradation constant, and [RP] is the concentration of RNAP-promoter promoter.</br>
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通过计算,我们可以得到结论,对于特定类型的promoter,RNAP结合到特定位置promtoer的概率是一定的。</br>
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但是对于转录而言,只有最靠近起始密码子的promoter可以启动转录。由此对tandem promoter而言,其形成RNAP-DNA complex的概率是至少有一个sub
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promoter上有RNAP的概率。</br>
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In equation 4, the protein increasing speed is determined by [mRNA] and v. With same RBS, v relates to the efficiency and concentration of ribosome and concentration of amino acids in the cell, which can be considered identical under the experiment condition of comparing different promoter. The protein degradation speed is determined by [protein] and k. k relates to protease system in the cell, which can also be considered as identical in measurements between different promoter.</br>
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由此我们求得(RNAP-DNA complex)相对于不同promoter及不同数目promoter的函数G。</br>
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</br>
</br>
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<h1 style="width:65%;"></h1></br>
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In equation 3, the mRNA increasing speed is determined by [RP] and α, and its degradation depends on [mRNA] and λ. Both α and λ can be treated as constant in the experimental condition of comparing different promoter. As α depends on the transcription initiation efficiency, which is assumed to be identical for any RNAP-DNA complex for simplicity. This is reasonable because if α varies, the difference of α can be incorporated in [RP] (and finally in pi, see latter derivation). Though this part of the equation varies from the equations in [5], it is justified by the phenomenon that when [RNAP] and [DNA] is hold in a constant, the UTP incorporation is a zero order reaction [2]. And λ depends on the concentration of RNase which doesn’t varies in different promoter measurement.</br>
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<strong>部分五、</strong>整合分析</br>
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此时我们已经得到</br>
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<h6 style="text-align:center;color:red">dm/dt=alpha*G-lambda*m;</h6></br>
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<h6 style="text-align:center;color:red">dp/dt=v*m-k.p;</h6></br>
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并可以求得p的浓度-时间方程用于GFP动态数据拟合。</br>
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</br>
</br>
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对于代谢工程,我们关注的是稳态下通路中蛋白的表达量。</br>
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Therefore, because we are interested in the steady state of the protein expression. We can set,</br>
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故有<h6 style="text-align:center;color:red">m(max)=alpha*G/lambda</h6></br>
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<img src="https://static.igem.org/mediawiki/2013/a/a3/WHU_2013Diff1.png" />
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<h6 style="text-align:center;color:red">p(max)=v*m/k=v*alpha*G/lambda/k</h6></br>
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注意到p是G的单变量函数,故由double promoter系统可以有效调控代谢通路中的Metabolic Flux</br></br>
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<h1 style="width:65%;"></h1></br></br>
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Revision as of 11:12, 22 September 2013

1. Overview


This model aims at predicting the final output of a tandem promoter system, which can be constituted of any number of and any type of sub-promoter(including sub-tandem promoter) in any order and any species. The Key idea of the model is that the strength of a promoter system is proportional to the probability of at least one RNA Polymerase (mentioned as RNAP latter) binding on the promoter.

2. Symbol table, Assumption and reasons.


  • 1.It’s assumed that the promoter strength is measured in the same species, with identical environment and growing stage. This ensure the assumption that the concentration of all subunits of RNAP, all subunits of ribosome, all RNA degradation enzymes, all kind of proteases and all transportation protein are thermodynamically identical. Otherwise, the model may fail to work properly.
  • 2.In all measurement, the contexts of the promoter are the same. i.e. same RBS, terminator, protein sequence, up stream element, down stream element and DNA supercoiling.
  • 3.All transcriptional factors are not considered in this version of the model, but can be included in the model with some modification to the equations.
  • 4.The promoter region is accessible for RNAP(and all kinds of its subunits), which means it’s not in heterochromatin region or any other condition that hamper a normal RNAP-DNA interaction.
  • 5.The probability of RNAP binding on the region between two sub-promoter within the tandem promoter system is neglected. As it contributes too little to final ptot.
  • 6.The RNAP-DNA binding is assumed to stay on equilibrium in the model. This is reasonable because the open complex formation is a slow rate limiting step of transcription. So in the time scale of open complex formation, RNAP-DNA binding can always reach its equilibrium in neglectable time[1][2]. It’s also observed that the inactive RNAP-DNA complex can be detected on the DNA[3].
  • 7.We assume different RNAP-Promoter complexes have a transcription rate α for simplicity. Because if they do not, the difference of α can be incorporated in pi. For derivation, see section 4.2 and 4.3.



h1 style="width:60%;font-size:30px;color:green;margin-left:26%;border:0;margin-top:40px;"> 3. Modeling result
We found that the strength of a tandem promoter system can be interpreted by a simple equation:

Where qi is the probability of a RNAP(with all of its subunit) not forming a RNAP-with complex with the ith sub-promoter, n the number of sub-promoters, j the coordinative factor, and ξ the strength constant.

If we define the highest possible expression level of a promoter in certain species is 1. Then the equation 1 become normalized.
This model explains 99% of the tandem promoter strength variation caused by
  • 1.number of sub-promoter,
  • 2.kind of sub-promoter,
  • 3.order of sub-promoter .

(With a R-square=0.992 and confidence bond of 95% when fitted with our data)
Figure 1. Model fitting result
Y-axis represent the normalized promoter strength, X-axis the number of sub-promoter
The blue dot is data extracted from ref.[4] fig.2, the red line is the prediction made by our model
, the red dotted line is the 95% prediction bound




(wait for latter data)


4.Model derivation


The promoter strength may be influenced by various factors. We need to simplify the system into some reasonable toy model by wiping out all relatively trivial factor.

4.1 Expression level Measurement
We use the fluorescence strength to indicate the strength of the promoter(Normalized by a inner reference fluorescence protein(FP) - mCherry. Please check details at the experiment part 网址). Because when the exciting light is fixed, the fluorescence is proportional to the concentration of FP. And FP can be lighted up in a short time after they are synthesis.

4.2 Translation and transcription According to the Central Dogma.
So we can write down the following ODE, which is similar to the equations in [5].
Where α means the mRNA producing constant, λ the mRNA degradation constant, v the protein synthesizing, k the protein degradation constant, and [RP] is the concentration of RNAP-promoter promoter.
In equation 4, the protein increasing speed is determined by [mRNA] and v. With same RBS, v relates to the efficiency and concentration of ribosome and concentration of amino acids in the cell, which can be considered identical under the experiment condition of comparing different promoter. The protein degradation speed is determined by [protein] and k. k relates to protease system in the cell, which can also be considered as identical in measurements between different promoter.

In equation 3, the mRNA increasing speed is determined by [RP] and α, and its degradation depends on [mRNA] and λ. Both α and λ can be treated as constant in the experimental condition of comparing different promoter. As α depends on the transcription initiation efficiency, which is assumed to be identical for any RNAP-DNA complex for simplicity. This is reasonable because if α varies, the difference of α can be incorporated in [RP] (and finally in pi, see latter derivation). Though this part of the equation varies from the equations in [5], it is justified by the phenomenon that when [RNAP] and [DNA] is hold in a constant, the UTP incorporation is a zero order reaction [2]. And λ depends on the concentration of RNase which doesn’t varies in different promoter measurement.

Therefore, because we are interested in the steady state of the protein expression. We can set,