Difference between revisions of "Team:UCSC/Model"
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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 | + | <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 DMB 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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Revision as of 02:45, 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.
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.
$$\frac{0.449\ mmol\ FWY}{1g\ biomass}\approx \frac{0.449\ mmol\ chor.}{1g\ biomass}\rightarrow\frac{1\ mol\ acet.}{3\ mol\ chor.}=\frac{0.15\ mmoles\ acet}{1\ g\ biomass}\times\frac{151.163g\ acet.}{1\ mol\ acet.}=\frac{22.62mg\ acet.}{1g\ biomass}$$
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.
$$\frac{0.093\ g\ FYW}{1\ g\ protein}\times\frac{0.6g protein}{1\ biomass}=\frac{0.056\ g\ FYW}{1g\ biomass}\rightarrow\frac{0.37\ mmol\ chor}{1\ g\ biomass}\times\frac{1\ mol\ acet}{3\ mol\ chor}\times\frac{151.163g\ acet}{1\ mol\ acet.}=\frac{18.61mg\ acet.}{1g\ biomass}$$$$\frac{0.051 g\ FYW}{1 g\ protein} * \frac{0.6 g\ protein}{1g\ biomass} = \frac{0.031g\ FWY}{1 g\ biomass} \rightarrow \frac{0.0.2973\ mmol\ chor}{1\ g\ biomass} * \frac{1\ mol\ acet}{3\ mol\ chor} *\frac{151.163\ g}{1 mol\ acet} = \frac{14.9\ mg\ acet}{1 g\ biomass}$$
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 DMB 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.
Light Intensity: μE m-2 s-1
Temperature: ℃
Starting Density: g biomass/ L
