Difference between revisions of "Team:RUM-UPRM/MathModels"
Claudiamanan (Talk | contribs) |
Claudiamanan (Talk | contribs) |
||
| Line 51: | Line 51: | ||
<h3 class="mx-auto my-0 text-center">Translation Rate</h3> | <h3 class="mx-auto my-0 text-center">Translation Rate</h3> | ||
<p>Using the assumptions the | <p>Using the assumptions the | ||
| − | <a href="https://2016.igem.org/Team:Sydney_Australia/ InternalCellular">2016 Sydney Team</a> (https://2016.igem.org/Team:Sydney_Australia/ InternalCellular) found in a literary search, we used the translation rate of 17.1 amino acids/seconds. This rate uses the assumption that the factor affecting it is the speed of the ribosome. The translation factor is also affected by the amount of available mRNA transcripts for the ribosome to bind to. The values used as Translation Factors are:</p> | + | <a href="https://2016.igem.org/Team:Sydney_Australia/InternalCellular">2016 Sydney Team</a> (https://2016.igem.org/Team:Sydney_Australia/ InternalCellular) found in a literary search, we used the translation rate of 17.1 amino acids/seconds. This rate uses the assumption that the factor affecting it is the speed of the ribosome. The translation factor is also affected by the amount of available mRNA transcripts for the ribosome to bind to. The values used as Translation Factors are:</p> |
<center><img src="https://static.igem.org/mediawiki/2020/9/98/T--RUM-UPRM--TABLA1INGE.png" style="width: 80%"> </center> | <center><img src="https://static.igem.org/mediawiki/2020/9/98/T--RUM-UPRM--TABLA1INGE.png" style="width: 80%"> </center> | ||
Revision as of 01:01, 23 October 2020
Mathematical Modelling
Introduction
Mathematical Modelling was incorporated into Phase 1 of our project as a way to better describe our system. It is significantly important since it is an efficient way to optimize our system and devices for Phase 2 which will be focused on experimentally testing and characterizing our prototypes and parts, respectively. To build the mathematical models based on Ordinary Differential Equations (ODEs), SimBiology, a MATLAB feature, was used. This MATLAB feature provides tools to model, simulate and analyze biological systems and is highly beneficial for providing accurate quantitative results of our system. Mathematical models were built for both genetic circuits, Mercury and RDX, in order to simulate the expected behavior of each device.
Cell Signaling Kinetics
To model our system and the reactions that take place within our devices, we have used the Law of Mass Action kinetics to determine how the concentrations of the species change with respect to time. For the first part of our model in Phase 1, we have assumed that reactions are irreversible. For Phase 2, after sufficient experimental data is obtained, reactions will be assumed to be reversible due to the fact that most biological reactions are deemed reversible and enough data will be collected to be able to build these reversible models.
