Team:USP-EEL-Brazil/Model
Model Introduction
The creation of a model to describe a project is highly important since the model attended the system’s parameters, it’s possible to predict results and find a way to illustrate the functioning of the system, even before it’s finished. To develop a model, you should build on existing knowledge on what you’re trying to describe, understand these data, and then pick up some equations that may be used to each component of your project. The knowledge varies from source, you may base yourself on other people’s research or you can produce your own data to your model. On our iGEM Project the model was created based on Myceliophtora thermophila laccase rather than Pleurotus ostreatus and Phoma sp. laccases, because the MT laccase is well known and there’s a lot of data available, once the chosen for us are quite unknown and not so well described for estrogens and xenoestrogens removal, causing some trouble to develop some kind of prediction for our system.
Our project aims to develop a reactor system to the enzyme production, then, purify the enzyme, and apply the purified enzyme in another reactor to the effluent treatment. But we can’t simply apply all of this without a base of what to expect. On the modeling, we seek to analyze kinetic behavior, verifying the effective removal and the variation of its efficiency in different types of reactors.
Thus, we hope to find parameters to the choosing of the best application system, and how it is going to attend the water flux of our region, a small countryside city, really small, the city of Lorena.
Theory
The team has planned a system with a bioreactor to eliminate endocrines disruptors from the water. We modeled 2 of them: a fed-batch reactor and an enzymatic membrane reactor. Our principal problem was finding data for all the situations we’re planning. The use of Laccase for degrading endocrines disruptors isn’t yet a conventional process, and as described above, was pretty hard to find data about it.
All laccases are known to follow the kinetics of Michaelis-Menten equation, described by the equation (1).
When [S]>>km, the expression (2) is obtained, and when km>>[S], the expression (3) is obtained.
Don’t worry, at the end of the section we’ll have a table where all variables are described.
Most part of the data we found were in removal rates, which were transformed from (% removal) to substrate concentration by equation (4):
Likewise, the derivative of expression (4) is considered by expression (5):
Being used to determinate the mass balances for each modelled reactor.
Reactors
To understand how our system would react to diferents inputs first we need to estabelecer um tipo de reactor.
BATCH REACTORS
For batch reactors we used the mass balance as the described by equation (5) when the data were based on removal ratio. Whenever the data was given in substrate concentration, the ordinary Michaelis-Menten was used, as above on equation (6)
And the boundary condition considered was that at t=0, the removal was 0 too.
Using the software Wolfram Mathematica, a nonlinear model was created to fit the data and find the km and the vmax¬¬.
FED-BATCH REACTORS
ENZYMATIC MEMBRANE REACTOR
Different Mediators Test
Some news this fine day!
Optimun pH and Temperature
Proteins are macromolecular organic compounds and some of them have catalytic function, they’re known as enzymes. As they’re made up with amino acids, they have charge along their chain that changes as the pH varies. For this reason, with the variation of the pH, the inter and intramolecular interactions can be increased or decreased, altering the tridimensional conformation of the molecule. All molecules have a more stable conformance, and when they’re not arranged this way, they can become very unstable, losing their activity and performance on their substrates.
The alteration in the conformation of the protein can change the affinity with substrates. Some substrates are preferable in low pH, and other may be preferable at high pH, for example.
As we’re using a model laccase (Myceliophthora thermophila), in this section we’ll discuss its properties around optimum pH and temperature. The 2 laccases we’re aiming to produce have poor data and we’ll use them to complement the discussion around the MT laccase.
MYCELYOPHTHORA THERMOPHILA LACCASE
For this laccase, the results obtained by Hollman (2008) about the optimum pH and the variation of the temperature around the enzyme’s stability are described in Figure 1.
These results show us that the optimum temperature may be around 50°C, and as more alkaline is the pH, more stable the enzyme gets. The pH 6 at 50°C appear to be the best conditions to the enzyme stability. Other researchers (Lloret et al.¸ 2010) studied the effects of pH separately at a temperature of 22,2°C on the stability of the lacMT (Fig. 2). We can notice that the enzyme has more stability when the pH is more alkaline (above pH 7) at a temperature near the room temperature.
PLEUROTUS OSTREATUS LACCASE
The studies around LacPO (LIBARDI JUNIOR, 2010) could describe its behavior with the variation of pH and temperature. On the figures below (3 and 4) these data can be analyzed.
In this study they used ABTS 20mM to determinate the activity using a McIlvain buffer with 1mL of the reactional mixture, at 25°C, containing 0.5U of laccase. Data show that the enzyme has more activity between pH 3 and 4.
For the temperature study the pH was maintained at 5, using ABTS 20mM to determinate the activity, using again a McIlvain buffer with 1mL of a reactional mixture and 0.5U of laccase. The best temperature for activity conservation was between 4 and 14°C.
PHOMA SP. LACCASE
LacPS was also studied by Nelson Libardi Junior (2010), at the same experimental conditions LacPO. Figure 5 can show the pH influence on the lacPS, and we can notice that the best results were obtained with acid pHs (3-5).
Differently from LacPO, this laccase is able to keep its activity in a wide range of temperature for a long period of time (4-28°C).
For the use of these laccases together we could set a system with ph 4 and temperature around 14°C.
REFERENCES
Hollmann, F., Gumulya, Y., Tölle, C., Liese, A., & Thum, O. (2008). Evaluation of the Laccase from Myceliophthora thermophila as Industrial Biocatalyst for Polymerization Reactions. Macromolecules, 41(22), 8520–8524. doi:10.1021/ma801763t
LIBARDI JUNIOR, Nelson. Estudo de lacases fúngicas para degradação de compostos interferentes endócrinos. 2010. 140 f. Dissertação (Mestrado) - Curso de Engenharia de Processos, Universidade da Região de Joinville, Joinville, 2010.
Lloret, L., Eibes, G., Lú-Chau, T. A., Moreira, M. T., Feijoo, G., & Lema, J. M. (2010). Laccase-catalyzed degradation of anti-inflammatories and estrogens. Biochemical Engineering Journal, 51(3), 124–131. doi:10.1016/j.bej.2010.06.005
Substrate Degradation
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