Difference between revisions of "Team:UCSC/Model"
| Line 1: | Line 1: | ||
{{UCSC-Header}} | {{UCSC-Header}} | ||
{{highcharts}} | {{highcharts}} | ||
| + | |||
<html> | <html> | ||
| − | <!-- MathJax (LaTeX for the web) --> | + | <head> |
| − | <script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_SVG.js"> | + | <!-- MathJax (LaTeX for the web) --> |
| − | </script> | + | <script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_SVG.js"> |
| − | <script type="text/x-mathjax-config"> | + | </script> |
| − | MathJax.Hub.Config({ | + | <script type="text/x-mathjax-config"> |
| − | + | MathJax.Hub.Config({ | |
| − | }); | + | SVG: { linebreaks: { automatic: true } } |
| − | </script> | + | }); |
| + | </script> | ||
| + | </head> | ||
<h1>Modeling</h1> | <h1>Modeling</h1> | ||
| − | < | + | <p>Predict and optimize yield.</p> |
<body align="center"> | <body align="center"> | ||
<div align="left"> | <div align="left"> | ||
<h4>Acetaminophen <img class="acetaminophen" src="https://upload.wikimedia.org/wikipedia/commons/thumb/2/29/Paracetamol-skeletal.svg/1200px-Paracetamol-skeletal.svg.png" style="width:168px;height=128px"> </h4> | <h4>Acetaminophen <img class="acetaminophen" src="https://upload.wikimedia.org/wikipedia/commons/thumb/2/29/Paracetamol-skeletal.svg/1200px-Paracetamol-skeletal.svg.png" style="width:168px;height=128px"> </h4> | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
To predict theoretical acetaminophen production, we calculated the amount of a precursor, chorismate, by quantifying its main products, the aromatic amino acids phenylalanine, tyrosine, and tryptophan. | To predict theoretical acetaminophen production, we calculated the amount of a precursor, chorismate, by quantifying its main products, the aromatic amino acids phenylalanine, tyrosine, and tryptophan. | ||
Since no amino acid composition data was available for Synechococcus, we started by using literature data for similar cyanobacteria species Spirulina and Synechocystis and found that between one and 13.6 percent of amino acids were aromatics by mass, or between XX and YY molar percent. | Since no amino acid composition data was available for Synechococcus, we started by using literature data for similar cyanobacteria species Spirulina and Synechocystis and found that between one and 13.6 percent of amino acids were aromatics by mass, or between XX and YY molar percent. | ||
To further verify our organism's amount of acetaminophen precursor, we ran both the genome and ribosomal protein sequences through a custom Python program converting codons to amino acids and calculating aromatic amino acid molar percentages which resulted in 9.3% and 5.14% respectively. | To further verify our organism's amount of acetaminophen precursor, we ran both the genome and ribosomal protein sequences through a custom Python program converting codons to amino acids and calculating aromatic amino acid molar percentages which resulted in 9.3% and 5.14% respectively. | ||
</br> | </br> | ||
| − | + | $$\frac{0.774\ mmol\ FWY}{1g\ protein}\times \frac{0.6g\ protein}{1g\ biomass} \approx \frac{0.46\ mmol \ chor.}{1g\ biomass}\rightarrow\frac{1\ mol\ acet.}{4\ mol\ chor.}\times\frac{151.163g}{1\ mol\ acet.}=\frac{17.56g\ acet.}{1g\ biomass}$$ | |
| − | + | (Equation/ table summing amino acids converting to molar percentage) | |
</br> | </br> | ||
Using our sequence generated median chorismate value of 9.3% and the assumption that our enzymes would take a third of the chorismate precursor, we got an estimate for acetaminophen concentration of between 17.5mg and 1.29 mg per gram dried biomass. | Using our sequence generated median chorismate value of 9.3% and the assumption that our enzymes would take a third of the chorismate precursor, we got an estimate for acetaminophen concentration of between 17.5mg and 1.29 mg per gram dried biomass. | ||
| Line 43: | Line 33: | ||
<div align="left"> | <div align="left"> | ||
<h4>Biomass <img class="spirulina_pic" src="https://static.igem.org/mediawiki/2017/5/5c/Spirulina_powder.png" style="width:168px;height=128px"></h4> | <h4>Biomass <img class="spirulina_pic" src="https://static.igem.org/mediawiki/2017/5/5c/Spirulina_powder.png" style="width:168px;height=128px"></h4> | ||
| − | To understand the production capacity of our organism, we aggregated growth data from published papers and all of our lab’s growth data. Using limited logistic growth curves and linear algebra to fit our equation, we modelled dried biomass and cell count per time, with the additional dependent variables of temperature, light intensity, and starter culture density. | + | <div class=text-containter> |
| + | To understand the production capacity of our organism, we aggregated growth data from published papers and all of our lab’s growth data. Using limited logistic growth curves and linear algebra to fit our equation, we modelled dried biomass and cell count per time, with the additional dependent variables of temperature, light intensity, and starter culture density. | ||
| + | </div> | ||
<div id="container"></div> | <div id="container"></div> | ||
| Line 70: | Line 62: | ||
<h3>References</h3> | <h3>References</h3> | ||
| − | <a | + | <a href= http://onlinelibrary.wiley.com/doi/10.1002/cjce.22154/abstract>Growth optimization of Synechococcus elongatus PCC7942 in lab flasks and a 2-D photobioreactor</a> </br> |
| − | <a | + | <a href= http://ucelinks.cdlib.org:8888/sfx_local?ID=doi:10.1007%2Fs10811-011-9683-2&genre=article&atitle=Carbon%20metabolism%20and%20energy%20conversion%>Carbon metabolism and energy conversion of Synechococcus sp. PCC 7942 under mixotrophic conditions.</a> |
| − | + | ||
| − | + | ||
</div> | </div> | ||
</body> | </body> | ||
| Line 141: | Line 131: | ||
margin-left: 80px; | margin-left: 80px; | ||
margin-right: 80px; | margin-right: 80px; | ||
| + | background-color: honeydew; | ||
| + | } | ||
| + | div{ | ||
background-color: honeydew; | background-color: honeydew; | ||
} | } | ||
img.acetaminophen { | img.acetaminophen { | ||
position: relative; | position: relative; | ||
| − | top: | + | top: 25px; |
} | } | ||
img.spirulina_pic { | img.spirulina_pic { | ||
position: relative; | position: relative; | ||
| − | top: 0px; | + | top: 40px; |
| + | } | ||
| + | h1 { | ||
| + | font-family: 'Quicksand'!important; | ||
| + | font-size: 400%; /*!important*/ | ||
| + | } | ||
| + | p { | ||
| + | font-family: 'Quicksand' !important; | ||
| + | font-size: 200% !important; | ||
| + | /* margin-left: 200px !important; | ||
| + | margin-right: 200px !important;*/ | ||
| + | |||
| + | } | ||
| + | .text-container { | ||
| + | width: 80%; | ||
| + | padding-left: 0px; | ||
| + | padding-right: 0px; | ||
} | } | ||
| Line 260: | Line 269: | ||
</script> | </script> | ||
</html> | </html> | ||
| − | + | {{Ucscfooter}} | |
{{UCSC-Footer}} | {{UCSC-Footer}} | ||
Revision as of 20:50, 20 September 2017
Modeling
Predict and optimize yield.
Acetaminophen 
To predict theoretical acetaminophen production, we calculated the amount of a precursor, chorismate, by quantifying its main products, the aromatic amino acids phenylalanine, tyrosine, and tryptophan.
Since no amino acid composition data was available for Synechococcus, we started by using literature data for similar cyanobacteria species Spirulina and Synechocystis and found that between one and 13.6 percent of amino acids were aromatics by mass, or between XX and YY molar percent.
To further verify our organism's amount of acetaminophen precursor, we ran both the genome and ribosomal protein sequences through a custom Python program converting codons to amino acids and calculating aromatic amino acid molar percentages which resulted in 9.3% and 5.14% respectively.
$$\frac{0.774\ mmol\ FWY}{1g\ protein}\times \frac{0.6g\ protein}{1g\ biomass} \approx \frac{0.46\ mmol \ chor.}{1g\ biomass}\rightarrow\frac{1\ mol\ acet.}{4\ mol\ chor.}\times\frac{151.163g}{1\ mol\ acet.}=\frac{17.56g\ acet.}{1g\ biomass}$$
(Equation/ table summing amino acids converting to molar percentage)
Using our sequence generated median chorismate value of 9.3% and the assumption that our enzymes would take a third of the chorismate precursor, we got an estimate for acetaminophen concentration of between 17.5mg and 1.29 mg per gram dried biomass.
These numbers show that there is likely enough precursor and that acetaminophen production should be within a measurable range.
(Equation producing acetaminophen)
Biomass 
To understand the production capacity of our organism, we aggregated growth data from published papers and all of our lab’s growth data. Using limited logistic growth curves and linear algebra to fit our equation, we modelled dried biomass and cell count per time, with the additional dependent variables of temperature, light intensity, and starter culture density.
Timescale:
days
Light Intensity:
μE m-2 s-1
Temperature:
℃
Starting Density:
g biomass/ L
References
Growth optimization of Synechococcus elongatus PCC7942 in lab flasks and a 2-D photobioreactor Carbon metabolism and energy conversion of Synechococcus sp. PCC 7942 under mixotrophic conditions.
Jack Baskin School of Engineering
University of California, Santa Cruz
1156 High Street
Santa Cruz, California, 95064
Contact Us: UCSC iGEM
