Difference between revisions of "Team:Stanford-Brown/SB16 Modeling"

 
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<li><a href="https://2016.igem.org/Team:Stanford-Brown/Engagement">Outreach</a></li>
 
<li><a href="https://2016.igem.org/Team:Stanford-Brown/Engagement">Outreach</a></li>
 
 
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<li><a href="https://2016.igem.org/Team:Stanford-Brown/SB16_Practices_Exploration">Exploration</a></li>
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<li><a href="https://2016.igem.org/Team:Stanford-Brown/SB16_Practices_Exploration">Life Beyond the Lab</a></li>
 
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<h1 class="sectionTitle-L firstTitle">Protein Optimization & Gibson Assembly Primer design</h1>
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<h2 class="subHead">Overview</h2>
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<h2 class="subHead">pABA Elementary Flux Mode Analysis</h2>
 
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      <div class="col-sm-12 pagetext">The purpose of this script is to expedite the process of protein DNA sequence optimization and Gibson Primer design for Gibson assembly reactions.  The files for the script can be found either <a href="https://github.com/gsun1729/Prot-Optimization-Gibson-Assembly">here on github</a> or be downloaded individually from the IGEM database below.  <br><br>
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      <div class="col-sm-12 pagetext">We wanted to use elementary flux mode analysis to identify targets for gene knockout/gene overexpression. Elementary flux mode analysis allows us to calculate the elementary flux modes, which are analogous to the 'metabolic freedom' of the metabolic model. Using the EFMs, we are able to understand all of the "potential capabilities" of the organism. We then examine the metabolic network for different things we want to have done, and things we want to eliminate in order to increase pABA production. Using this, we select targets for knockout/overexpression. <br><br>
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Note because IGEM does not support .py file extensions, you will need to remove .txt from the extensions of all of the downloaded files from the links below and append ".py" to the end of (1) seq_analyzer, (2) seq_tools, (3) input_tools, and (4) format_tools (i.e. "seq_tools.txt" becomes "seq_tools.py.") No further formatting is needed for the github link download.<br>
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<ul>
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  <li><a href="https://static.igem.org/mediawiki/2016/1/1f/T--Stanford-Brown--POGAS_seq_analyzer.txt">seq_analyzer</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/b/b7/T--Stanford-Brown--POGAS_seq_tools.txt">seq_tools</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/e/e5/T--Stanford-Brown--POGAS_input_tools.txt">input_tools</a>
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  <li><a href="https://2016.igem.org/File:T--Stanford-Brown--POGAS_format_tools.txt">format_tools</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/d/d1/T--Stanford-Brown--POGAS_NT_Lib.txt">NT_Lib</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/6/60/T--Stanford-Brown--POGAS_codonlib.txt">Codon_Lib</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/9/90/T--Stanford-Brown--POGAS_restriction_enzymes.txt">restriction_enzymes</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/d/d5/T--Stanford-Brown--POGAS_ecoli.txt">ecoli</a>
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In order to calculate the EFMs, we used efmtool <a href="http://www.csb.ethz.ch/tools/software/efmtool.html">[1]</a> from ETH Zurich. We also used several MATLAB scripts from Dr. Nils Averesch to calculate product yields from the EFM model. <br><br>
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  <li><a href="https://static.igem.org/mediawiki/2016/4/41/T--Stanford-Brown--POGAS_Ecolik12.txt">ecoliK12</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/f/f5/T--Stanford-Brown--POGAS_yeast.txt">yeast</a>
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Sample input files are also included in the repository and are listed below
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  <li><a href="https://static.igem.org/mediawiki/2016/1/1f/T--Stanford-Brown--POGAS_seq_analyzer.txt">seq_analyzer</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/b/b7/T--Stanford-Brown--POGAS_seq_tools.txt">seq_tools</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/e/e5/T--Stanford-Brown--POGAS_input_tools.txt">input_tools</a>
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  <li><a href="https://2016.igem.org/File:T--Stanford-Brown--POGAS_format_tools.txt">format_tools</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/d/d1/T--Stanford-Brown--POGAS_NT_Lib.txt">NT_Lib</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/6/60/T--Stanford-Brown--POGAS_codonlib.txt">Codon_Lib</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/9/90/T--Stanford-Brown--POGAS_restriction_enzymes.txt">restriction_enzymes</a>
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  <li><a href="https://static.igem.org/mediawiki/2016/d/d5/T--Stanford-Brown--POGAS_ecoli.txt">ecoli</a>
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We were interested in finding ways to produce a larger amount of pABA. In a standard, unmodified metabolic network, the amount of pABA being produced is relatively little. The calculated product yield was: <br>
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  <li><a href="https://static.igem.org/mediawiki/2016/4/41/T--Stanford-Brown--POGAS_Ecolik12.txt">ecoliK12</a>
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Product_max_without_RX = 49.6064<br><br>
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  <li><a href="https://static.igem.org/mediawiki/2016/f/f5/T--Stanford-Brown--POGAS_yeast.txt">yeast</a>
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The biomass vs. product yield graph is shown below:
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      <h2 class="subHead">Input File Formatting</h2>
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<img src="https://static.igem.org/mediawiki/2016/9/93/T--Stanford-Brown--pabFig.png" class="img-L"/>
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<div class="col-sm-12 pagetext">In order to run the program, the user has to provide a text file containing the sequences in need of optimization and/or primer design. Each sequence should be included on a separate line; any blank newline entries and spaces will result in a processing error. The sequences should also contain no other characters other than that representing nucleotides (A,T,G,C) or amino acids (G,A,L,M,F,W,K,Q,E,S,P,V,I,C,Y,H,R,N,D,T,*).
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For protein optimization, sequences in the input file are not limited to only amino acid or nucleotide sequences--the user can input either and the program will recognize the sequence type and process it accordingly. This is limited however in cases where the only amino acids in the sequence are alanine, threonine, cysteine, and glycine, since the single letter code for each amino acid is also found in the single letter nucleotide representations. For these cases, the program will be unable to distinguish between an amino acid or nucloetide sequence--this however can be corrected by putting a "*" at the end of an amino acid sequence.
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<div class="col-sm-12 pagetext">We generated a metabolic network where R34 (Phosphotransferase system (EC 2.7.1.69)) was removed. Our reasoning was that glucose is preferentially taken up through the phosphotransferase system, which consumes PEP. In our case, PEP is also the precursor to the Shikimate pathway. By changing the glucose uptake mechanism, we expect to see an increase in the amount of pABA. With R34 removed, the results were:
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<br><br>Hexokinase (EC 2.7.1.1)<br><br>
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<ol>
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  <li>First sequence contains at least 50nt of the 3' end of the backbone where the 5' end of the first fragment will join to</li>
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  <li>N number of sequences to be assembled ...</li>
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  <li>Last sequence contains the 5' end of the backbone where the the 3' end of the last fragment will join to This is illustrated in the following figure, where the first sequence in the file is "Backbone front", last sequence is "Backbone rear", and the middle sequences in the file would be the N fragments listed in order of assembly.</li>
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Product_max_without_RX = 92.4829
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<img src="https://static.igem.org/mediawiki/2016/2/20/T--Stanford-Brown--ProtOpt_Frags.png" class="img-L"/>
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<div class="col-sm-12 pagetext">From these results, we can identify R34 as a possible target for gene knockout.
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Latest revision as of 03:17, 20 October 2016


Stanford-Brown 2016

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pABA Elementary Flux Mode Analysis

We wanted to use elementary flux mode analysis to identify targets for gene knockout/gene overexpression. Elementary flux mode analysis allows us to calculate the elementary flux modes, which are analogous to the 'metabolic freedom' of the metabolic model. Using the EFMs, we are able to understand all of the "potential capabilities" of the organism. We then examine the metabolic network for different things we want to have done, and things we want to eliminate in order to increase pABA production. Using this, we select targets for knockout/overexpression.

In order to calculate the EFMs, we used efmtool [1] from ETH Zurich. We also used several MATLAB scripts from Dr. Nils Averesch to calculate product yields from the EFM model.

We were interested in finding ways to produce a larger amount of pABA. In a standard, unmodified metabolic network, the amount of pABA being produced is relatively little. The calculated product yield was:
Product_max_without_RX = 49.6064

The biomass vs. product yield graph is shown below:
We generated a metabolic network where R34 (Phosphotransferase system (EC 2.7.1.69)) was removed. Our reasoning was that glucose is preferentially taken up through the phosphotransferase system, which consumes PEP. In our case, PEP is also the precursor to the Shikimate pathway. By changing the glucose uptake mechanism, we expect to see an increase in the amount of pABA. With R34 removed, the results were:

Hexokinase (EC 2.7.1.1)

Product_max_without_RX = 92.4829
From these results, we can identify R34 as a possible target for gene knockout.