Model
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Introduction

There are many factors in the experiment that will affect our results, and the model can help us reasonably analyze and predict the impact of these factors.


Melanogenesis kinetics

Objective

  1. Establish a melanin pathway, predict the changes in melanin production over time, and create a three-dimensional image to observe the trend of melanin production over time at different tyrosine concentrations.

  2. Predict the time point when the bacteria enters the stable growth phase.

Background

In this experiment, melanin was produced by microbial fermentation. Generally, the batch fermentation process can be roughly divided into four stages, namely, adaptation (stagnation) stage, logarithmic growth stage, growth stability stage and death stage. At the end of the growth period, the necessary conditions were created for the formation of products. After entering the stable period, the metabolism of bacteria was active and a large number of products were synthesized.

Considering the fermentation yield and avoiding bacterial contamination in industrial production, we hope to enter the stable growth period as soon as possible. According to the material balance in the fermentation process, the model can predict the melanin production curve and the time to enter the stable production period.

We simplified the unimportant parameters in the process, only focused on the main reactants, and successfully predicted the reactant formation curve.

Model

1.1 Reaction principle

Melanin production equation:

Table 1.  Description of melanin pathway parameters

Parameter Description Unit
[X] Bacterial concentration
[T] Matrix concentration
[M] Melanin concentration
t Incubation time
µ Specific growth rate
Specific tyrosine consumption rate
Specific melanin production rate

1.2 Assumption

  1. The growth curve of microorganisms is usually S-shaped.
  2. Secondary metabolism is non-growth-related, and the time when the maximum number of secondary metabolites reaches is later than the time when the maximum amount of microbial biomass reaches.
  3. Oxygen is sufficient during fermentation, and copper ions are not considered to combine with products.

1.3 Data

Figure 1.  Data on the changes of various substances over time during batch fermentation process

Figure 2.  Metabolic changes during batch fermentation of bacterial cells

Table 2.  Melanin production at different initial concentrations of tyrosine

Tyrosine concentration( Melanin production
0 0.818
0.5 0.893
1 1.039
1.5 1.058
2 0.998
2.5 1.391
 

Figure 3.  The relationship between melanin concentration and initial

1.4 Results

Figure 4.  Tyrosine Conversion and Melanin Production Simulation

Figure 5.  Biomass formation curve

The three-dimensional diagram is as follows:

Figure 6.  3D visualization of changes in melanin production and initial tyrosine concentration over time

Conclusions:

  1. Under the condition of sufficient oxygen and proper copper ion concentration, the initial tyrosine concentration was positively correlated with melanin production.

  2. According to the images obtained from the model, it can be observed intuitively that the reaction process enters the growth stable period at about 25h.

In order to improve our model in the future, we hope to find a more accurate way to measure melanin production, so that we can better simulate the production curve. We will explore the relationship between dissolved oxygen and melanin production under sufficient substrate and appropriate copper ion concentration.

Reference

[1] YU Jun-tang, TANG Xiao-xuan, WU Xing-yan, et al. New Biotech-nology[M]. Beijing, Chemical Industry Press,2003:150-153.

[2] Victor EBalderas-Hernandez AídaGutiérrez-Alejandre AlfredoMartinez FranciscoBolívar Guiller- moGosset María IChávez-Béjar. Metabolic engineering of Escherichia coli to optimize melanin synthesis from glucose[J]. Microbial Cell Factories, 2013, 12.

[3] WEI Hai-lan,YU Li-bo,YI Zhi-wei,et al. Studies on the melanin production and gene cloning of hppD from the deep-sea bacterium Pseudomonas sp. bIp-2[J]. Journal of Applied Oceanography, 2014, 33(04): 499-507.

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