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− | <h2 | + | <h2 style="text-align:center">Introduction</h2> |
− | <h4> | + | <h4>This year our project is the introduction of acrylic synthetic routes in Escherichia coli or Saccharomyces |
− | + | cerevisiae to produce acrylic acid. | |
− | + | <br> | |
− | + | <br> Primitive metabolic path map | |
− | + | <br> | |
− | + | <br> We have a rational new design and transformation of the core enzyme ceaS2, at the same time, we | |
− | + | also want to be optimized to improve the acrylic acid production in the metabolic flow. | |
− | + | <br> We know that for Escherichia coli, the carbon flow rate of its original glycerol metabolic pathway | |
+ | may not be sufficient, and if the new glycerol metabolic pathway can be used to increase the carbon | ||
+ | flow of DHAP or G3P, the substrate of the core enzyme ceaS2 can be increased Concentration to increase | ||
+ | acrylic acid production. | ||
+ | <br> Therefore, through the literature review, we found two enzymes which can achieve efficient conversion | ||
+ | of glycerol to generate DHAP the same way. | ||
+ | <br> In our new approach, Glycerol dehydrogenase (Gly DH) is capable of efficiently converting glycerol | ||
+ | to 1,3-Dihydroxyacetone (DHA) and then phosphorylates DHA to DHAP via Dihydroxyacetone kinase (DAK). | ||
+ | <br> | ||
+ | <br> New route map | ||
+ | <br> Before the implementation of the formal experiment, we need to model it to analyze the impact of | ||
+ | the introduction of new routes on the original metabolic flow, especially the two intermediates of | ||
+ | DHAP or G3P. Specifically, we care about the following two issues: | ||
+ | <br> 1. Has the DHAP or G3P's carbon flow improved after the introduction of new metabolic pathways? | ||
+ | Is it compared to the previous increase in production? | ||
+ | <br> 2. The introduction of new pathways after the entire metabolic pathway is stable and robust. How | ||
+ | is it? | ||
+ | <br> In order to answer these two questions, we established a carbon metabolic flow model. | ||
+ | </h4> | ||
+ | <h4>The overall workflow is as follows:</h4> | ||
+ | <h2 style="text-align:center">Parameter estimation</h2> | ||
+ | <h4>There are many parameters to be determined in the model. Most of these kinetic parameters can be found | ||
+ | in the literature or in the database, but at the same time, there are some kinetic parameters of | ||
+ | the enzyme we are looking for. Its organic matter, or the temperature and ph of the enzyme are different. | ||
+ | Therefore, we need to re-estimate this part of the parameters. | ||
+ | <br> | ||
+ | <br> In the process data link, we cited the method using the data point weighting of University of Manchester | ||
+ | in year 2016 . The weighting of the samples is as follows: | ||
+ | <br> 1. When the sample PH is the same, the sample is weighted by 4 .2 when they are close. 1 when they | ||
+ | differ much. | ||
+ | <br> 2. When the sample temperature is the same, the pH is the same. | ||
+ | <br> 3. When the samples are from the same species , the weight of the sample is 4.When they are the | ||
+ | non-identical species and are the prokaryotes,or the corresponding species mutated to the corresponding | ||
+ | species, the weight is 2. When they are the non-identical species and are the eukaryotes, the weight | ||
+ | is 1. | ||
+ | <br> 4. Try to delete the missing data. If there are some essential samples of the temperature and PH | ||
+ | missing, then the corresponding weight is 2. | ||
+ | <br> | ||
+ | <br> 1.kernel density estimate 2. Gaussian mixed model. | ||
+ | <br> | ||
+ | <br> The fourth point reflects our point of view of Bayesian. In the absence of prior knowledge of the | ||
+ | case, we take as much as possible the weight of neutrality. | ||
+ | <br> Based on the points above, we get a new parametric vector after weighting.In our model, we do the | ||
+ | fitting of the probability density according to the two methods of the parameter vector. | ||
+ | <br> 1.kernel density estimate 2. Gaussian mixed model. | ||
</h4> | </h4> | ||
− | |||
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− | + | <h2 style="text-align:center">The basic workflow of parameter estimation:</h2> | |
− | + | <h4>The Gaussian mixture model can be approximated to any real probability distribution in theory. The EM | |
− | + | algorithm is used to estimate the parameters required for the model. And we use the Gaussian mixture | |
− | + | model to estimate the probability density of the possible distribution of parameters. | |
− | + | <br> After making the probability distribution, we select Bin randomly, which meet the conditions of | |
− | < | + | width = len / 10.And we select the most possible bin based on the CDF, and estimate the corresponding |
− | + | parameters when the bin reach average value. | |
− | + | <br> Finally, we get the estimated parameter values, as well as the corresponding parameters of the original | |
− | + | PDF. The specific form and parameter values are as follows: | |
− | + | <br> The reaction path of the original pathway is Gly to Gly-3-p and then to DAHP | |
− | + | <br> The original pathway belongs to the reaction of a single channel, and there is a random bibi reaction | |
− | + | and an irreversible Mickey equation reaction. The reaction involves two enzymes paticipating - glpk | |
− | + | and glpD. We assume that the reaction concentration of these two enzymes is 0.01 mM, assuming that | |
− | + | the initial [Gly] concentration is 10 mM, the initial concentration of ATP 10 mM, Gly-3 The concentration | |
− | + | of -p 0 mM and the concentration of DHAP 0 mM at the same time. | |
− | + | <br> | |
+ | <br> In this reaction, we make the following assumptions about our model: | ||
+ | <br> 1. The ATP of the E.coil system is given externally completely, assuming that the culture conditions | ||
+ | given externally are sufficient and ATP maintains a stable constant. | ||
+ | <br> 2. Assume that the substrate involved in the reaction does not participate in other reactions. | ||
+ | <br> In order to determine the yield of the target product, we chose to observe the efficiency of the | ||
+ | DHAP yield estimation system in view of the lack of basic Deas2 enzyme data. | ||
+ | <br> | ||
</h4> | </h4> | ||
− | + | <h4 style="text-align:center">Changes of metabolic flux before passage</h4> | |
− | + | <h4> | |
− | < | + | <br> We can observe that the DHAP stops growing close to 50 minutes. |
− | < | + | <br> |
− | </ | + | <br> Then, we need to test the carbon pathway through the modified pathway, And add a metabolic pathway |
− | < | + | enzyme-catalyzed by GlyDH enzyme and DAK in the original path, while the need for NOX enzyme and |
− | + | CAT enzyme from the role of NAD + supplement, resulting in DHA, and finally Phosphorylation produces | |
− | < | + | DHAP. |
− | + | <br> metabolic flow after the transformation of the reaction model. According to the actual situation | |
− | + | of the reaction, we make the following assumptions: | |
− | + | <br> 1. In the reaction , due to the process of hydrogen peroxide to the production of O2, that is, the | |
+ | process of generating acceptor, is faster, we will regard the reaction of NOX enzyme NADH catalytic | ||
+ | as an ordinary Michael's equation, rather than ordered sequence reaction. | ||
+ | <br> 2. Random pairs of sequence reactions and ordered sequence reaction equations are identical. So | ||
+ | we substrate which is identified as the [A] substrate depending on the integrity of the data. | ||
+ | <br> | ||
+ | </h4> | ||
+ | <h4 style="text-align:center">Metabolic pathways after transformation</h4> | ||
+ | <h4> | ||
+ | <br> We can see that the rate of DHAP is faster after changing the metabolic pathway, which means that | ||
+ | the higher the output per unit time after being put in use, the sooner the reaction is done. Compared | ||
+ | to the pre-improved pathway ,the reaction finishes roughly five minutes ahead. | ||
+ | <br> Sensitive analysis | ||
+ | <br> In the previous pathway study, we noted that the Kcat values of glpK and GlyDH enzymes are unknown | ||
+ | (we do not have a large deviation in the absence of a sample expressed in E.coil). | ||
+ | <br> The Kcat value of the reaction of propanal is the Kcat value of the reaction of Gly and GlyDH. It | ||
+ | is also assumed that the K63 ratio of the two enzymes is 2: 1. | ||
+ | <br> We often use Kcat / Km to describe the catalytic efficiency of different enzymes for the same substrate, | ||
+ | and as a result of our experiments, GlyDH exhibited higher catalytic efficiency than glpK. Thus we | ||
+ | assume that the GlyDH enzyme and the glpK enzyme satisfy the following relationship: | ||
+ | <br> GlyDH enzyme activity and the Km value for gly and the Km value of the glpk enzyme to gly have been | ||
+ | known in previous studies. Next we adjust the alpha coefficient to study the effect of different | ||
+ | ratios on the overall metabolic flow. | ||
</h4> | </h4> | ||
+ | <br> | ||
+ | <h4 style="text-align:center">KATA Sensitivity Test before Modification</h4> | ||
+ | <br> | ||
+ | <h4 style="text-align:center">KATA Sensitivity Test after Modification</h4> | ||
+ | <h4> | ||
+ | <br> We show the highest ratio (1000) and the lowest ratio (0.001) in yellow and blue lines respectively. | ||
+ | The pre-transformation pathway is most sensitive to the change of Kcat in glpK enzyme, and the metabolic | ||
+ | pathway of the target substrate is transformed with the change of α Rate is always higher than the | ||
+ | pre-transformation pathway, even when the glpK Kcat / Km value is 100 times the GlyDH, the reason | ||
+ | may be DAK enzyme catalytic efficiency’s higher than glpD. | ||
+ | <br> As we can see, the previous reaction is dependent on ATP, and in the previous hypothesis, we make | ||
+ | ATP stable in the constant .In order to analyze the ATP concentration changes on the impact of glycerol | ||
+ | conversion, we add a Standard deviation of 0.05, mean of the normal distribution of variables to | ||
+ | disturb the timing of the concentration of ATP in ODE. | ||
+ | <br> For the sake of us to observe the significant results, we assume that the initial concentration | ||
+ | of ATP is 0 and is always greater than zero. | ||
+ | <br> And the change curve of the concentration in 60min is shown below: | ||
+ | </h4> | ||
+ | <br> | ||
+ | <br> | ||
+ | <h4 style="text-align:center">Before transformation</h4> | ||
+ | <br> | ||
+ | <h4 style="text-align:center">After trransformation</h4> | ||
+ | <h4> | ||
+ | <br> We found that the random change of ATP concentration had a significant effect on the pathway after | ||
+ | transformation, and the rate of DHAP synthesis was lower than that before transformation. | ||
+ | <br> But when we adjust the standard deviation of the normal distribution random variable to 0.05, the | ||
+ | result is shown below. | ||
+ | <br> Thus, we found that even if ATP had a greater perturbation, the overall level was relatively high | ||
+ | in 0-60 min compared to the previous standard deviation of 0.02. While the transformation of the | ||
+ | metabolic pathway also reflects a more stable curve of change. At this point the concentration of | ||
+ | DHAP is not significantly affected by changes in ATP concentration. | ||
+ | <br> Thus, in actual production, we only need to keep the ATP concentration at a slightly higher level, | ||
+ | not only to ensure the production of the target product, but to increase the stability of the system | ||
+ | as well. | ||
+ | <br> | ||
+ | </h4> | ||
+ | |||
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Revision as of 16:47, 30 October 2017