Difference between revisions of "Team:Valencia UPV/Results"

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<h1>Results</h1>
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<p>Here you can describe the results of your project and your future plans. </p>
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<h3>What should this page contain?</h3>
 
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<ul>
 
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<li> Clearly and objectively describe the results of your work.</li>
 
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<li> Future plans for the project. </li>
 
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<li> Considerations for replicating the experiments. </li>
 
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</ul>
 
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<h3>Describe what your results mean </h3>
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<ul>
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<li> Interpretation of the results obtained during your project. Don't just show a plot/figure/graph/other, tell us what you think the data means. This is an important part of your project that the judges will look for. </li>
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<li> Show data, but remember all measurement and characterization data must be on part pages in the Registry. </li>
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<li> Consider including an analysis summary section to discuss what your results mean. Judges like to read what you think your data means, beyond all the data you have acquired during your project. </li>
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<div class="main-container">
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            <section style="/* background-color: #353535; *//* background-image: url(&quot;https://previews.123rf.com/images/bogdanhoda/bogdanhoda1504/bogdanhoda150400014/38730978-test-tubes-in-clinic-pharmacy-and-medical-research-laboratory-with-male-scientist-using-pipette.jpg&quot;); *//* background-size: 100%; */height:44.2em;margin-top: 0em;padding-top: 1.1em;" class="height-90 parallax">
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      <div class="row">
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        <div class="col-md-3" style="
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              <h4 style="
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                  padding-left: 1em;
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                  ">Index</h4>
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              <ul class="lateral">
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                  <li class="lateral">
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                    <div class="tab__title" style="
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                        ">
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                        <a href="#models" class="lateral inner-link" style="
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                          ">Models & Experiments</a>
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                          <!--<ul style="list-style-type: circle;">
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                          <li><p>
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                          <a href="#design_process" class="lateral inner-link" style="
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                          ">Modeling process</a>
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                          </p></li>
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                          <li><p>
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                          <a href="#const_models" class="lateral inner-link" style="
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                          ">Constitutive expression models</a>
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                          </p></li>
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                          <li><p>
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                          <a href="#ind_models" class="lateral inner-link" style="
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                          ">Inducible expression models</a>                 
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                          </p></li>
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                          <li><p>
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                          <a href="#optimization" class="lateral inner-link" style="
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                          ">Multi-objective parameter optimization</a>
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                          </p></li>
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                          </ul>-->
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                        <a href="#simtool" class="lateral inner-link" style="
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                          ">Simulation Tool</a>
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                    </div>
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                  </li>
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                  <li class="lateral">
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                        ">
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                        <a href="#references" class="lateral inner-link" style="
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                          ">References</a>
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                    </div>
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                  </li>
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              <a class="anchorOffset" id="lmModeling"></a>
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              <h2 class="h2Modeling">Modeling</h2>
 +
              <p>
 +
              <b>Do you think it is possible to mathematically describe a cell? Would you like to know the possibilities that modeling offers you?</b>
 +
              </p>
 +
              <p>
 +
One of the fundamental bases of the Printeria project has undoubtedly been <b>mathematical modeling</b>. Thanks to the development and application of new mathematical models, it is possible to <b>quantify the expression of proteins</b> in cells, and therefore <b>characterize</b> through different experiments the parts designed by Printeria. From the Printeria Modeling team, we intend to reach different goals:
 +
</p>
 +
<ul>
 +
<li><p>
 +
<b>Design simple mathematical models</b> based on differential equations that describe the biochemical processes of a cell. With them, we can simulate the different genetic circuits that Printeria allows us to build.
 +
</p></li>
 +
<li><p>
 +
Develop a <b>Simulation Tool</b> that allows the user to visualize a prediction of the results of their experiment before running it in Printeria.
 +
</p></li>
 +
<li><p>
 +
<b>Optimize model parameters</b> to match simulation results to experimental data obtained from Printeria constructions.
 +
</p></li>
 +
<li><p>
 +
<b><a href="https://2018.igem.org/Team:Valencia_UPV/Experiments#imCharact" target="_blank">Characterize the parts</a> of our <a href="https://2018.igem.org/Team:Valencia_UPV/Part_Collection" target="_blank">Part Collection</a></b> from the optimization results and provide the user with all the information about the Printeria kit.
 +
</p></li>
 +
</ul>
 +
<p>
 +
Although in the development of the project we have dealt with all these aspects, all of them have a single purpose: demonstrate the importance and many applications of <b>describing in a mathematical way the biological processes</b> that take place inside the cell.
 +
                </p>
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                    <a class="inner-link" href="#models"><img class="fotosModeling" src="https://static.igem.org/mediawiki/2018/c/ca/T--Valencia_UPV--instagramUPV2018.png"></a>
 +
                    <p style="text-align: center !important; font-weight: bold;">Models &amp; Experiments</p>
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                  </div>
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                  <div class="col-md-3" style="text-align: center;margin-right: 2em;">
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                    <a class="inner-link" href="#simtool"><img class="fotosModeling" src="https://static.igem.org/mediawiki/2018/c/ca/T--Valencia_UPV--instagramUPV2018.png"></a>
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                    <p style="text-align: center !important; font-weight: bold;">Simulation Tool</p>
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<h3> Project Achievements </h3>
 
  
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<p>You can also include a list of bullet points (and links) of the successes and failures you have had over your summer. It is a quick reference page for the judges to see what you achieved during your summer.</p>
 
  
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<ul>
 
−
<li>A list of linked bullet points of the successful results during your project</li>
 
−
<li>A list of linked bullet points of the unsuccessful results during your project. This is about being scientifically honest. If you worked on an area for a long time with no success, tell us so we know where you put your effort.</li>
 
−
</ul>
 
  
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<h3>Inspiration</h3>
 
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<p>See how other teams presented their results.</p>
 
−
<ul>
 
−
<li><a href="https://2014.igem.org/Team:TU_Darmstadt/Results/Pathway">2014 TU Darmstadt </a></li>
 
−
<li><a href="https://2014.igem.org/Team:Imperial/Results">2014 Imperial </a></li>
 
−
<li><a href="https://2014.igem.org/Team:Paris_Bettencourt/Results">2014 Paris Bettencourt </a></li>
 
−
</ul>
 
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                  <a class="anchorOffset" id="references"></a>
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                  <h3>References</h3>
 +
                  <ol class="references">
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                  <li><p><a class="anchorOffset" id="references1"></a>
 +
                  Picó, J., Vignoni, A., Picó-Marco, E., & Boada, Y. (2015). <i>Modelado de sistemas bioquímicos: De la ley de acción de masas a la aproximación lineal del ruido.</i> Revista Iberoamericana de Automática e Informática Industrial RIAI, 12(3), 241-252.
 +
                  </p></li>
 +
                  <li><p><a class="anchorOffset" id="references2"></a>
 +
                              Y. Boada, A. Vignoni, and J. Picó. Engineered control of genetic variability reveals interplay among quorum sensing, feedback regulation, and biochemical noise. ACS Synthetic Biology, 6(10):1903–1912, 2017a.
 +
                  </p></li>
 +
                  <li><p><a class="anchorOffset" id="references3"></a>
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                  Segel, L. A., & Slemrod, M. (1989). <i>The quasi-steady-state assumption: a case study in perturbation.</i> SIAM review, 31(3), 446-477.
 +
                  </p></li>
 +
                  <li><p><a class="anchorOffset" id="references4"></a>
 +
                  Boada, Y., Vignoni, A., Reynoso-Meza, G., & Picó, J. (2016).<i> Parameter identification in synthetic biological circuits using multi-objective optimization</i>. Ifac-Papersonline, 49(26), 77-82.
 +
                  </p></li>
 +
                  <li><p><a class="anchorOffset" id="references5"></a>
 +
                              R. Milo and R. Phillips. Cell Biology by the Numbers. First edition, 2015. ISBN9780815345374.
 +
                  </p></li>
 +
                  <li><p>
 +
                  Boada, Y., Vignoni, A., & Picó, J. (2017). <i>Reduction of population variability in protein expression: A control engineering approach.</i> Actas de las XXXVIII Jornadas de Automática.
 +
                  </p></li>
 +
                  <li><p><a class="anchorOffset" id="references7"></a>
 +
                        U. Alon. An Introduction to Systems Biology. Desing Principles of Biological Circuits. Champan and Hall/CRC, Edition, 2007.
 +
                  </p></li>
 +
                  <li><p><a class="anchorOffset" id="references8"></a>
 +
                  Schleif, R. (2000). <i>Regulation of the L-arabinose operon of Escherichia coli.</i> Trends in Genetics, 16(12), 559-565.
 +
                  </p></li>
 +
                  <li><p><a class="anchorOffset" id="references9"></a>
 +
                  Boada, Y. (2018). <i>A systems engineering approach to model, tune and test synthetic gene circuits.</i> PhD. Thesis, Universitat Politècnica de València.
 +
                  </p></li>
 +
                  <li><p><a class="anchorOffset" id="references10"></a>
 +
                              N. E. Buchler, U. Gerland, and T. Hwa. Nonlinear protein degradation and the function of genetic circuits. Proceedings of the National Academy of Sciences of the United States of America, 102(27):9559–9564, 2005.
 +
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Revision as of 21:17, 14 October 2018

Stack Multipurpose HTML Template

Index

Modeling

Do you think it is possible to mathematically describe a cell? Would you like to know the possibilities that modeling offers you?

One of the fundamental bases of the Printeria project has undoubtedly been mathematical modeling. Thanks to the development and application of new mathematical models, it is possible to quantify the expression of proteins in cells, and therefore characterize through different experiments the parts designed by Printeria. From the Printeria Modeling team, we intend to reach different goals:

  • Design simple mathematical models based on differential equations that describe the biochemical processes of a cell. With them, we can simulate the different genetic circuits that Printeria allows us to build.

  • Develop a Simulation Tool that allows the user to visualize a prediction of the results of their experiment before running it in Printeria.

  • Optimize model parameters to match simulation results to experimental data obtained from Printeria constructions.

  • Characterize the parts of our Part Collection from the optimization results and provide the user with all the information about the Printeria kit.

Although in the development of the project we have dealt with all these aspects, all of them have a single purpose: demonstrate the importance and many applications of describing in a mathematical way the biological processes that take place inside the cell.

Models & Experiments

Simulation Tool

References

  1. Picó, J., Vignoni, A., Picó-Marco, E., & Boada, Y. (2015). Modelado de sistemas bioquímicos: De la ley de acción de masas a la aproximación lineal del ruido. Revista Iberoamericana de Automática e Informática Industrial RIAI, 12(3), 241-252.

  2. Y. Boada, A. Vignoni, and J. Picó. Engineered control of genetic variability reveals interplay among quorum sensing, feedback regulation, and biochemical noise. ACS Synthetic Biology, 6(10):1903–1912, 2017a.

  3. Segel, L. A., & Slemrod, M. (1989). The quasi-steady-state assumption: a case study in perturbation. SIAM review, 31(3), 446-477.

  4. Boada, Y., Vignoni, A., Reynoso-Meza, G., & Picó, J. (2016). Parameter identification in synthetic biological circuits using multi-objective optimization. Ifac-Papersonline, 49(26), 77-82.

  5. R. Milo and R. Phillips. Cell Biology by the Numbers. First edition, 2015. ISBN9780815345374.

  6. Boada, Y., Vignoni, A., & Picó, J. (2017). Reduction of population variability in protein expression: A control engineering approach. Actas de las XXXVIII Jornadas de Automática.

  7. U. Alon. An Introduction to Systems Biology. Desing Principles of Biological Circuits. Champan and Hall/CRC, Edition, 2007.

  8. Schleif, R. (2000). Regulation of the L-arabinose operon of Escherichia coli. Trends in Genetics, 16(12), 559-565.

  9. Boada, Y. (2018). A systems engineering approach to model, tune and test synthetic gene circuits. PhD. Thesis, Universitat Politècnica de València.

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