diff --git a/content/12.model.md b/content/12.model.md
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@@ -7,7 +7,7 @@
 <br>
 
 
-Our main goal is to filter out a promoter sequence with the best distinction under conditions with or without environmental oxygen, so that the survival of E.coli differs most in the tumor and the normal homeoenvironment. To achieve this goal, we develop <strong><font color=#8B0012>H</font></strong>ybrid <strong><font color=#8B0012>P</font></strong>romoter <strong><font color=#8B0012>P</font></strong>redicting (HPP) model. We divide the whole task into the following 3 steps:
+Our main goal is to filter out a promoter sequence with the best distinction under conditions with or without environmental oxygen, so that the survival of E.coli differs most in the tumour and the normal homeoenvironment. To achieve this goal, we develop <strong><font color=#8B0012>H</font></strong>ybrid <strong><font color=#8B0012>P</font></strong>romoter <strong><font color=#8B0012>P</font></strong>redicting (HPP) model. We divide the whole task into the following 3 steps:
 
 <br>
 
@@ -151,7 +151,7 @@ Since the culture environment varies with batch, which results in huge differenc
 
 #### **1.Model Construction**
 
-We constructed the model based on the molecular mechanism of gene circuit of AND-gate, and gave the predicted transcription rate at arbitrarily given lactate and oxygen concentration. Thus, we theoretically demonstrated the selectivity and specificity of our engineered E.coli for tumor microenvironment. Also, results of modeling gave clues about methods for gene circuit optimization.
+We constructed the model based on the molecular mechanism of gene circuit of AND-gate, and gave the predicted transcription rate at arbitrarily given lactate and oxygen concentration. Thus, we theoretically demonstrated the selectivity and specificity of our engineered E.coli for tumour microenvironment. Also, results of modeling gave clues about methods for gene circuit optimization.
 
 ##### **1.1 Landscape of the Circuit**
 
@@ -467,7 +467,7 @@ According to the distance between the FNR protein binding site and the RNA polym
 
 <br>
 
-Based on this, we fixed other parameters in the model and adjust he value of $\Delta e_{FR}$. The value of this parameter in the original model is -8 ${k_b}T$ , and now observe the distribution of transcription rate and experimental data prediction when the energy term is taken as -6, -7, -9, -10, -11, -12 ${k_b}T$ units (Figure 9). We found that as the absolute binding energy between FNR and RNA polymerase increased, the overall transcription rate increased, but the targeting ability for the hypoxic and high lactate conditions corresponding to tumors (lactate>10mM, oxygen<0.2) decreased. We found that as the absolute binding energy between FNR and RNA polymerase increased, the overall transcription rate increased, but the targeting ability for the hypoxic and high lactate conditions corresponding to tumors (lactate>10mM, oxygen<0.2) decreased. The reason for this phenomenon is that under aerobic conditions, although most DNA strands exist in the form of loops, when the absolute value of binding energy is high, DNA strands that do not exist in the form of loops are more in the form of FNR bound RNA polymerase, resulting in higher compositional expression of pPepT.
+Based on this, we fixed other parameters in the model and adjust he value of $\Delta e_{FR}$. The value of this parameter in the original model is -8 ${k_b}T$ , and now observe the distribution of transcription rate and experimental data prediction when the energy term is taken as -6, -7, -9, -10, -11, -12 ${k_b}T$ units (Figure 9). We found that as the absolute binding energy between FNR and RNA polymerase increased, the overall transcription rate increased, but the targeting ability for the hypoxic and high lactate conditions corresponding to tumours (lactate>10mM, oxygen<0.2) decreased. We found that as the absolute binding energy between FNR and RNA polymerase increased, the overall transcription rate increased, but the targeting ability for the hypoxic and high lactate conditions corresponding to tumours (lactate>10mM, oxygen<0.2) decreased. The reason for this phenomenon is that under aerobic conditions, although most DNA strands exist in the form of loops, when the absolute value of binding energy is high, DNA strands that do not exist in the form of loops are more in the form of FNR bound RNA polymerase, resulting in higher compositional expression of pPepT.
 
 <br>
 
@@ -699,7 +699,7 @@ For this model, the body is represented by the following compartments:
 <br>
 
 - **Central Compartment (C):** This is the primary region where the drug is administered and from where it gets distributed to other parts of the body.
-- **Tumor Compartment (T):** A specific target for many drugs, this compartment represents the tumor region in the body.
+- **Tumour Compartment (T):** A specific target for many drugs, this compartment represents the tumour region in the body.
 - **Liver Compartment (L):** The liver, a major organ for drug metabolism, has its own compartment.
 - **Peripheral Compartment (P):** This represents other parts of the body not explicitly modeled.
 - **Phagocyte Compartment (PC):** Representing the cells that engulf and destroy bacteria, this compartment is crucial for a bacteria-based drug.
@@ -717,8 +717,8 @@ The model is governed by a plethora of parameters, each adding a layer of depth
 * Cx:  The concentration of drug in the x-compartment.
 
 - $k_{cij{\text{max}}}$ and $K_{ij}$  determine the flow from the i-compartment to the j-compartment.
-- r: The intrinsic growth rate of the bacteria in the Tumor Compartment.
-- K: The carrying capacity, or the maximum concentration that the Tumor Compartment can sustain.
+- r: The intrinsic growth rate of the bacteria in the Tumour Compartment.
+- K: The carrying capacity, or the maximum concentration that the Tumour Compartment can sustain.
 
 <br>
 
@@ -732,7 +732,7 @@ The heart of this model lies in its **differential equations**. These equations
 
 - Transfer of drug between compartments.
 - Metabolism or elimination of the drug.
-- Growth or decay of bacterial concentration, especially in the tumor region.
+- Growth or decay of bacterial concentration, especially in the tumour region.
 
 <br>
 
@@ -748,7 +748,7 @@ Following this logic, we get the ODEs:
 $$
 \frac{dCc}{dt} = -k_{ct} \cdot Cc + k_{tc} \cdot Ct - k_{cl} \cdot Cc + k_{lc} \cdot Cl + k_{pcl} \cdot Cpc - k_{cp} \cdot Cc + k_{pc} \cdot Cp - k_{e_c} \cdot Cc
 $$
-**Tumor Compartment (T):**
+**Tumour Compartment (T):**
 $$
 \frac{dCt}{dt} = k_{ct} \cdot Cc - k_{tc} \cdot Ct + r \cdot Ct \cdot \left(1 - \frac{Ct}{K}\right) - k_{e_t} \cdot Ct
 $$
@@ -827,7 +827,7 @@ Each simulation painted a vivid picture, showing how the bacterial concentration
 
 <br>
 
-From the graph, we can see that drug concentration in the tumor compartments maintains a relatively high numerical value. The relative order of final concentration is as follows: tumor > central compartment > liver > peripheral compartment > phagocyte. This is quite a reasonable outcome since the hypoxia and high-lactate environment boost bacteria growth. As the only compartment connected to the tumor, the central compartment has the second highest concentration. Due to the comprehensive effect of a relatively larger decaying rate and moving-in rate, the order is followed by the peripheral and liver compartment. Phagocyte displays a constantly low concentration since it's where the bacteria are deliberately killed.
+From the graph, we can see that drug concentration in the tumour compartments maintains a relatively high numerical value. The relative order of final concentration is as follows: tumour > central compartment > liver > peripheral compartment > phagocyte. This is quite a reasonable outcome since the hypoxia and high-lactate environment boost bacteria growth. As the only compartment connected to the tumour, the central compartment has the second highest concentration. Due to the comprehensive effect of a relatively larger decaying rate and moving-in rate, the order is followed by the peripheral and liver compartment. Phagocyte displays a constantly low concentration since it's where the bacteria are deliberately killed.
 
 <br>
 
@@ -935,9 +935,9 @@ The multi-compartment model offered a profound insight into the journey of a bac
 
 1. Harimoto, T., Hahn, J., Chen, YY. *et al.* A programmable encapsulation system improves delivery of therapeutic bacteria in mice. *Nat Biotechnol* **40**, 1259–1269 (2022). https://doi.org/10.1038/s41587-022-01244-y
 
-2. Gao Y, Zhou H, Liu G, Wu J, Yuan Y, Shang A. Tumor Microenvironment: Lactic Acid Promotes Tumor Development. J Immunol Res. 2022 Jun 12;2022:3119375. doi: 10.1155/2022/3119375. PMID: 35733921; PMCID: PMC9207018.      
+2. Gao Y, Zhou H, Liu G, Wu J, Yuan Y, Shang A. Tumour Microenvironment: Lactic Acid Promotes Tumour Development. J Immunol Res. 2022 Jun 12;2022:3119375. doi: 10.1155/2022/3119375. PMID: 35733921; PMCID: PMC9207018.      
 
-3.  Brizel D. M., Schroeder T., Scher R. L., et al. Elevated tumor lactate concentrations predict for an increased risk of metastases in head-and-neck cancer. International Journal of Radiation Oncology • Biology • Physics . 2001;51(2):349–353. doi: 10.1016/S0360-3016(01)01630-3.
+3.  Brizel D. M., Schroeder T., Scher R. L., et al. Elevated tumour lactate concentrations predict for an increased risk of metastases in head-and-neck cancer. International Journal of Radiation Oncology • Biology • Physics . 2001;51(2):349–353. doi: 10.1016/S0360-3016(01)01630-3.
 
 4. Bintu L, Buchler NE, Garcia HG, Gerland U, Hwa T, Kondev J, Phillips R. Transcriptional regulation by the numbers: models. Curr Opin Genet Dev. 2005 Apr;15(2):116-24. doi: 10.1016/j.gde.2005.02.007. PMID: 15797194; PMCID: PMC3482385.
 
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