Difference between revisions of "Team:RHIT/MetabolismModel"

 
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Metabolism Model
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<h4>Metabolism Model</h4>
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<p>The FBA model allows us to run quick simulations on the growth of the E. coli on various environmental conditions [source]. Below is a specific simulation with just FBA, assuming the E. coli was growing in aerobic conditions on various carbon sources, all at the maximum flux of -10 mM/(gram of DW * hr). </p>
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<p>The FBA model allows us to run quick simulations on the growth of the <em>E. coli </em>on various environmental conditions [1]. Below is a specific simulation with just FBA, assuming the <em>E. coli</em> was growing in aerobic conditions on various carbon sources, all at the maximum flux of -10 mM/(gram of DW * hr). </p>
  
 
<p>The yield was predicted through maximizing the fluxes of reactions ATP synthase (with 4 protons per ATP) and both polyphosphate kinases. </p>
 
<p>The yield was predicted through maximizing the fluxes of reactions ATP synthase (with 4 protons per ATP) and both polyphosphate kinases. </p>
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Table 3. Conversion of growth flux to doubling time<br />
 
Table 3. Conversion of growth flux to doubling time<br />
 
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<p>The yield was predicted through maximizing the fluxes of reactions ATP synthase (with 4 protons per ATP) and both polyphosphate kinases. It is known that E. coli can double at a faster rate of 20 mins, however this model is scaled to wild type variety that is not necessarily optimized to grow at top speed. The growth flux for E.coli just on glucose is the default setting of the model. To scale it to 20 min doubling time requires only scaling the biomass growth reaction in the model to the values needed for doubling time. For our simulation, we opted for default setting and more realistic timescales for these transformed bacteria.</p>
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<p>The yield was predicted through maximizing the fluxes of reactions ATP synthase (with 4 protons per ATP) and both polyphosphate kinases. It is known that <em>E. coli</em> can double at a faster rate of 20 mins, however this model is scaled to wild type variety that is not necessarily optimized to grow at top speed. The growth flux for E.coli just on glucose is the default setting of the model. To scale it to 20 min doubling time requires only scaling the biomass growth reaction in the model to the values needed for doubling time. For our simulation, we opted for default setting and more realistic timescales for these transformed bacteria.</p>
 
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<h3>References:</h3>
 
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<p>[1] J. D. Orth, et al. “A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011.” Molecular System Biology, October 2011. Vol. 7, no.535. [Online].  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261703/<br />  
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[2] S. Cayley, B.A. Lewis, H.J. Guttman, and M.T. Record Jr. “Characterization of the cytoplasm of Escherichia coli K-12 as a function of external osmolarity. Implications for protein-DNA interactions in vivo.” Journal of Molecular Biology, vol. 222 no. 2, 1991, pp. 281-300. [Online]. http://bionumbers.hms.harvard.edu/bionumber.aspx?id=103905&ver=13&trm=e%20coli%20weight&org<br />   
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<li>[1] J. D. Orth, et al. “A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011.” Molecular System Biology, October 2011. Vol. 7, no.535. [Online].  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261703/</li>  
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[3] L. Wang, Y. J. Zhou, D. Ji, and Z.K. Zhao, “An accurate method for estimation of the intracellular aqueous volume of Escherichia coli cells,” Journal of Microbiological Methods, 2013, p. 8. [Online]. http://bionumbers.hms.harvard.edu/bionumber.aspx?id=108813&ver=3&trm=e%20coli%20cell%20volume&org<br />  
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<li>[2] S. Cayley, B.A. Lewis, H.J. Guttman, and M.T. Record Jr. “Characterization of the cytoplasm of Escherichia coli K-12 as a function of external osmolarity. Implications for protein-DNA interactions in vivo.” Journal of Molecular Biology, vol. 222 no. 2, 1991, pp. 281-300. [Online]. http://bionumbers.hms.harvard.edu/bionumber.aspx?id=103905&ver=13&trm=e%20coli%20weight&org</li>   
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[4] H.W. Blanch and D.S. Clark, “Unstructured models of Microbial Growth,” in Biochemical Engeering. Boca Raton: Taylor & Francis Group, 1997, pp. 185-188.
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<li>[3] L. Wang, Y. J. Zhou, D. Ji, and Z.K. Zhao, “An accurate method for estimation of the intracellular aqueous volume of Escherichia coli cells,” Journal of Microbiological Methods, 2013, p. 8. [Online]. http://bionumbers.hms.harvard.edu/bionumber.aspx?id=108813&ver=3&trm=e%20coli%20cell%20volume&org</li>  
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<li>[4] H.W. Blanch and D.S. Clark, “Unstructured models of Microbial Growth,” in Biochemical Engeering. Boca Raton: Taylor & Francis Group, 1997, pp. 185-188. </li>
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Latest revision as of 01:54, 11 September 2018




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Metabolism Model




The FBA model allows us to run quick simulations on the growth of the E. coli on various environmental conditions [1]. Below is a specific simulation with just FBA, assuming the E. coli was growing in aerobic conditions on various carbon sources, all at the maximum flux of -10 mM/(gram of DW * hr).

The yield was predicted through maximizing the fluxes of reactions ATP synthase (with 4 protons per ATP) and both polyphosphate kinases.

Simulations:

Table 1. List of parameters used in the metabolism model.
Table 2. Theoretical ATP Production of E.coli (1366 model) growing on different media. Table 3. Conversion of growth flux to doubling time

The yield was predicted through maximizing the fluxes of reactions ATP synthase (with 4 protons per ATP) and both polyphosphate kinases. It is known that E. coli can double at a faster rate of 20 mins, however this model is scaled to wild type variety that is not necessarily optimized to grow at top speed. The growth flux for E.coli just on glucose is the default setting of the model. To scale it to 20 min doubling time requires only scaling the biomass growth reaction in the model to the values needed for doubling time. For our simulation, we opted for default setting and more realistic timescales for these transformed bacteria.

References:

  • [1] J. D. Orth, et al. “A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011.” Molecular System Biology, October 2011. Vol. 7, no.535. [Online]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261703/
  • [2] S. Cayley, B.A. Lewis, H.J. Guttman, and M.T. Record Jr. “Characterization of the cytoplasm of Escherichia coli K-12 as a function of external osmolarity. Implications for protein-DNA interactions in vivo.” Journal of Molecular Biology, vol. 222 no. 2, 1991, pp. 281-300. [Online]. http://bionumbers.hms.harvard.edu/bionumber.aspx?id=103905&ver=13&trm=e%20coli%20weight&org
  • [3] L. Wang, Y. J. Zhou, D. Ji, and Z.K. Zhao, “An accurate method for estimation of the intracellular aqueous volume of Escherichia coli cells,” Journal of Microbiological Methods, 2013, p. 8. [Online]. http://bionumbers.hms.harvard.edu/bionumber.aspx?id=108813&ver=3&trm=e%20coli%20cell%20volume&org
  • [4] H.W. Blanch and D.S. Clark, “Unstructured models of Microbial Growth,” in Biochemical Engeering. Boca Raton: Taylor & Francis Group, 1997, pp. 185-188.