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<p class="lead">The modeling can be divided in 4 sections. In <b>section 1</b>, the csgA production rate, intracellular csgA concentration and csgB membrane concentration are determined. As our csgA production is controlled under the induction of rhamnose, all rates and concentrations are calculated for two levels of induction (0.2% (w/v) and 0.5% (w/v)) rhamnose. In <b>section 2</b>, the rates and concentrations are used in a grid model which is able to make an estimate on the characteristic time of curli formation. In <b>section 3</b> the characteristic time for curli formation is used to predict the strength of the biofilm. Finally, in <b>section 4</b>, an application in MATLAB is presented able to calculate the printing time of a certain figure or shape with our Biolinker printer. To go to either of these sections, click on one of the buttons below or select your section of interest in the modeling submenu!</p>
 
<p class="lead">The modeling can be divided in 4 sections. In <b>section 1</b>, the csgA production rate, intracellular csgA concentration and csgB membrane concentration are determined. As our csgA production is controlled under the induction of rhamnose, all rates and concentrations are calculated for two levels of induction (0.2% (w/v) and 0.5% (w/v)) rhamnose. In <b>section 2</b>, the rates and concentrations are used in a grid model which is able to make an estimate on the characteristic time of curli formation. In <b>section 3</b> the characteristic time for curli formation is used to predict the strength of the biofilm. Finally, in <b>section 4</b>, an application in MATLAB is presented able to calculate the printing time of a certain figure or shape with our Biolinker printer. To go to either of these sections, click on one of the buttons below or select your section of interest in the modeling submenu!</p>
  
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Revision as of 01:40, 18 September 2015

Modeling

..

Overview

Our main goal is to make a reproducible biofilm. The strength of the biofilm is determined by the degree of intercellular connectivity through curli fibers. With modeling, it is possible to determine which factors have a strong influence on the intercellular connectivity. For instance, one could argue that a higher csgB nucleator production would lead to more curli fibers and therefore an improved connectivity. But if the csgA production would be limiting, solely short curli chains would be formed possibly preventing the cells to even interconnect.

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Fig. 1: Illustration depicting the cells interconnecting by curli fibers. The csgA protein is secreted by the cells to the extracellular space. A curli can be formed when the csgA reacts with the nucleator protein csgB, located at the outer membrane of the cells.

The modeling can be divided in 4 sections. In section 1, the csgA production rate, intracellular csgA concentration and csgB membrane concentration are determined. As our csgA production is controlled under the induction of rhamnose, all rates and concentrations are calculated for two levels of induction (0.2% (w/v) and 0.5% (w/v)) rhamnose. In section 2, the rates and concentrations are used in a grid model which is able to make an estimate on the characteristic time of curli formation. In section 3 the characteristic time for curli formation is used to predict the strength of the biofilm. Finally, in section 4, an application in MATLAB is presented able to calculate the printing time of a certain figure or shape with our Biolinker printer. To go to either of these sections, click on one of the buttons below or select your section of interest in the modeling submenu!

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Generic placeholder image Generic placeholder image Generic placeholder image

References

“FACS-optimized mutants of the green fluorescent protein (GFP)”, Brendan P. Cormack, Raphael H. Valdivia and Stanley Falkow, Gene, 173 (1996) 33-38

“Automated Live Cell Imaging of Green Fluorescent Protein Degradation in Individual Fibroblasts”, Michael Halter, Alex Tona, Kiran Bhadriraju, Anne L. Plant, John T. Elliott, Cytometry Part A ,71A: 827-834, (2007)

“Optimization of an E. coli L-rhamnose-inducible expression vector: test of various genetic module combinations”, Angelika Wegerer, Tianqi Sun and Josef Altenbuchner, BMC Biotechnology 2008, 8:2

https://greenfluorescentblog.wordpress.com/tag/gfpmut3/, (2012)

“Measuring the activity of BioBrick promoters using an in vivo reference standard”, Jason R Kelly, Adam J Rubin, Joseph H Davis, Caroline M Ajo-Franklin, John Cumbers, Michael J Czar, Kim de Mora, Aaron L Glieberman, Dileep D Monie and Drew Endy, Journal of Biological Engineering 2009, 3:4

“Alternative Approach To Modeling Bacterial Lag Time, Using Logistic Regression as a Function of Time, Temperature, pH, and Sodium Chloride Concentration”, Shige Koseki and Junko Nonaka, FEMS Yeast Res 14 (2014) 586–600

“Gatekeeper residues in the major curlin subunit modulate bacterial amyloid fiber biogenesis”, Xuan Wang, Yizhou Zhou, Juan-Jie Ren, Neal D. Hammer, and Matthew R. Chapman, PNAS, (2010), vol. 107., no.1, 163-168

http://parts.igem.org/Part:BBa_K914003

Secretion and functional display of fusion proteins through the curli biogenesis pathway, Van Gerven N, Goyal P, Vandenbussche G, De Kerpel M, Jonckheere W, De Greve H, Remaut H, Molecular Microbiology (2014) 91(5), 1022–1035