Difference between revisions of "Team:HUST-China/Comparison between PSB"
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<h3><b>2.<i>Rhodopseudomonas palustris</i></b></h3> | <h3><b>2.<i>Rhodopseudomonas palustris</i></b></h3> | ||
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<p>These are the main differential equations about the modeling part of the <I><i>Rhodopseudomonas palustris</i></I> (abbreviated as “Rps”). Through these differential equations, we can calculate the concentrations of Rps, carbon dioxide and lactate. The parameters and variables above will be introduced below.This part is similar to that of <I><i>Synechocystis</i></I>, because in our experiment, they are both used to provide lactate to <I><i>Shewanella</i></I> and have similar genetic modification.</p> | <p>These are the main differential equations about the modeling part of the <I><i>Rhodopseudomonas palustris</i></I> (abbreviated as “Rps”). Through these differential equations, we can calculate the concentrations of Rps, carbon dioxide and lactate. The parameters and variables above will be introduced below.This part is similar to that of <I><i>Synechocystis</i></I>, because in our experiment, they are both used to provide lactate to <I><i>Shewanella</i></I> and have similar genetic modification.</p> | ||
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<p>From the same reference we get the relationship between the light intensity and the growth rate. The function</p> | <p>From the same reference we get the relationship between the light intensity and the growth rate. The function</p> | ||
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<p>is from another reference (Xiong et al. 2012)<sup>[2]</sup>. The parameter <img style="vertical-align:middle;height:30px" src="https://static.igem.org/mediawiki/2018/0/0c/T--HUST-China--2018-model-new-c006.png"> the decline of the growth rate caused by gene editing. This parameter is introduced to make the growth model of wild <I><i>Rhodopseudomonas palustris</i></I> fit to the growth of edited ones in the reference ( Henrike et al. 2012 )<sup>[3]</sup>.</p> | <p>is from another reference (Xiong et al. 2012)<sup>[2]</sup>. The parameter <img style="vertical-align:middle;height:30px" src="https://static.igem.org/mediawiki/2018/0/0c/T--HUST-China--2018-model-new-c006.png"> the decline of the growth rate caused by gene editing. This parameter is introduced to make the growth model of wild <I><i>Rhodopseudomonas palustris</i></I> fit to the growth of edited ones in the reference ( Henrike et al. 2012 )<sup>[3]</sup>.</p> | ||
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<p>This stands for the CO<sub>2</sub> consumed by each unit of <i>Rhodopseudomonas palustris</i>. <img style="vertical-align:middle;height:50px" src="https://static.igem.org/mediawiki/2018/d/d7/T--HUST-China--2018-model-new-r007.png"> is used for growth, <img style="vertical-align:middle;height:50px" src="https://static.igem.org/mediawiki/2018/6/64/T--HUST-China--2018-model-new-r008.png"> is used for lactate producing, and <img style="vertical-align:middle;height:30px" src="https://static.igem.org/mediawiki/2018/d/d3/T--HUST-China--2018-model-new-r009.png"> used for sustaining its life.</p> | <p>This stands for the CO<sub>2</sub> consumed by each unit of <i>Rhodopseudomonas palustris</i>. <img style="vertical-align:middle;height:50px" src="https://static.igem.org/mediawiki/2018/d/d7/T--HUST-China--2018-model-new-r007.png"> is used for growth, <img style="vertical-align:middle;height:50px" src="https://static.igem.org/mediawiki/2018/6/64/T--HUST-China--2018-model-new-r008.png"> is used for lactate producing, and <img style="vertical-align:middle;height:30px" src="https://static.igem.org/mediawiki/2018/d/d3/T--HUST-China--2018-model-new-r009.png"> used for sustaining its life.</p> | ||
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Revision as of 19:23, 17 October 2018
Comparison_between_PSB
1. Abstract
Modelling is a powerful tool in synthetic biology that allows us to get a deeper understanding of our system. In order to see whether our system can work and how our system will work, we build this model to simulate our system. This model shows us the details of our system and give our intelligent device software data so that we can change the environment to increase the efficiency and stability of Optopia.
2. Overview
functions
1.Synechocystis
These are the main differential equations of the Synechocystis part of the model. The parameters and variables above will be introduced below.
These functions calculate the growth rate of Synechocystis. The two functions correspond to different concentrations of CO2 in the solution.
and
make the growth rate decline when the concentration of CO2 in the solution is too high or too low. (David et al. 2015)[1]. From the same reference we get the relationship between the light intensity and the growth rate. The function
is from another reference(XIONG et al. 2012) [2]. The parameter 
the decline of the growth rate caused by gene editing. This parameter is introduced to make the growth model of wild Synechocystis fit to the growth of edited ones in the reference (Henrike et al. 2010)[3].
This stands for the CO2 consumed by each unit of Synechocystis.
is used for growth,
is used for lactate producing, and 
is used for sustaining its life.
The O2 consuming is calculated by 
because the photosynthesis and respiration in Synechocystis have the same stoichiometric ratio.
This function shows the inhibit of CO2 to the metabolism of Synechocystis. The lack of carbon source will not only effect the growth rate of the bacteria, but also reduce the produce rate of lactate.
| Parameter | Description | Value | Unit | Source |
|---|---|---|---|---|
| qLac,Synec | Lactate produced by each unit of Synechocystis. | 6.582×10-4 | h-1 | Fitting from reference [3] |
| YCO2,Synec | Yield coefficient of Synechocystis related to CO2 consuming, showing the CO2 consumed for growth | 2.284×102 | 1 | Fitting from reference [1] |
| Y O2,CO2 | O2 producing coefficient related to CO2 consuming. | 1.375 | 1 | Calculated by the stoichiometric in the chemical equation of the photosynthesis and respiration in Synechocystis and relative molecular mass of O2 and CO2 |
| YCO2,Lac | O2 producing coefficient related to lactate consuming. | 6.818×10-1 | 1 | Calculated by the stoichiometric in the chemical equation of lactate producing, and relative molecular mass of CO2 and lactate |
| mCO2,Synec | The sustain coefficient of Synechocystis. Stand for the CO2 consuming by the unit dry weight of alive Synechocystis. | 1.164×10-1 | h-1 | Fitting from reference [1] |
| μmax,Synec | The max growth rate of Synechocystis. | 5.210×10-2 | h-1 | Fitting from reference [1] |
| kCO2,Synec,1 | The semi-saturation constant of CO2 concentration when it is low. | 3.551×10-6 | g⋅L-1 | Fitting from reference [1] |
| kCO2,Synec,2 | The semi-saturation constant of CO2 concentration when it is high | 7.788×10-2 | g⋅L-1 | Fitting from reference [1] |
| cl,CO2 | The critical value of CO2 concentration. Different formulas are applied when CO2 concentration in the solution is above or below this value. | 7.788×10-2 | g⋅L-1 | Fitting from reference [1] |
| cmax,Synec | The max concertation of Synechocystis in the solution. | 3.160×10-1 | g⋅L-1 | Fitting from reference [1] |
| Ik,Synec | The light intensity constant of Synechocystis while the light intensity is lower than 8000Lux | 8.749×102 | Lux | Fitting from reference [1] |
| kdecline | The decline caused by the gene editing. | 4.209×10-1 | 1 | Fitting from reference [1] |
2.Rhodopseudomonas palustris
These are the main differential equations about the modeling part of the Rhodopseudomonas palustris (abbreviated as “Rps”). Through these differential equations, we can calculate the concentrations of Rps, carbon dioxide and lactate. The parameters and variables above will be introduced below.This part is similar to that of Synechocystis, because in our experiment, they are both used to provide lactate to Shewanella and have similar genetic modification.
The function calculates the growth rate of Rhodopseudomonas palustris. The function corresponds to different concentrations of CO2 in the solution.
makes the growth rate decline when the concentration of CO2 in the solution is too high. This is gotten from the reference (David et al. 2015)[1].
From the same reference we get the relationship between the light intensity and the growth rate. The function
is from another reference (Xiong et al. 2012)[2]. The parameter the decline of the growth rate caused by gene editing. This parameter is introduced to make the growth model of wild Rhodopseudomonas palustris fit to the growth of edited ones in the reference ( Henrike et al. 2012 )[3].
This stands for the CO2 consumed by each unit of Rhodopseudomonas palustris. 
is used for growth, 
is used for lactate producing, and 
used for sustaining its life.
This function shows the inhibit of CO2 to the metabolism of Rhodopseudomonas palustris. The lack of carbon source will not only impact the growth rate of the bacteria, but also reduce the production rate of lactate.
3. Shewanella
In generate, the three elements urgently needed to be modeled in Shewanella are the changes in biomass (Dry Weight, g/L), electricity production (mV), and lactate content (g/L) over time. Once the Shewanella modeling is completed, we only need to combine the model of Shewanella with the model of Synechocystis or Rhodopseudomonas palustris to determine which one is better to facilitate electricity produce. The process of deduction will write blow:
First, we need to simulate biomass function. Our biomass function is based on monod equation:
In this function, μ is specific growth rate of biomass.
Because there are two important growth factors in our model: lactate and oxygen content, we need some modifying tasks in this model. Noticing that the concentration of oxygen and lactate are both promoting biomass growth, with the inspiration of monod equation, we take the two factors into consideration so the function changes to:
In this function, cLac is concentration of lactate.
After this, we realized that a factor about oxygen competition is needed to add in the function. So we import a parameter to solve this problem, the function is modified to:
Taking the efficiency ratio of aerobic and anaerobic respiration (19:1) into consideration, we calculated two parameters:
The two parameters are used in our electricity production simulation:
The basic function is the famous Nernst equation:
In this function Ox is oxidized type, and Red is reduced type.
The Nernst equation is very clear and easy to use, but we have to add a Shewanella biomass factor to show the macroscopic electricity production. The lactic consuming value is closely related to the concentration of Shewanella. In addition, after the metabolic analysis of lactic in Shewanella, we promoted the function by modifying (or simplifying) the 
section. As a result, the electricity production function is:
In this function,
The last work in Shewanella modeling is the lactic consuming simulation:
The concentration of lactate dominates largely on electricity production. Similarly, with the inspiration of Monod equation, we notice that the concentration of lactate acid and Shewanella itself is in a positive correlation to lactic consuming rate. So we have summarized the lactic consuming equation:
Part3: Whole design
Design of MFC
We have designed a bipolar chamber MFC this year. Proton exchange membrane divided it into anode chamber and cathode chamber. Anode chamber containing S.oneidensis, nutrient substance(LB、lactate ) or other electrical producing microbes were sealed to prevent the entry of external oxygen. Considering safety and oxidation-reduction potential, we put ferric chloride solution in cathode chamber so that S.oneidensis can transfer electrons outside of their membranes by electron transport chain. Then electrons will reduce ferric ion into ferrous through carbon cloth and produce electricity.We recorded open circuit voltage curve and load voltage curve of MFCs in each different systems. Also, we have measured the biomass of each system in order to ensure whether the improved electricity could be attributed to more attached Shewanella cells on the anodes or the higher electroactivity of single cell.[11]
Co-culture
Obviously, the ecological relationship between microorganisms is very complex. There is not only the competition between them for the nutrient, but also the regulation of metabolites among them including induction, transgenosis and synergistic metabolism. Besides, it has been found that the co-culture of microorganisms can improve the electric efficiency of Microbial Fuel Cell under certain conditions.
Metabolites exchange is a common relationship in co-culturing. Therefore, we have designed a clear microbial metabolic pathway to achieve the conversion from light to electricity as well as used more potential symbiotic relationships between the flora to help improve the electricity production efficiency of MFC.
By consulting literature, we found two kinds of microorganisms——Cyanobacteria and Rhodopseudomonas palustris, both of which can utilize light energy and provide lactate to S.oneidensis after doing molecular construction.
In order to provide a basic growth environment, we mix the culture medium of different strains.（Please refer to our protocol section for the composition of the mediums.）
Synechocystis PCC6803
Lactate produced by Synechocystis PCC6803 can be used as the optimal carbon source for Shewanella. At the same time, acetate produced by Shewanella can be used as the organic carbon source of Synechocystis PCC6803 to increase the lactate production. And the metabolite exchange of Synechocystis PCC6803 and Shewanella is the basis for our photoautotrophic MFC.[12].
Rhodopseudomonas palustris
We attempted to engineer Rhodopseudomonas palustris by synthetic biology to achieve the same or a better function of Synechococcus elongatus.
In the preliminary experiment, we found that there may be more potential interactions in the co-culture of Rhodopseudomonas palustris and Shewanella, which can greatly improve the coulombic efficiency of our MFC (please refer our results section for more detials). This is an unexpected surprise for us, which improve to our confidence in the success of the project.
Reference
[1]Henrike Niederholtmeyer, Bernd T. Wolfstädter, David F. Savage, Pamela A. Silver, Jeffrey C. Way. Engineering Cyanobacteria to Synthesize and Export Hydrophilic Products. Applied and Environmental Microbiology. 2010, 76(11): 3462-3466.
[2] 《沼泽红假单胞菌的研究进展及其应用》王跃先、刘德海
[3] 周茂洪 赵肖为 吴雪昌《光合细菌沼泽红假单胞菌同化磷能力的研究》 《科技通报》2002年 第2期 142-146页
[4] KEGG, https://www.genome.jp/kegg/pathway.html
[5] Cloning and sequence analysis of the gene encoding Lactococcus lactis malolactic enzyme: relationships with malic enzymes FEMS Microbiol Lett. 1994 Feb 1;116(1):79-86
[6] Fine tuning the transcription of ldhA for d-lactate production August 2012, Volume 39, Issue 8, pp 1209–1217
[7] Transport of L-Lactate, D-Lactate, and Glycolate by the LldP and GlcA Membrane Carriers of Escherichia coli Volume 290, Issue 2, 18 January 2002, Pages 824-829
[8] Enhancement of Hydrogen Production and Carbon Fixation in Purple Nonsulfur Bacterium Bacterium by Synthetic Biology Shou-Chen Lo
[9] Jcat, http://www.jcat.de/#opennewwindow
[10] Genomic reconstruction of Shewanella oneidensis MR-1 metabolism reveals a previously uncharacterized machinery for lactate utilization PNAS February 24, 2009 106 (8) 2874-2879
[11] Modular Engineering Intracellular NADH Regeneration Boosts Extracellular Electron Transfer of Shewanella oneidensis MR‑1 ACS Synth. Biol. 2018, 7, 885−895
[12]Varman A. M., Yu Y., You L. & Tang Y. J. Photoautotrophic production of d-lactate in an engineered cyanobacterium. Microb. Cell Fact. 12, 117 (2013).
