Team:Valencia UPV/Results

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Index

Models & Experiments
Simulation Tool
References

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

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.
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.
Segel, L. A., & Slemrod, M. (1989). The quasi-steady-state assumption: a case study in perturbation. SIAM review, 31(3), 446-477.
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.
R. Milo and R. Phillips. Cell Biology by the Numbers. First edition, 2015. ISBN9780815345374.
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.
U. Alon. An Introduction to Systems Biology. Desing Principles of Biological Circuits. Champan and Hall/CRC, Edition, 2007.
Schleif, R. (2000). Regulation of the L-arabinose operon of Escherichia coli. Trends in Genetics, 16(12), 559-565.
Boada, Y. (2018). A systems engineering approach to model, tune and test synthetic gene circuits. PhD. Thesis, Universitat Politècnica de València.
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.

[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.

[10] 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.