Difference between revisions of "Team:UCSC/Model"

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     <h3>Background</h3>
 
     <h3>Background</h3>
       <p>The purpose of modeling is to carefully examine the pathways of each intended biosynthetic products, look for ways to optimize production, understand limiting factors, and support the team in wet lab. To accomplish these goals, we read dozens of different academic papers, sorted through metabolic pathways, and used several different methods to model production of acetaminophen and B<sub>12</sub>. Each of these modeling methods has different assumptions which allow these data-points to be averaged; providing reasonable quantitative estimates of our biosynthetic products.</p>
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       <p>The purpose of modeling is to carefully examine the pathways of each intended biosynthetic products, look for ways to optimize production, and understand limiting factors. To accomplish these goals, we used available metabolic pathways for our target organism, and evaluated several different methods to model production of acetaminophen and B<sub>12</sub> in cyanobacteria. Each of these modeling methods has different assumptions which allow these data to be averaged; providing reasonable quantitative estimates of our biosynthetic products.</p>
 
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<h2 style="text-align: left; font-weight: 500;">Overview</h2>
 
<h2 style="text-align: left; font-weight: 500;">Overview</h2>
     <p>To predict acetaminophen biosynthesis, we analyzed the abundance of the acetaminophen's precursor, chorismate. Chorismate is a precursor primarily for the three aromatic amino acids, as well as salicylic acid, folate, vitamins, and many alkaloids<sup>[1]</sup>. We used published data on these products and gene transcription of amino acids for multiple species of cyanobacteria to assume the chorismate pool available to be processed into acetaminophen. Following that, we compared enzyme binding affinity K<sub>m</sub>'s by creating a simulation of chorismate metabolism in Python to approximate enzyme rates. Insufficient data on cyanobacterial K<sub>m</sub> values necessitated using data from other bacterial species and comparing sequence identities to find the best match with our enzymes. We assumed the highest matching sequence K<sub>m</sub>'s would be analagous to our enzyme's rate. With this information, we made a quantitative metabolic model which estimates how much of our precursor goes down each pathway.</p>
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     <p>To predict acetaminophen biosynthesis, we analyzed the abundance of the acetaminophen's precursor, chorismate. Chorismate is primarily used by the cell to produce the three aromatic amino acids, but is also used to synthesize salicylic acid, folate, vitamins, and many alkaloids<sup>[1]</sup>. We used public data on these chorismate products and gene transcription of amino acids for multiple species of cyanobacteria to estimate the chorismate pool available for acetaminophen production. Using enzyme binding affinity K<sub>m</sub>'s we created a simulation of chorismate metabolism in Python to approximate enzyme rates. Insufficient data on cyanobacterial K<sub>m</sub> values necessitated using data from other related bacterial species. We assumed the enzymes with the highest matching sequence identity to <i>S. elongatus</i> PCC 7942 and <i>A. platensis</i> would have K<sub>m</sub>'s close to the enzyme rates of <i>S. elongatus</i> PCC 7942 and <i>A. platensis</i>. With this information, we made a quantitative metabolic model which estimates how much of our precursor goes down each pathway.</p>
  
 
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<h2 style="text-align: left; font-weight: 500;">Amino Acid Method</h2>
 
<h2 style="text-align: left; font-weight: 500;">Amino Acid Method</h2>
         <p>Once we found out that acetaminophen was produced from the same precursors as the tryptophan and folate, we found published amino acid composition data for <i>Arthrospira platensis</i> and back converted those amino acids to moles of acetaminophen precursors anthranilate and PABA. These molecules are the direct substrate for our enzyme <i>4ABH</i>, converting it to 4-aminophenol before <i>nHoa</i> converts it to acetaminophen. We show several different calculations below using different sources of data.</p>
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         <p>Because chorismate is used as a precursor for the aromatic amino acids, we used the amino acid concentration as a proxy for the chorismate concentration. The amino acid composition for <i>A. platensis</i> is publicly available, and we used that to calculate the moles per cell of the amino acid precursors anthranilate and PABA. These molecules are the direct substrate for our enzyme <i>4ABH</i>, which converts anthranilate and PABA to the intermediate 4-aminophenol before <i>nhoA</i> converts that to acetaminophen. We show several different calculations below using different sources of data.</p>
  
  
 
<h2 style="text-align: left; font-weight: 400;">Assumptions</h2>
 
<h2 style="text-align: left; font-weight: 400;">Assumptions</h2>
 
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                 <li><i>Synechococcus</i> and <i>Arthrospira platensis</i> have similar amino acid ratios. Since there was no available amino acid data for our transformed <i>Synechococcus</i>, we must assume our species has a similar ratio to the more well described <i>Arthrospira platensis</i>.</li>
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                 <li><i>Synechococcus</i> and <i>Arthrospira platensis</i> have similar amino acid ratios. Since there was no available amino acid data for <i>Synechococcus</i>, we assumed it has a similar ratio to the more well described <i>Arthrospira platensis</i>.</li>
 
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                 <li>The amount of tryptophan and folate are equal to the moles of their precursors, anthranilate and PABA.</li>
 
                 <li>The amount of tryptophan and folate are equal to the moles of their precursors, anthranilate and PABA.</li>
 
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                 <br>
                 <li>Of the available precursors, 30% will go down our pathway. This is based off of K<sub>m</sub> ratios of <i>4ABH</i> and its competitor <i>TrpD</i>.</li>
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                 <li>Based on the K<sub>m</sub> ratios of <i>4ABH</i> and its competitor <i>TrpD</i>, 30% of the available precursors will go down the acetaminophen pathway.</li>
 
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           <p>To validate our organism's quantity of chorismate precursor, we used a custom Python program to convert DNA sequences to amino acids and calculate molar and mass percentages of chorismate derived aromatic amino acids. We ran both the genome and ribosomal protein sequences through our program, which resulted in 9.3% and 5.14% by mass aromatic amino acids. Using our higher sequence 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.</p>
+
           <p>To validate <i>Synechococcus's</i> quantity of chorismate precursor, we used a Python script to convert coding DNA sequences to amino acids and calculate the percentage of aromatic amino acids to estimate the available chorismate. The aromatic amino acids make up 9.3% and 5.14% of the total amino acid by mass. Using 9.3% and the assumption that 30% of the available chorismate would be used by our engineered pathway, we estimate our cells would produce 18.61mg (&#177; 1.63mg) of acetaminophen per 1gram of dried biomass of <i>Synechococcus</i>.</p>
  
  
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           <p>These numbers show that there will probably be enough precursor to produce a useful, detectable quantity of acetaminophen.  Based on literature and sequence estimates of aromatic amino acids, we can assume there would be at least that many moles of chorismate from which our added pathway pushes towards acetaminophen. The three calculations above can be averaged to finally predict 18.61mg &#177; 1.63mg acetaminophen per gram of <i>Synechococcus</i> biomass.</p>
 
           <p>These numbers show that there will probably be enough precursor to produce a useful, detectable quantity of acetaminophen.  Based on literature and sequence estimates of aromatic amino acids, we can assume there would be at least that many moles of chorismate from which our added pathway pushes towards acetaminophen. The three calculations above can be averaged to finally predict 18.61mg &#177; 1.63mg acetaminophen per gram of <i>Synechococcus</i> biomass.</p>
         <p>We used different chorismate concentration estimates to reach several different estimates for acetaminophen, averaging 18.61mg &#177; 1.63mg acetaminophen per gram of <i>Synechococcus</i> biomass. This would be a sufficient quantity to detect through HPLC and serve as a starting point for optimizing production. This means that one 325mg dose of acetaminophen could be obtained in ~17g of biomass, meaning a 12 by 3 feet round pool could produce enough acetaminophen for more than 500 people every 10 days.</p>
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         <p>We used different chorismate concentration estimates to reach several different estimates for acetaminophen, averaging 18.61mg &#177; 1.63mg acetaminophen per gram of <i>Synechococcus</i> biomass. 18.61mg is enough acetaminophen to be detected by HPLC. This means that one 325mg dose of acetaminophen could be obtained with ~17g of <i>Synechococcus</i>. Scaling up, a 12 by 3 feet round pool could produce enough acetaminophen for more than 500 people every 10 days.</p>
  
 
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             <p>Predicting the quantity of active form of B<sub>12</sub> produced depends on a successful metabolism of the active lower ligand, 5,6-dimethyl-benzimidizole (5,6-DMB) and the quantity of cobalt which binds to our engineered product. Cobalt is a limiting factor for growth in many marine environments, limiting formation of B<sub>12</sub>’s large corrin-ring. Even for species that use adenine to bind to the lower ligand and form pseudo-B<sub>12</sub>, cobalt supplementation enhances growth and thus is likely the limiting precursor for B<sub>12</sub> production<sup>[5]</sup>. The gene <i>BluB</i> was inserted to enzymatically convert FMNH<sub>2</sub> to our activating lower ligand 5,6-DMB. With this ligand available and our second gene insert, <i>BluB</i>, which preferentially attaches 5,6-DMB to the cobalt, the vast majority of our B<sub>12</sub> should be made in active form. The gene that creates the lower ligand from the FMNH<sub>2</sub>, <i>Ssue</i>, came from <i>Synechococcus elongatus 7002</i> 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 <i>Arthrospira platensis</i> produces between 150-250µg pseudo-cobalamin per hundred grams dry weight with the non-human-usable adenine as the lower ligand<sup>[6]</sup>.  If we assume our <i>Blub</i>/<i>CobC</i> enzyme complex works as well as it does in its origin organism, as assayed in Microbial Cell Factories paper<sup>[7]</sup>, 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 B<sub>12</sub> resulting in a production of almost exactly the Recommended Daily Value of 6µg B<sub>12</sub> per 3g serving.</p>
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             <p>Predicting the quantity of DMB B<sub>12</sub> produced depends on a successful metabolism of the active lower ligand, 5,6-dimethyl-benzimidizole (5,6-DMB) and the quantity of cobalt which binds to our engineered product. Cobalt is a limiting factor for growth in many marine environments, limiting formation of B<sub>12</sub>’s large corrin-ring. Even for species that use adenine to bind to the lower ligand and form B<sub>12</sub> analogs, cobalt supplementation enhances growth and thus is likely the limiting precursor for B<sub>12</sub> production<sup>[5]</sup>. The gene <i>BluB</i> was inserted to enzymatically convert FMNH<sub>2</sub> to our activating lower ligand 5,6-DMB. With this ligand available and our second gene insert, <i>BluB</i>, which preferentially attaches 5,6-DMB to the cobalt, biases B<sub>12</sub> to the DMB form. The gene that creates the lower ligand from the FMNH<sub>2</sub>, <i>Ssue</i>, came from <i>Synechococcus elongatus 7002</i> 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 <i>Arthrospira platensis</i> produces between 150-250µg pseudo-cobalamin per hundred grams dry weight with the non-human-usable adenine as the lower ligand<sup>[6]</sup>.  If we assume our <i>Blub</i>/<i>CobC</i> enzyme complex works as well as it does in its origin organism, as assayed in Microbial Cell Factories paper<sup>[7]</sup>, 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 B<sub>12</sub> resulting in a production of almost exactly the Recommended Daily Value of 6µg B<sub>12</sub> per 3g serving.</p>
 
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           <p>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 to fit our data to an equation, we modelled dried biomass and cell count with respect to time. We have also used growth optimization papers<sup>[8,9]</sup> to add additional dependent variables of temperature, light intensity, and starter culture density to our equation.</p>
+
           <p>To understand the production capacity of our organism, we aggregated growth data from published papers and all of our own growth data.  Using carrying-capacity-limited logistic growth curves to fit our data to an equation, we modelled dried biomass and cell count with respect to time. We have also used growth optimization papers<sup>[8,9]</sup> to add additional dependent variables of temperature, light intensity, and starter culture density to our equation.</p>
  
 
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Revision as of 02:22, 1 November 2017


MODELING

Predict and optimize yield




Background

The purpose of modeling is to carefully examine the pathways of each intended biosynthetic products, look for ways to optimize production, and understand limiting factors. To accomplish these goals, we used available metabolic pathways for our target organism, and evaluated several different methods to model production of acetaminophen and B12 in cyanobacteria. Each of these modeling methods has different assumptions which allow these data to be averaged; providing reasonable quantitative estimates of our biosynthetic products.

ACETAMINOPHEN



Overview

To predict acetaminophen biosynthesis, we analyzed the abundance of the acetaminophen's precursor, chorismate. Chorismate is primarily used by the cell to produce the three aromatic amino acids, but is also used to synthesize salicylic acid, folate, vitamins, and many alkaloids[1]. We used public data on these chorismate products and gene transcription of amino acids for multiple species of cyanobacteria to estimate the chorismate pool available for acetaminophen production. Using enzyme binding affinity Km's we created a simulation of chorismate metabolism in Python to approximate enzyme rates. Insufficient data on cyanobacterial Km values necessitated using data from other related bacterial species. We assumed the enzymes with the highest matching sequence identity to S. elongatus PCC 7942 and A. platensis would have Km's close to the enzyme rates of S. elongatus PCC 7942 and A. platensis. With this information, we made a quantitative metabolic model which estimates how much of our precursor goes down each pathway.


Chorismate is processed into the aromatic amino acids, phenylalanine, tyrosine, tryptophan, and folate. Our inserted enzymes metabolize PABA and anthranilate to make 4-aminophenol which is then processed by nHoa to make acetaminophen.

Amino Acid Method

Because chorismate is used as a precursor for the aromatic amino acids, we used the amino acid concentration as a proxy for the chorismate concentration. The amino acid composition for A. platensis is publicly available, and we used that to calculate the moles per cell of the amino acid precursors anthranilate and PABA. These molecules are the direct substrate for our enzyme 4ABH, which converts anthranilate and PABA to the intermediate 4-aminophenol before nhoA converts that to acetaminophen. We show several different calculations below using different sources of data.

Assumptions

  • Synechococcus and Arthrospira platensis have similar amino acid ratios. Since there was no available amino acid data for Synechococcus, we assumed it has a similar ratio to the more well described Arthrospira platensis.

  • The amount of tryptophan and folate are equal to the moles of their precursors, anthranilate and PABA.

  • Based on the Km ratios of 4ABH and its competitor TrpD, 30% of the available precursors will go down the acetaminophen pathway.



$$\frac{0.449\ mmol\ FWY}{1g\ biomass}\approx \frac{0.449\ mmol\ chor.}{1g\ biomass}\rightarrow\frac{1\ mol\ acet.}{3\ mol\ chor.}=\frac{0.15\ mmoles\ acet}{1\ g\ biomass}\times\frac{151.163g\ acet.}{1\ mol\ acet.}=\frac{22.62mg\ acet.}{1g\ biomass}$$
This estimate for acetaminophen production uses averaged literature data of aromatic amino acids (phenylalanine, tryptophan, and tyrosine, FWY) from Arthrospira platensis[2,3] and assuming one third of the chorismate precursor goes to our inserted enzyme, 4ABH.

To validate Synechococcus's quantity of chorismate precursor, we used a Python script to convert coding DNA sequences to amino acids and calculate the percentage of aromatic amino acids to estimate the available chorismate. The aromatic amino acids make up 9.3% and 5.14% of the total amino acid by mass. Using 9.3% and the assumption that 30% of the available chorismate would be used by our engineered pathway, we estimate our cells would produce 18.61mg (± 1.63mg) of acetaminophen per 1gram of dried biomass of Synechococcus.

$$\frac{0.093\ g\ FYW}{1\ g\ protein}\times\frac{0.6g protein}{1\ biomass}=\frac{0.056\ g\ FYW}{1g\ biomass}\rightarrow\frac{0.37\ mmol\ chor}{1\ g\ biomass}\times\frac{1\ mol\ acet}{3\ mol\ chor}\times\frac{151.163g\ acet}{1\ mol\ acet.}=\frac{18.61mg\ acet.}{1g\ biomass}$$
This estimate is based on aromatic amino acids quantities calculated by translating the organism's 3MB genome and assuming a third of precursor goes to our pathway. This equation assumes that Synechococcus's translated genome is an accurate representation of its amino acid production.

$$\frac{0.051 g\ FYW}{1 g\ protein} * \frac{0.6 g\ protein}{1g\ biomass} = \frac{0.031g\ FWY}{1 g\ biomass} \rightarrow \frac{0.0.2973\ mmol\ chor}{1\ g\ biomass} * \frac{1\ mol\ acet}{3\ mol\ chor} *\frac{151.163\ g}{1 mol\ acet} = \frac{14.9\ mg\ acet}{1 g\ biomass}$$
This equation assumes translated ribosomal amino acid composition is similar to total cellular amino acid composition due to ribosomal proteins being highly expressed in cells, composing 9-22% of all proteins by mass[4].

These numbers show that there will probably be enough precursor to produce a useful, detectable quantity of acetaminophen. Based on literature and sequence estimates of aromatic amino acids, we can assume there would be at least that many moles of chorismate from which our added pathway pushes towards acetaminophen. The three calculations above can be averaged to finally predict 18.61mg ± 1.63mg acetaminophen per gram of Synechococcus biomass.


We used different chorismate concentration estimates to reach several different estimates for acetaminophen, averaging 18.61mg ± 1.63mg acetaminophen per gram of Synechococcus biomass. 18.61mg is enough acetaminophen to be detected by HPLC. This means that one 325mg dose of acetaminophen could be obtained with ~17g of Synechococcus. Scaling up, a 12 by 3 feet round pool could produce enough acetaminophen for more than 500 people every 10 days.

VITAMIN B12


Predicting the quantity of DMB B12 produced depends on a successful metabolism of the active lower ligand, 5,6-dimethyl-benzimidizole (5,6-DMB) and the quantity of cobalt which binds to our engineered product. Cobalt is a limiting factor for growth in many marine environments, limiting formation of B12’s large corrin-ring. Even for species that use adenine to bind to the lower ligand and form B12 analogs, cobalt supplementation enhances growth and thus is likely the limiting precursor for B12 production[5]. The gene BluB was inserted to enzymatically convert FMNH2 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, biases B12 to the DMB form. The gene that creates the lower ligand from the FMNH2, Ssue, came from Synechococcus elongatus 7002 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 Arthrospira platensis produces between 150-250µg pseudo-cobalamin per hundred grams dry weight with the non-human-usable adenine as the lower ligand[6]. 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[7], 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.


BIOMASS




To understand the production capacity of our organism, we aggregated growth data from published papers and all of our own growth data. Using carrying-capacity-limited logistic growth curves to fit our data to an equation, we modelled dried biomass and cell count with respect to time. We have also used growth optimization papers[8,9] to add additional dependent variables of temperature, light intensity, and starter culture density to our equation.



Timescale: days
Light Intensity: μE m-2 s-1
Temperature:
Starting Density: g biomass/ L





  • [1] Walsh, C. T., Haynes, S. W., & Ames, B. D. (2012). Aminobenzoates as building blocks for natural product assembly lines. Nat. Prod. Rep., 29(1), 37–59. https://doi.org/10.1039/C1NP00072A
  • [2] Food Composition Databases Show Foods -- Seaweed, spirulina, dried. (n.d.). Retrieved October 27, 2017, from 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=
  • [3] Narasimha, D. L. R., Venkataraman, G. S., Duggal, S. K., & Eggum, B. O. (1982). Nutritional quality of the blue-green alga Spirulina platensis geitler. Journal of the Science of Food and Agriculture, 33(5), 456–460. https://doi.org/10.1002/jsfa.2740330511
  • [4] Dennis, P. P., & Bremer, H. (1974). Macromolecular Composition During Steady-State Growth of Escherichia coli B/r. Journal of Bacteriology, 119(1), 270–281.
  • [5] Panzeca, C., Beck, A. J., Leblanc, K., Taylor, G. T., Hutchins, D. A., & Sañudo-Wilhelmy, S. A. (2008). Potential cobalt limitation of vitamin B12 synthesis in the North Atlantic Ocean. Global Biogeochemical Cycles, 22(2), GB2029. https://doi.org/10.1029/2007GB003124
  • [6] Watanabe, F., Katsura, H., Takenaka, S., Fujita, T., Abe, K., Tamura, Y., … Nakano, Y. (1999). Pseudovitamin B12 Is the Predominant Cobamide of an Algal Health Food, Spirulina Tablets. Journal of Agricultural and Food Chemistry, 47(11), 4736–4741. https://doi.org/10.1021/jf990541b
  • [7] Deptula, P., Kylli, P., Chamlagain, B., Holm, L., Kostiainen, R., Piironen, V., … Varmanen, P. (2015). BluB/CobT2 fusion enzyme activity reveals mechanisms responsible for production of active form of vitamin B12 by Propionibacterium freudenreichii. Microbial Cell Factories, 14. https://doi.org/10.1186/s12934-015-0363-9
  • [8] Kuan, D., Duff, S., Posarac, D., & Bi, X. (2015). Growth optimization of Synechococcus elongatus PCC7942 in lab flasks and a 2-D photobioreactor. The Canadian Journal of Chemical Engineering, 93(4), 640–647. https://doi.org/10.1002/cjce.22154
  • [9] Yan, R., Zhu, D., Zhang, Z., Zeng, Q., & Chu, J. (2012). Carbon metabolism and energy conversion of Synechococcus sp. PCC 7942 under mixotrophic conditions: comparison with photoautotrophic condition. Journal of Applied Phycology, 24(4), 657–668. https://doi.org/10.1007/s10811-011-9683-2