Revision as of 02:24, 18 October 2018

Modelling

In order to predict the concentration of different substance in E.coli, we set the kinetic model according to the reaction rate theory and enzymatic reaction kinetics as our first model in our project. And the production and conversion rate model is included to simulate the directed evolution model and the natural evolution model, and then we can get the time we need in our directed evolution method of DTE. The third model we set up is the microfluidics model to predict and simulate the situation in the microfluidics chip, which is our hardware for the gradient concentration of the psicose and antibiotic to let us have a high throughput experiment. The fourth model we set is the market prediction model to predict the future market and the coefficient between different age groups and the tendency to adopt psicose.

Psicose Synthesis Kinetic Model
Production Simulink Model
Market Prediction Model
Hairpin Coupling Model

The Establishment of Psicose Synthesis Kinetic Model

In our design, the DTE process is one of the most significant parts in manufacturing psicose. The main process of psicose manufacture is catalyzed by D-psicose 3-epimerase. The models of device A, B, C and D are as follows.

In device A, extracellular concentration of $psicose$ is higher than the intracellular concentration, so it can enter the cells by diffusion. As a small molecular, the $psicose$ inside the cell can be combined with $pPsiR$ to generate $CCI$. $pPsiR$ is a repressor, which can bind to promoters on DNA and block gene expression. After binding with $psicode$,$pPsiR$ falls off from the promoter and the gene starts expressing, And eventually produce the produce $EGFP$.

For device A, the dynamic equations can be listed as follows:

Concentrations of $psicose$ are different inside and outside the cell, so the diffusion rate of $psicose$ is proportional to the concentration difference between inside and outside of the cells. so the rate at which $psicose$ enters the cell by diffusion is $$-\gamma_F([PsiO]-[PsiI])$$
Where $\gamma_F$ is the diffusion coefficient.

Considering that the volume of the cells is small enough to be negligible relative to the external solution, so the amount of $psicose$ that diffuses into the cells is very small. We can think of the concentration of the external solution as constant, which means $$\frac{\text{d}[PsiO]}{ \text{d}t}=0$$ When reducer psicose combines with repressor, the process is $$\eta_1 PsiI+pPsiR\rightarrow CCI$$ Where $PsiI$ is the intracellular $psicose$, $pPsiR$ is $psicose$ dependent repressor, $CCI$ is $psicose$-repressor complex and $\eta_1$ is coordination number of $psicose$ for $pPsiR$.

The change in concentration of $PsiI$，$pPsiR$ and $CCI$ contains three influencing factors: the binding reaction of $pPsiR$ and $psicose$, and the degradation reaction of themselves and the reverse reaction.

Here we consider the binding reaction as a second order reaction and the degradation reaction and reverse reaction as first order reactions. According to law of mass action, reaction rate is proportional to the product of the reactant concentration, so we have $$\frac{\text{d}[PsiI]}{\text{d}t}=-\gamma_F([PsiI]-[PsiO])-m_{pPsiR,Psi}[PsiI]^{\eta_1}[pPsiR]+m_{CCI}[CCI]-\delta_{PsiI}[PsiI]$$ And considering $pPsiR$ is constantly expressing, we can get $$\frac{\text{d}[pPsiR]}{\text{d}t}=\alpha_{pPsiR}-m_{pPsiR,Psi}[PsiI]^{\eta_1}[pPsiR]+m_{CCI}[CCI]-\delta_{pPsiR}[pPsiR]$$ Where $m_{pPsiR,Psi}$ is the coefficient of reaction rate of the binding reaction, $\alpha_{pPsiR}$ is the rate of constant expression of $pPsiR$, $\delta_{pPsiR}$ and $\delta_{PsiI}$ are coefficients of reaction rate of the degradation reaction of $pPsiR$ and $PsicoseI$ respectively.
The concentration of inactivated repressor is $$\frac{\text{d}[CCI]}{\text{d}t}=m_{pPsiR,Psi}[PsiI]^{\eta_1}[pPsiR]-m_{CCI}[CCI]-\delta_{CCI}[CCI]$$ The change in $EGFP$ concentration depends on the concentration of its repressor, $pPsiR$ which can be described by Hill-equation. Considering $EGFP$ is also degrading, the equation is $$\frac{\text{d}[EGFP]}{\text{d}t}=H\frac{\beta_{EGFP}K^n}{K^n+[pPsiR]^n} -\delta_{EGFP}[EGFP]$$ Where $n$ is hill coefficient, $K$ is the ligand concentration producing half occupation, $\beta_{EGFP}$ is maximal transcription rate of gene $EGFP$, and $H$ is a constant used to indicate the deviation between the theoretical and actual values.

In device B, extracellular concentration of $IPTG$ is higher than which is intracellular, so it can enter the cells by diffusion. As a small molecular, the $IPTG$ inside the cell can be combined with $pLacR$ to generate $CC$. $pLacR$ is a repressor, which can bind to promoters on DNA and block gene expression. After binding with $IPTG$, $pLacR$ falls off from the promoter and the gene starts expressing, and eventually produce the produce $EGFP$. Since the gene of $ABR$ and the gene of $EGFP$ are connected in series, they are expressed together.

Similarly, we can get the function of device B by using the reaction rate equation and the diffusion function. First, $$\frac{\text{d}[IPTGO]}{\text{d}t}=0$$ According to the reaction between $IPTGI$ and $pLacR$ and the reaction rate equation, we can get $$\eta_2 IPTGI+pLacR\rightarrow CC$$ Where $\eta_2$ is coordination number of $IPTG$ for $pLacR$.

This reaction is similar with the reaction in device A, so we have $$\frac{\text{d}[IPTGI]}{\text{d}t}=-\gamma_{IPTG}([IPTGI]-[IPTGO])-m_{IPTG,pLacR}[IPTGI]^{\eta_2}[pLacR]+m_{CC}[CC]-\delta_{IPTG}[IPTGI]$$ Similarly, we can also get $$\frac{\text{d}[pLacR]}{\text{d}t}=\alpha_{pLacR}-m_{IPTG,pLacR}[IPTGI]^{\eta_2}[pLacR]+m_{CC}[CC]-\delta_{pLacR}[pLacR]$$ $$\frac{\text{d}[CC]}{\text{d}t}=m_{IPTG,pLacR}[IPTGI]^{\eta_2}[pLacR]-m_{CC}[CC]-\delta_{CC}[CC]$$ And $pLac$ is a transcription activator of gene $EGFP$, according to hill equation, the concentration of $EGFP$ is $$\frac{\text{d}[EGFP]}{\text{d}t}=H\frac{\beta_{EGFP}K^n}{K^n+[pLacR]^n} -\delta_{EGFP} [EGFP] $$ The concentration of $EGFP$ is the same as the concentration of antibiotics resistance due to the transcription and translation of antibiotic resistance gene combined with the gene of $EGFP$. $$\frac{\text{d}[EGFP]}{\text{d}t}=\frac{\text{d}[ABR]}{\text{d}t}$$ Where $[EGFP]$ is the concentration of $EGFP$ and $[ABR]$ is the concentration of antibiotic resistance protein expression.

Device C is the same with device B. The only difference is the hairpin between gene of $EGFP$ and gene of $RFP$. Similarly, we can get the function of hairpin and its coefficient.

The first few equations are the same as in device B: $$\frac{\text{d}[IPTGO]}{\text{d}t}=0$$ $$\frac{\text{d}[IPTGI]}{\text{d}t}=-\gamma_{IPTG}([IPTGI]-[IPTGO])-m_{IPTG,pLacR}[IPTGI][pLacR]+m_{CC}[CC]-\delta_{IPTG}[IPTGI]$$ $$\frac{\text{d}[pLacR]}{\text{d}t}=\alpha_{pLacR}-m_{IPTG,pLacR}[IPTGI][pLacR]+m_{CC}[CC]-\delta_{pLacR}[pLacR]$$ $$\frac{\text{d}[CC]}{\text{d}t}=m_{IPTG,pLacR}[IPTGI][pLacR]-m_{CC}[CC]-\delta_{CC}[CC]$$ $$\frac{\text{d}[EGFP]}{\text{d}t}=H\frac{\beta_{EGFP}K^n}{K^n+[pLacR]^n} -\delta_{EGFP} [EGFP]$$ The presence of the hairpin leads to a decrease in $RFP$ expression efficiency, so we have $$\frac{\text{d}[EGFP]}{\text{d}t}=k\frac{\text{d}[RFP]}{\text{d}t}$$ Where $[RFP]$ is the concentration of red fluorescence protein, $k$ is the coefficient of hairpin.

In device D, $IPTG$ gets in cells and bind with $pLacR$, which is a repressor for gene $DTE$. After $IPTG$ binding with $pLacR$, $DTE$ starts to express, and as an enzyme, to catalysis $fructose$ to turn into $psicose$. As more and more $psicose$ are produced, more and more the repressor of gene $EGFP$, $pLacR$ are inactivated, so expression of $EGPF$ increase. At the same time, expression of $ABR$ also increase since the gene of $ABR$ and the gene of $EGFP$ are connected in series by a hairpin.

For device D, $psicose$ and $fuctose$ get in cells by diffusion: $$\frac{\text{d}[PsiO]}{\text{d}t}=\frac{-\gamma_{F}([PsiO]-[PsiI])}{V_{outside}}=0 $$ $$\frac{\text{d}[FO]}{\text{d}t}=\frac{-\gamma_{F}([FO]-[FI])}{V_{outside}}=0$$ $$\frac{\text{d}[IPTGO]}{\text{d}t}=\frac{-\gamma_{F}([IPTGO]-[IPTGI])}{V_{outside}}=0$$ And device D consists of device A and device C connected by an extra step:

$$FI\overset{DTE}{\rightarrow}PsiI $$ This reaction is an enzyme catalyzed reaction, which can be described by Michaelis equation, so we can get the following equations: $$\frac{\text{d}[FI]}{\text{d}t}=\gamma_{F}([FI]-[FO])-\frac{k_2[DTE][FI]}{K_M+[FI]}-\delta_{FI}[FI]$$ $$\frac{\text{d}[PsiI]}{\text{d}t}=-\gamma_{F}([PsiI]-[PsiO])+\frac{k_2[DTE][FI]}{K_M+[FI]}-\delta_{PsiI}[PsiI]$$ $$\frac{\text{d}[IPTGI]}{\text{d}t}=-\gamma_{IPTG}([IPTGI]-[IPTGO])+m_{IPTG,pLacR}[IPTGI][pLacR]-\delta_{IPTG}[IPTGI]$$ Where $k_2$ is reaction rate coefficient of transition state product’s decomposition reaction, $K_M$ is the Michaelis contant.
The rest of the equations are the same with which in device A and device C: $$\frac{\text{d}[pLacR]}{\text{d}t}=\alpha_{pLacR}-m_{IPTG,pLacR}[IPTGI][pLacR]+m_{CC}[CC]-\delta_{pLacR}[pLacR]$$ $$\frac{\text{d}[pPsiR]}{\text{d}t}=\alpha_{pPsiR}-m_{pPsiR,PsiI}[pPsiR][PsiI]+m_{CCI}[CCI]-\delta_{pLacR}[pLacR]$$ $$\frac{\text{d}[pPsiR]}{\text{d}t}=\alpha_{pPsiR}-m_{pPsiR,Psi}[PsiI][pPsiR]+m_{CCI}[CCI]-\delta_{pPsiR}[pPsiR]$$ $$\frac{\text{d}[CC]}{\text{d} t}=m_{IPTG,pLacR}[PsiI][pPsiR]-m_{CC}[CC]-\delta_{CC}[CC]$$ $$\frac{\text{d}[CCI]}{\text{d} t}=m_{pSiR}[PsiI][pPsiR]-m_{CCI}[CCI]-\delta_{CCI}[CCI]$$ $$\frac{\text{d}[DTE]}{\text{d}t}=H\frac{\beta_{EGFP}K^n}{K^n+[pLacR]^n}-\delta_{DTE}[DTE]$$ $$\frac{\text{d}[EGFP]}{\text{d}t}=H\frac{\beta_{EGFP}K^n}{K^n+[pPsiR]^n}-\delta_{EGFP}[EGFP]$$ $$\frac{\text{d}[ABR]}{\text{d}t}=k\frac{\text{d}[EGFP]}{\text{d}t}$$ The results are shown in the Results Tab as follows.

Device A Validation

To validate our ODE model, we cooperate with SYSU and share our wetLab data to validate our model and their software CO-RAD. This year, SYSU-Software developed CO-RAD, a Collaborative Optimization platform with multiple functions including Recommendation, Analysis and Design. During the conversion with them, they offered to build ODE system to predict the behavior of device, which can verify our design from another perspective. They built the model with their software CO-RAD, based on our circuit design device A:

In this genetic circuit, PsiR is a predicted LacI family transcription factor with high affinity for D-Psicose. This implies that PsiR is potentially capable of binding a consensus sequence in the promoter region and preventing transcription of the regulated promoters in the absence of D-Psicose. With addition of D-psicose, EGFP can be successfully produced. With their visual designer and the embedded mathematical ODE systems model, they constructed our device A and dynamically analyzed the amounts of materials in it. They pay attention to the expression of EGFP, since its amount can be evaluated in unit of optical density. Related formulas and parameters are shown below: $$\frac{d[PsiR]}{dt} = \frac{k_{PsiR}[gene_{PsiR}]}{1+[Psicose]^{n1}}-d_{PsiR}[PsiR]$$ $$\frac{d[EGFP]}{dt}=\frac{k_{EGFP}[gene_{EGFP}]}{1+[PsiR]^{n2}}-d_{PsiR}[EGFP]$$

Production and Conversion Rate Simulink Model

The Establishment of Production and Conversion Rate Simulink Model

To understand how we get psicose in a deep view, replication, transcription and translation are involved to describe the synthesis of psicose and thus come to our production on a large scale. As we all know, the enzyme will degrade due to the mutation of coding sequence during DNA replication and the transcription error.

Although the rate mutation and transcription error are one in 100 million and one in 10 thousand, the yield of psicose may not decrease because even errors happen in the process of replication and transcription, the type and order of amino acid may not reverse as well as the function of our enzyme which lead to the same catalytic rate of the original one.

However, the butterfly effect tells us that we cannot ignore the small change in our system, even it is little enough for us to ignore. Considering the mutation rate and transcription error, the production of psicose is closely related to the cycling times, error rate and original production. The production of psicose is a function with variables of cycling times, error rate and original production. $$Prd(t)=Prd(t,Times,errRate,oriPrd)$$ Where Times is the cycling times of psicose, errRate is the rate of replication and transcription error, oriPrd is the original production of our system. The errRate is influenced by the replication error rate and the transcription error rate. Meantime, the more replication and transcription errors we get, the lower or higher production we will get. The error rate function is $$errRate(t)=repRat(t)\cdot trsRat(t)$$ Where the replication error rate depends on the length of D-psicose 3-epimerase coding sequence, the multiplication of single error rate define the replication and transcription rate as follows $$repRat(t)=srepRat^{len}(t)$$ The length of mRNA is one third of the cDNA length, here we get $$trsRat(t)=\sqrt[3]{strsRat^{len}(t)}$$ Although errors can result in the decrease of production, the increase of production is also promoting on account of the uncertainty of variation. For a natural evolution system, we consider that the probability of production decrease and increase is equal, decided by the error rate per cycling period. Therefore, the average production growth rate is distributed normally with the mean of 0 and the variance of error rate. The production growth rate is shown as follows $$grsRate \sim N(0,errRate)$$ If we set X as production growth rate,$\sigma^2$ as error rate, then we will get $X \sim N(0,\sigma^2)$ And the probability density function is $$f(x\mid 0,\sigma^2)=\frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{x^2}{2\sigma^2}}$$ The probability of production growth rate is $$P(x_1 < grsRat < x_2)=F(x_2)-F(x_1)=\int_{x_1}^{x_2}f(x)dx$$ The original production is also vital when we simulate the production of our system by reason that the larger our system is, the more psicose we will get in our manufacturing and it makes no sense to compare a certain system with another in a totally different scale. So the standardization and the estimation of original production for psicose is necessary.

There are many variables which have impacts on the original production, such as the quantity of E.coli, the growth rate of E.coli, the conversion rate of psicose and the total amount of substrate which determines the scale of the production system. The original production rate is as follows
$$\frac{\text{d}oriPrd}{\text{d}t}=N\alpha C_{sub}$$ Where $oriPrd$ is the original production of the psicose production system, $N$ is the number of E.coli in the system, $\alpha$ is the conversion rate for D-psicose 3-epimerase, $C_{sub}$ is the concentration of substrate, which represents the concentration of fructose in our production system for psicose.

As for the growth of E.coli, we use Logistic equation to describe the process of E.coli proliferating. $$\frac{\text{d}N}{\text{d}t}=r_1N(1-\frac{N}{r_2})$$

Meanwhile, the concentration of substrate is changing during the process of fermentation in our psicose-production system. We assume the concentration of substrate changes little and can be processed as a constant in order to simplify our process and to find out the discipline of production change, as well as simulating our system in directed evolution method. The concentration of substrate is as follows: $$C_{sub}\approx C_{sub}(t_0)=constant$$ In this way, we can get the original production of psicose from the very beginning. The original production of psicose can be calculated as a constant as below $$oriPrd=\lim_{t_0\rightarrow 0}\int_0^{t_0}\alpha N(t)C_{sub}(t)dt$$ Finally, based on the probability distribution function, the variation of production is defined and we can compare the psicose production in natural system and directed evolution system.

Hairpin Coupling Model

The Establishment of Hairpin Coupling Model

Hairpin coupling model is set up with the help of OUC, we have a good time discussing our hairpin coupling model. Thanks to our collaboration, we can set up a hairpin coupling model as a fresh man. At the very beginning, the most important thing to set up the hairpin coupling model is to determine how can the hairpin structure couple the upstream and downstream expression.

The hairpin structure is the same with the stem-loop structure and forms when there is a similar strand of DNA or RNA due to the base complementary pairing principle. When the hairpin is in the gene and between two coding sequences, the expression levels of these two coding sequence will change because of the hairpin structure. What’s more important is that how can we determine the coupling efficiency based on the hairpin structure.

The hairpin structure is between the two coding sequences and we can use ordinary differential equation to describe the dynamics of genetic circuit. The expression level of protein can be defined as follows: $$\frac{\text{d}[Protein_1]}{ \text{d}t}=k_1-\delta_1 [Protein_1]$$ $$\frac{\text{d}[Protein_2]}{ \text{d}t}=k_2-\delta_2 [Protein_2]$$ When the protein expression level is stable, the expression level of two protein can be defined as: $$Protein_1^{Sta}=\frac{k_1}{\delta_1}$$ $$Protein_2^{Sta}=\frac{k_2}{\delta_2}$$ Then the coupling efficiency can be defined as follows: $$cpEff=\frac{ Protein_2^{Sta}}{ Protein_1^{Sta}}=\frac{\frac{k_2}{\delta_2}}{\frac{k_1}{\delta_1}}=\frac{k_2 \cdot \delta_1}{k_1 \cdot \delta_2}$$ Where $k_1$ means the transcription rate of $protein_1$ and $k_2$ represents the transcription rate of $protein_2$. Due to the research of OUC, we can calculate $k_1$, $k_2$ of coding sequence using a thermodynamics model.

The translation rate of the coding sequence can be defined as the following formula in statistical thermodynamics because with the decrease of Gibbs free energy, the system will become more stable and the ribosome will be more likely binding DNA. Then the $k_1$ can be calculated as $$k_1 \varpropto e^{-\beta \cdot \Delta G_{total1}}$$ We make the formula into an equation: $$k_1 = k_1^{'} e^{-\beta \cdot \Delta G_{total1}}$$ Where $\beta$ is the Boltzmann constant, which is $0.45 \pm 0.05 mol/kcal $, $k_1^{'}$ is the coefficient of the Gibbs free energy function.

The $\Delta G_{total1}$ is the total binding free energy between the ribosome and $5’$ UTR, and can be calculated as follows: $$\Delta G_{total1} =\sum_i \Delta G_i$$ Where $\sum_i \Delta G_i$ means the Gibbs free energy from the different sites interacting with each other, including the free energy of mRNA-rRNA complex, the non-coding sequence between the coding sequence, the free energy of the ribosome-mRNA complex and the free energy of tRNA-ribosome complex.

The free energy above can be calculated by Nucleic Acid Package which is a software for nucleic acid structure and free energy design. Thus, the five parts of free energy can be calculated by the software.

For the calculation of transcription rate $k_2$, we add related factors into $k_2$ as follows to describe the influence of hairpin structure: $$k_2 \varpropto e^{-\beta \Delta G_{total2}}+r_{reinit}$$ Similarly, we make the formula above as an equation: $$ k_2 =k_2^{'}[ e^{-\beta \Delta G_{total2}}+r_{reinit}]$$ Where $k_2^{'}$ is the coefficient of the Gribbs free energy function and the hairpin influence function. And it is easy to calculate $\Delta G_{total2}$ due to the formula above: $$\Delta G_{total2} =\sum_i \Delta G_i$$

As for $r_{reinit}$, the downstream gene is likely to be expressed in a high level because the distance of two genes are short while the downstream gene is also more likely to be terminated because of the more complex hairpin structure it has. So $r_{reinit}$ can be calculated as follows: $$r_{reinit}=k_{dis} \cdot k_{com} e^{-\beta \Delta G_{total2}}$$ Where $\Delta G_{total2} =\sum_i \Delta G_i -\Delta G_{coupling}F_{coupling}$, $k_{dis} $ means the distance between two coding sequences with the hairpin structure and $k_{com}$ means the complexity of the hairpin structure. From the research of OUC, we can know $k_{dis}$ is the same with the intergenic distance dependence as follows: $$k_{dis}=\left\{\begin{matrix} {0.0072 \pm 0.0048 \qquad 0 \le d \le 25}\\ {0.0220 \qquad d=-4}\\ {0.0072+(0.0004)\cdot (d+10) \qquad -10\le d \le 25} \end{matrix}\right.$$ Where $d=n_{nt}-3$ and $n_{nt}$ is the number of single nucleotides between the starting site and the terminating site. For the different hairpin structure, the value of $k_{com}$ is different and is defined as the length of the hairpin structure: $$k_{com}= k_{com}^{'} e^{-\beta \Delta Len G_{total2}}$$ Where $k_{com}^{'}$ is the coefficient of the hairpin complex function and $Len$ is the length of hairpin structure determined by the complementary base pairing. Considering $F_{coupling}=\frac{1}{1+C\cdot e^{- \beta \cdot \Delta G_{total2}}}$ as is shown in the hairpin coupling model by OUC, we can get the coupling efficiency as follows: $$cpEff==\frac{k_2 \cdot \delta_1}{k_1 \cdot \delta_2}=\frac{ \delta_2 \cdot k_2^{'} [ e^{-\beta \Delta G_{total2}}+r_{reinit}]}{\delta_1 \cdot k_1^{'} e^{-\beta \cdot \Delta G_{total1}}}$$

OUC Hairpin Coupling Model

Market Prediction Model

The Establishment of Market Prediction Model

In our market model, we’d like to analyze the relationship between the choice of psicose and the market scale. Then, the market od psicose is predicted. According to our project, another thing we would like to model is to predict the psicose market based on the survey we did in the human practice part. From the results of the survey, we can know that the different questions have different meanings behind the questions.

For different questions, we analyze every question in the questionnaire and we then define different indicators in the questionnaire. Some questions are ignored because they are not available to predict the marker scale. Eventually, after we select the proper questions, we define them as different indicators of the public tastes, which are used to predict the market.

The questions we choose are as follows:

Then for each respondents, the results can be described by a vector as follows $$Result_i=(Qs_{1(i)},Qs_{2(i)},Qs_{3(i)},Qs_{4(i)},Qs_{5(i)},Qs_{6(i)},Qs_{7(i)},Qs_{8(i)})$$ To describe whether the respondent is interested in psicose and tends to buy it, we set a evaluation function to evaluate the degree of intending to buy and taste psicose. Considering question 2,3,8, we think these 3 questions are negative to the purchase of psicose and we process them first before we calculate this three questions. $$Qs_{2(i)}^{'}=\frac{1}{ Qs_{2(i)}}, Qs_{3(i)}^{'}=\frac{1}{ Qs_{3(i)}}, Qs_{6(i)}^{'}=\frac{1}{ Qs_{6(i)}}$$ For the positive questions, which are beneficial for the choice of psicose, we consider the value of them are equal to the question results: $$$Qs_{j(i)}^{'}= Qs_{2(i)},j=1,4,5,7,8$$ Then we can get the evaluation function for each respondent as follows: $$EvF(Result_i)=\sum_{j=1}^8 Qs_{j(i)}^{'}$$ The gender and the age range should also take into consideration. For a certain age range, we can get the average $EvF$ of this age group: $$\mu_{EvF} (AgeGroup)=\frac{1}{Result_i (AgeGroup)}\sum EvF[Result_i (AgeGroup)]$$ Where $AgeGroup$ is divided into 8 groups as the results of the survey.

The results are shown as follows:

From the picture, we know that different age groups have different preference towards psicose and it is better for us to include the influence of the age group distribution. By weighting people of different ages, we get the corresponding market share, and then we can calculate the demand for psicose in different ages. $$PsiDe(AgeGroup,t)=PsiTo(t) \cdot \frac{\mu_{EvF}(AgeGroup)}{\sum \mu_{EvF}(AgeGroup)}$$ Where $PsiDe(t)$ is the demand for psicose in a certain age, $PsiTo(t)$ is the total psicose production. When we aim to research the market situation in a small range, the $EvF(AgeGroup)$ will not change in a short period and the total psicose production can be estimated as the demand for the sugar: $$PsiTo(t)=\alpha_{Psi}\cdot SugTo(t)$$ Where $\alpha_{Psi}$ represents the percentage of psicose to sugar and $SugTo(t)$ is the total demand for sugar.

Basing our model, we can calculate and simulate our system as follows:

From the picture below, it is evident that with time going by, the concentration of IPTG outside the cell will decrease due to the diffusion process, while the RFP expression level is increasing stably.

If we do more experiments shown below, we can find that the expression level of RFP is increasing with the increase of the concentration of IPTG, which proves that we can get a linear range of IPTG to get the RFP expression level. According to device A, B, C, D, this conclusion can also be made, which proves the validation of our kinetic model

From the result above, the SYSU model can prove their simulation has the same trend of the real experiment data, which also gave us a meaningful design performance forecast, which also validate our ODE model.

SYSU Validation Model

With the directed evolution method to produce psicose, what matters is the conversion rate and the scale of the production system. As the results shown above, if we cultivate our system for many times, the conversion rate of DTE is increasing stably with the directed evolution method.

If we do diretecd evolution for more times, we will get the same result and the increase of conversion rate with directed evolution is stable, while the conversion rate of DTE in natural evolution system fluctuate in a certain range.

If we have a system with a stable psicose production of 50,what happens in directed evolution system is the slight increase in the production with the times of producting increasing. Whereas, the production of natural evolution system is still in the original production and vibrate in a small range.

With more simulations ,we can find that although the production is not always increasing all the time, the main stream of the directed evolution is to have a good production of psicose. The production of natural system will increase or decrease due to the vibration of the conversion rate, and if we select the positive strain each evolution process,, we will get the same production results which is the factory method for strain selection.

From the questionnaire we got, we can calculate different $EvF$ in different ages:

Reference

[Kinetic Model]
Golding I, Paulsson J, Zawilski S M, et al. Real-time kinetics of gene activity in individual bacteria.[J]. Cell, 2005, 123(6):1025-1036.
Höfer T, Rasch M J. On the kinetic design of transcription[J]. Genome Informatics, 2005, 16(1):73-82.
De C B, Chen B, Gottesman M, et al. Regulation of lac mRNA synthesis in a soluble cell-free system.[J]. Nature New Biology, 1971, 230(10):37.

[Production Simulink Model]
Schmidt F R. Optimization and scale up of industrial fermentation processes.[J]. Appl Microbiol Biotechnol, 2005, 68(4):425-435.
Lin P Y, Whang L M, Wu Y R, et al. Biological hydrogen production of the genus Clostridium: Metabolic study and mathematical model simulation[J]. International Journal of Hydrogen Energy, 2007, 32(12):1728-1735.
Whang L M, Hsiao C J, Cheng S S. A dual-substrate steady-state model for biological hydrogen production in an anaerobic hydrogen fermentation process[J]. Biotechnology & Bioengineering, 2010, 95(3):492-500.
Rousu J, Elomaa T, Aarts R. Predicting the speed of beer fermentation in laboratory and industrial scale[J]. 1999, 1607.
Hansen L H, Knudsen S, Sørensen S J. The Effect of the lacY Gene on the Induction of IPTG Inducible Promoters, Studied in Escherichia coli and Pseudomonas fluorescens[J]. Current Microbiology, 1998, 36(6):341.

[Hairpin Coupling Model]
Tattersall P, Ward D C. Rolling hairpin model for replication of parvovirus and linear chromosomal DNA[J]. Nature, 1976, 263(5573):106-109.
Briffotaux J, Kobryn K. Preventing Broken Borrelia Telomeres: ResT COUPLES DUAL HAIRPIN TELOMERE FORMATION WITH PRODUCT RELEASE[J]. Journal of Biological Chemistry, 2010, 285(52):41010.

[Market Prediction Model]
Cohen R. A Theoretical Model for Consumer Market Prediction[J]. Sociological Inquiry, 2010, 32(1):43-50.
Jain P C. Relation between Market Model Prediction Errors and Omitted Variables: A Methodological Note[J]. Journal of Accounting Research, 1986, 24(1):187-193.

[Directed Evolution Data]
Schmidtdannert C, Arnold F H. Directed evolution of industrial enzymes.[J]. Trends in Biotechnology, 1999, 17(4):135-136.
Ghadessy F J, Ong J L, Holliger P. Directed evolution of polymerase function by compartmentalized self-replication.[J]. Proceedings of the National Academy of Sciences of the United States of America, 2001, 98(8):4552-4557.
Cherry J R, Lamsa M H, Schneider P, et al. Directed evolution of a fungal peroxidase[J]. Nature Biotechnology, 1999, 17(4):379-384.
Reetz M T, Carballeira J D. Iterative saturation mutagenesis (ISM) for rapid directed evolution of functional enzymes[J]. Nature Protocols, 2007, 2(4):891-903.
You L, Arnold F H. Directed evolution of subtilisin E in Bacillus subtilis to enhance total activity in aqueous dimethylformamide.[J]. Protein Eng, 1996, 9(1):77-83.

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 <div class="page-header" id="section8">
 <div align="center"><h2>Reference</h2></div>
-<blockquote>Schmidt F R. Optimization and scale up of industrial fermentation processes.[J]. Appl Microbiol Biotechnol, 2005, 68(4):425-435.<br>
+<blockquote>
+[Kinetic Model]<br>
+Golding I, Paulsson J, Zawilski S M, et al. Real-time kinetics of gene activity in individual bacteria.[J]. Cell, 2005, 123(6):1025-1036.<br>
+Höfer T, Rasch M J. On the kinetic design of transcription[J]. Genome Informatics, 2005, 16(1):73-82.<br>
+De C B, Chen B, Gottesman M, et al. Regulation of lac mRNA synthesis in a soluble cell-free system.[J]. Nature New Biology, 1971, 230(10):37.<br><br>
+[Production Simulink Model]<br>
+Schmidt F R. Optimization and scale up of industrial fermentation processes.[J]. Appl Microbiol Biotechnol, 2005, 68(4):425-435.<br>
 Lin P Y, Whang L M, Wu Y R, et al. Biological hydrogen production of the genus Clostridium: Metabolic study and mathematical model simulation[J]. International Journal of Hydrogen Energy, 2007, 32(12):1728-1735.<br>
 Whang L M, Hsiao C J, Cheng S S. A dual-substrate steady-state model for biological hydrogen production in an anaerobic hydrogen fermentation process[J]. Biotechnology & Bioengineering, 2010, 95(3):492-500.<br>
-Rousu J, Elomaa T, Aarts R. Predicting the speed of beer fermentation in laboratory and industrial scale[J]. 1999, 1607.
+Rousu J, Elomaa T, Aarts R. Predicting the speed of beer fermentation in laboratory and industrial scale[J]. 1999, 1607.<br>
-Tattersall P, Ward D C. Rolling hairpin model for replication of parvovirus and linear chromosomal DNA[J]. Nature, 1976, 263(5573):106-109.
+Hansen L H, Knudsen S, Sørensen S J. The Effect of the lacY Gene on the Induction of IPTG Inducible Promoters, Studied in Escherichia coli and Pseudomonas fluorescens[J]. Current Microbiology, 1998, 36(6):341.<br><br>
-Briffotaux J, Kobryn K. Preventing Broken Borrelia Telomeres: ResT COUPLES DUAL HAIRPIN TELOMERE FORMATION WITH PRODUCT RELEASE[J]. Journal of Biological Chemistry, 2010, 285(52):41010.
+[Hairpin Coupling Model]<br>
+Tattersall P, Ward D C. Rolling hairpin model for replication of parvovirus and linear chromosomal DNA[J]. Nature, 1976, 263(5573):106-109.<br>
+Briffotaux J, Kobryn K. Preventing Broken Borrelia Telomeres: ResT COUPLES DUAL HAIRPIN TELOMERE FORMATION WITH PRODUCT RELEASE[J]. Journal of Biological Chemistry, 2010, 285(52):41010.<br><br>
+[Market Prediction Model]<br>
+Cohen R. A Theoretical Model for Consumer Market Prediction[J]. Sociological Inquiry, 2010, 32(1):43-50. <br>
+Jain P C. Relation between Market Model Prediction Errors and Omitted Variables: A Methodological Note[J]. Journal of Accounting Research, 1986, 24(1):187-193.<br><br>
+[Directed Evolution Data]<br>
+Schmidtdannert C, Arnold F H. Directed evolution of industrial enzymes.[J]. Trends in Biotechnology, 1999, 17(4):135-136.<br>
+Ghadessy F J, Ong J L, Holliger P. Directed evolution of polymerase function by compartmentalized self-replication.[J]. Proceedings of the National Academy of Sciences of the United States of America, 2001, 98(8):4552-4557.<br>
+Cherry J R, Lamsa M H, Schneider P, et al. Directed evolution of a fungal peroxidase[J]. Nature Biotechnology, 1999, 17(4):379-384.<br>
+Reetz M T, Carballeira J D. Iterative saturation mutagenesis (ISM) for rapid directed evolution of functional enzymes[J]. Nature Protocols, 2007, 2(4):891-903.<br>
+You L, Arnold F H. Directed evolution of subtilisin E in Bacillus subtilis to enhance total activity in aqueous dimethylformamide.[J]. Protein Eng, 1996, 9(1):77-83.<br><br>
 </blockquote>

Difference between revisions of "Team:XJTU-China/Model"