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| − | <script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_SVG.js">
| + | <meta name="UCSC IGEM" content="The legendary, UCSC igem Science Superheros"> |
| − | </script>
| + | <title>Santa Cruz IGEM</title> |
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| − | <body class="workies">
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| − | <center style="background-color:honeydew;">
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| − | <h1>Modeling</h1>
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| − | <img class=title-image src="https://static.igem.org/mediawiki/2017/d/d9/Model-Icon-Cropped.png">
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| − | <div class=quote>Predict and optimize yield</div>
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| − | <div align="left">
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| − | <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>
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| − | <div class=text>
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| − | To predict theoretical acetaminophen production, we calculated the amount of its precursor, chorismate, by quantifying its main products, the aromatic amino acids phenylalanine, tyrosine, and tryptophan (FWY).
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| − | Since no amino acid composition data was available for Synechococcus, we started by using literature data for the similar cyanobacteria species Spirulina found that between 11 and 13.6 percent of amino acids were aromatics by mass, or between 6.5 and 7.7 molar percent of total protein. Even using the lower aromatic percentages and assuming a third of precursor goes to our pathway, we predict 22.6mg acetaminophen per gram biomass.
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| − | </br>
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| − | </div>
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| − | $$
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| − | \frac{0.449\ mmol\ FWY}{1g\ biomass}\approx \frac{0.449\ mmol\ chor.}{1g\ biomass}\rightarrow\frac{1\ mol\ acet.}{3\ mol\ chor.}=\frac{0.15\ mmoles\ acet}{1\ g\ biomass}\times\frac{151.163g\ acet.}{1\ mol\ acet.}=\frac{22.62mg\ acet.}{1g\ biomass}$$
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| − | <div class=caption>
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| − | An estimate for acetaminophen production using the aromatic amino acid (FWY) composition for Spirulina and assuming one third of the precursor goes to our inserted enzyme, 4ABH.
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| − | </div>
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| − | </br>
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| − | <div class=text>
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| − | 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. Using our sequence analysis based value of 9.3% and the assumption that our enzymes would take a third of the acetaminophen precursor, we estimate an acetaminophen concentration would be around 18mg per gram dried biomass.
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| − | </br>
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| − | $$\frac{0.093\ g\ FYW}{1\ g\ protein}\times\frac{0.6g protein}{1\ biomass}=\frac{0.056\ g\ FYW}{1g\ biomass}\rightarrow\frac{0.37\ mmol\ chor}{1\ g\ biomass}\times\frac{1\ mol\ acet}{3\ mol\ chor}\times\frac{151.163g\ acet}{1\ mol\ acet.}=\frac{18.61mg\ acet.}{1g\ biomass}$$
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| − | <div class=caption>
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| − | This equation is based on moles of aromatic amino acids calculated by translating the organism's 3MB genome and assuming a third of precursor goes to our pathway.
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| − | </div>
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| − | $$
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| − | \frac{0.051 g\ FYW}{1 g\ protein} * \frac{0.6 g\ protein}{1g\ biomass} = \frac{0.031g\ FWY}{1 g\ biomass} \rightarrow \frac{0.xx\ mmol\ chor}{1 g\ biomass} * \frac{1mol\ acet}{3 mol\ chor} *\frac{151.163}{1 mol\ acet} = \frac{xxxg\ acet}{1 g\ biomass}
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| − | $$
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| − | <div class= caption>
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| − | The choice to use ribosomal proteins as an estimate was due to the fact that ribosomal proteins are highly expessed in cells and would provide an accurate portrayal of the major protein composition in a cell at a given time.
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| − | </div>
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| − | </br>
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| − | <div class=text>
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| − | These numbers show that there is likely enough precursor and that acetaminophen production should be within a userful, measurable range of up to 23mg acetaminophen per gram biomass.
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| − | </div>
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| − | </div>
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| − | <div align="left">
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| − | <h4>Vitamin B-12<img class="B12-Structure" src="https://static.igem.org/mediawiki/2017/2/2e/B12-Structure.png" style="width:168px;height=128px"></h4>
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| − | <div class=text>
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| − | Predicting the quantity of active form of B12 produced depends on the success of our metabolic engineering and the quantity of cobalt which binds to our engineered product, the active lower ligand, 5,6-dimethyl-benzimidizole (5,6-DMB). Cobalt is a limiting factor for growth in many marine environments, limiting formation of B12’s large corrin-ring (1) . Even for species that use abundant adenine to bind to the lower ligand and form pseudo-B12, cobalt supplementation enhances growth and thus is likely the limiting precursor for B12 production. The gene BluB was inserted to enzymatically convert FMNH_2 to our activating lower ligand 5,6-DMB. With this ligand available and our second gene insert, BluB, which preferentially attaches 5,6-DMB to the cobalt, the vast majority of our B-12 should be made in active form. The gene that creates the lower ligand from the FMNH2, Ssue, came from X and had the pTrc promoter rendering it a strong enough producer to prevent 5,6-DMB from being the limiting factor. Published HPLC results show that that Spirulina produces between 150-250µg pseudo-cobalamin per hundred grams dry weight with the non-human-usable adenine as the lower ligand(1.5). If we assume our Blub/CobC enzyme complex works as well as it does in its origin organism, as assayed in Microbial Cell Factories paper (2), then research suggests 5,6-DMB has a much higher binding affinity for cobalt and thus nearly all of it will be converted to active-form B12 resulting in a production of almost exactly the Recommended Daily Value of 6µg B12 per 3g serving.
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| − | </div>
| + | <div id="page"> |
| − | <h4>Biomass <img class="spirulina_pic" src="https://static.igem.org/mediawiki/2017/5/5c/Spirulina_powder.png" style="width:168px;height=128px"></h4>
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| − | <div class=text>
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| − | To understand the production capacity of our organism, we aggregated growth data from published papers and all of our lab’s growth data. Using carrying capacity 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.
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| − | <span class="greenie">
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| − | <h3>References</h3>
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| − | <center>
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| − | <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>
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| − | <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></br>
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| − | <a href=https://ndb.nal.usda.gov/ndb/foods/show/3306?fgcd=&manu=&lfacet=&format=Full&count=&max=50&offset=&sort=default&order=asc&qlookup=11667&ds=&qt=&qp=&qa=&qn=&q=&ing=>United States Department of Agriculture Full Nutritional Report, Spirulina</a></br>
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| − | <a href=http://onlinelibrary.wiley.com/store/10.1002/jsfa.2740330511/asset/2740330511_ftp.pdf?v=1&t=j7tly39i&s=d889996cde56001a928eced82e3506459f823956>Nutritional Quality of the Blue-Green Alga Spirulina platensis. </a>
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| − | document.getElementById("htmlIntensityID").innerHTML=localStorage.jsIntensityValue;
| |
| − | }
| |
| − |
| |
| − | function MinusOneDegree() {
| |
| − | if(localStorage.jsTempValue>1){
| |
| − | localStorage.jsTempValue=parseInt(localStorage.jsTempValue)-1;
| |
| − | }
| |
| − | document.getElementById("htmlTempID").innerHTML=localStorage.jsTempValue;
| |
| − | }
| |
| − | function PlusOneDegree() {
| |
| − | localStorage.jsTempValue=parseInt(localStorage.jsTempValue)+1;
| |
| − | document.getElementById("htmlTempID").innerHTML = localStorage.jsTempValue;
| |
| − | }
| |
| − | function MinusDensity() {
| |
| − | if(localStorage.jsDensityValue>0.15){
| |
| − | localStorage.jsDensityValue=(Number(localStorage.jsDensityValue)-0.1).toFixed(1);
| |
| − | }
| |
| − | document.getElementById("htmlDensityID").innerHTML=localStorage.jsDensityValue;
| |
| − | }
| |
| − | function PlusDensity() {
| |
| − | localStorage.jsDensityValue=(Number(localStorage.jsDensityValue)+0.1).toFixed(1);
| |
| − | document.getElementById("htmlDensityID").innerHTML = localStorage.jsDensityValue;
| |
| − | }
| |
| − |
| |
| − | </script>
| |
| − |
| |
| − | <script src="https://code.highcharts.com/highcharts.src.js"></script>
| |
| − |
| |
| − | <script> //Charting
| |
| − | function MakeChart(){
| |
| − | var yValueArray = []
| |
| − | for(var dayNum=0; dayNum<=localStorage.jsDaysValue; dayNum++){
| |
| − | var myNum = 1/(1+(Math.pow(Math.E,((-1+(Math.abs(localStorage.jsIntensityValue-100)/250))+(Math.abs(localStorage.jsTempValue-33)/9))*dayNum+5)))
| |
| − | //yValueArray.push(myNum);
| |
| − | yValueArray.push(Number(myNum.toFixed(4))+Number(localStorage.jsDensityValue));
| |
| − | }
| |
| − |
| |
| − | //document.getElementById("demo").innerHTML = localStorage.jsDensityValue;
| |
| − |
| |
| − | Highcharts.chart('graph', {
| |
| − |
| |
| − | title: {
| |
| − | text: ''
| |
| − | },
| |
| − | subtitle: {
| |
| − | text: ''
| |
| − | },
| |
| − | yAxis: {
| |
| − | title: {
| |
| − | text: 'Grams Dried Biomass per Liter'
| |
| − | }
| |
| − | },
| |
| − | xAxis: {
| |
| − | title: {
| |
| − | text: 'Days'
| |
| − | }
| |
| − | },
| |
| − | legend: {
| |
| − | layout: 'vertical',
| |
| − | align: 'right',
| |
| − | verticalAlign: 'center'
| |
| − | },
| |
| − | plotOptions: {
| |
| − | series: {
| |
| − | pointStart: 0
| |
| − | }
| |
| − | },
| |
| − | series: [{
| |
| − | name: 'Synechococcus 7942',
| |
| − | data: yValueArray,
| |
| − | }]
| |
| − | });
| |
| − | }
| |
| − | MakeChart()
| |
| − | </script>
| |
| − | </html>
| |
| − | {{Scroll}}
| |
| | {{UCSC-Footer}} | | {{UCSC-Footer}} |
