Team:Heidelberg/Modelling/Ind Production

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     <figcaption style="width:200px;"><b>Fig. 1</b> Network graph.</figcaption>
     <figcaption style="width:200px;"><b>Fig. 1</b> Network graph.</figcaption>
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     <figcaption style="width:200px;"><b>Fig. 2</b> Experimental data represented by asterisks, model trajectories represented by lines, 95% confidence interval represented by shaded region.</figcaption>
     <figcaption style="width:200px;"><b>Fig. 2</b> Experimental data represented by asterisks, model trajectories represented by lines, 95% confidence interval represented by shaded region.</figcaption>
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     <figcaption style="width:200px;"><b>Fig. 3</b> Internal states.</figcaption>
     <figcaption style="width:200px;"><b>Fig. 3</b> Internal states.</figcaption>
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     <figcaption style="width:200px;"><b>Fig. 4</b> Robust parameter estimation.</figcaption>
     <figcaption style="width:200px;"><b>Fig. 4</b> Robust parameter estimation.</figcaption>
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     <figcaption style="width:200px;"><b>Fig. 5</b> Identifiability analysis.</figcaption>
     <figcaption style="width:200px;"><b>Fig. 5</b> Identifiability analysis.</figcaption>
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Revision as of 21:32, 26 October 2013

Indigoidine Production. Quantitative dynamic modeling.

Highlights

  • Suitable model for bacterial growth
  • Proper description of Indigoidine Production
  • Toxicity of Indigoidine synthesis for bacteria
  • Optimized production rates
  • Identifiability analysis
  • ...

Abstract

...

Introduction

Based on coupled ordinary differential equations (ODEs). Mathematical modelling allows for Identifiability analysis

Fig. 1 Network graph.
Fig. 2 Experimental data represented by asterisks, model trajectories represented by lines, 95% confidence interval represented by shaded region.
Fig. 3 Internal states.
Fig. 4 Robust parameter estimation.
Fig. 5 Identifiability analysis.

Intro ctd.

Results

The ODE system determining the time evolution of the dynamical variables is given by the following four equations: $$ \mathrm{d}\mathrm{[Bac]}/\mathrm{d}t = -\frac{\mathrm{[Bac]} \cdot \left(\mathrm{[Bac]} - \mathrm{Bacmax\_native\_svp}\right) \cdot \left(\mathrm{beta\_native\_svp} - \mathrm{[Ind]} \cdot \mathrm{ki\_native\_svp}\right)}{\mathrm{Bacmax\_native\_svp}} $$ $$\mathrm{d}\mathrm{[Glu]}/\mathrm{d}t = - \mathrm{[Bac]} \cdot \mathrm{[Glu]} \cdot \mathrm{ksyn\_native\_svp} $$ $$\mathrm{d}\mathrm{[cGlu]}/\mathrm{d}t = - \mathrm{kdim\_native\_svp} \cdot {\mathrm{[cGlu]}}^2 - \mathrm{kdegg\_native\_svp} \cdot \mathrm{[cGlu]} + \mathrm{[Bac]} \cdot \mathrm{[Glu]} \cdot \mathrm{ksyn\_native\_svp} $$ $$\mathrm{d}\mathrm{[Ind]}/\mathrm{d}t = {\mathrm{[cGlu]}}^2 \cdot \mathrm{kdim\_native\_svp} - \mathrm{[Ind]} \cdot \mathrm{kdegi\_native\_svp} $$

Results 1

Results 1.

Discussion

Discussion.

References.

Thanks to

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