Team:CIDEB-UANL Mexico/math overview

When Biology meets Math

 

For iGEM projects, the use of mathematical models is necessary to predict the behavior of a biological machine, representing the quantitative relations between two or more variables involved in the function and expression of a gene or a set of genes in organisms like E. coli.

Our team decided to use a deterministic model to simulate and represent the function of the four modules, assuming that the variables (mRNA and protein concentrations) adopt a continuous behavior and obey kinetic rules that can be represented with constant values.

This type of mathematical model is used to include variables that considerate both, the gene expression and the physiological cycles (chemical process, transport of proteins, etc.). By the use of traditional differential equations we were able to construct the description and analyze the behavior of mRNAs and protein production. We were focused in determine through a mathematical model the ideal behavior of the four modules independently.

Capture

Aroma

Resistance

Union

Deterministic Modelling: Equations and Parameters

As it was previously stated, it was necessary to work with different equations, focused in the production and degradation rate of proteins, according to the length of the genes in each module. This was performed in order to obtain both, the concentration rates of mRNA and protein based on system (gene) length and protein length respectively.

Equations

● mRNA

Generally, to describe the amount of mRNA produced over t time, the equation implemented is shown below:

\begin{equation} \large \frac{d[mRNA]}{dt} = \alpha_{1} \cdot f_{y} - d_{1}[mRNA] \end{equation}

Where: “α₁” means the transcription rate of a given gene; “f_y” represents a regulatory function (if there is) that can activate or inhibit the system; and “d₁ [mRNA]” the degradation rate of the mRNA produced.

● Protein

The same happens with the protein production, but differs in the formula because it also involves a post-translational variable:

\begin{equation} \large \frac{d[P]}{dt} = \alpha_{2} \cdot[mRNA] - d_{2}[P] - f_{post} \end{equation}

Where: “α₂ [mRNA]” means the translation rate of a protein based on the amount of mRNA available; “d₂”[P] represents the degradation rate of that protein; and “f_post" the posttranslational variables which affect the production of the final (functional) protein.

Parameters

To determine both the transcription (1) and translation (2) rates, the parameters from Singapore 2008 iGEM team, at wich E. coli carries out transcription and translation were used ; as well as the degradation rates from Beijing PKU 2009. The parameters from Singapore 2008 iGEM were used, assuming a transcription speed of 70nt/s and a translation speed of 40aa/s. The speeds were multiplied by 60, because minutes were used as units in the simulations of the modules. We assumed that E. coli division time was 30min based on PKU Beijing 2009 iGEM team.

\begin{equation} \large \alpha_{1} = \frac{transcription speed}{gene length (nt)} \end{equation}
\begin{equation} \large \alpha_{2} = \frac{translation speed}{protein length (aa)} \end{equation}
\begin{equation} \large \alpha_{1} = \frac{70 \frac {nt}{s} \cdot(60)}{gene length (nt)} \end{equation}
\begin{equation} \large \alpha_{2} = \frac{40 \frac {aa}{s} \cdot(60)}{protein length (aa)} \end{equation}
\begin{equation} \large d_{1} = \frac{1}{half-life(min)} + \frac{1}{division time (min)} \end{equation}
\begin{equation} \large d_{2} = \frac{1}{half-life(min)} + \frac{1}{division time (min)} \end{equation}
\begin{equation} \large d_{1} = \frac{1}{half-life(min)} + \frac{1}{30min} \end{equation}
\begin{equation} \large d_{2} = \frac{1}{half-life(min)} + \frac{1}{30min} \end{equation}

Bibliography/References

● David Dibden, J. G. (2005). In vivo cycling of the Escherichia coli transcription factor FNR between active and inactive states. Microbiology, 4063-4070.

● Douglas Selinger, R. M. (2003). Global RNA Half-Life Analysis in Escherichia coli Reveals Positional Patterns of Transcript Degradation. Genome Research, 216-223.

● iGEM CIDEB UANL. (2012). Modelling: Equations.Retrieved from: https://2012hs.igem.org/Team:CIDEB-UANL_Mexico/Math/Overview

● James Hare, K. T. (1991). Mechanisms of plasma membrane protein degradation: Recycling proteins are degraded more rapidly than those confined to the cell surface. PNAS, 5902-5906.

● Martina Bergant, N. M. (2010). Modification of Human Papillomavirus Minor Capsid Protein L2 by Sumoylation Journal of Virology, 11585-11589.

● NTU Singapore. (2008). Modelling: Parameters. Retrieved from: https://2008.igem.org/Team:NTU-Singapore/Modelling/Parameter

● PKU Beijing. (2009).Modelling: Parameters. Retrieved from: https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters

● Vorackova Irena, S. S. (2011). Purification of proteins containing zinc finger domains using Immobilized Metal Ion Affinity Chromatography. Protein Expression and Purification, 88-95.

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