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Revision as of 23:06, 27 October 2013

Modeling IMA System

First Reaction - Cobalt ions bind to albumin

The cobalt ion binds to albumin protein, originating the cobalt-albumin complex. If the albumin is derived from a healthy patient, it will bind more cobalt ions than a albumin derived from a patient with heart disease. It happens because patients with heart conditions will have a isquemic modified albumin (IMA), which has less capability of binding to cobalt. Thus, the amount of free cobalt in the system will be determined by the presence of a normal or ischemic albumin in the sample.

Second Reaction - The entry of cobalt in the cell

There are four cobalt transporters in the cell. The rcnA (Uniprot code: P76425) and zntA (Uniprot code: P37617) transport the cobalt to outside the cell. The corA (Uniprot code: P0ABI4) and zupT (Uniprot code: P0A8H3) transport the cobalt to inside the cell.

The equation for the cobalt flux through these transporters is:

Third Reaction - Transcription

In our modeling, we considered the cobalt acting as a transcriptional activator in order to simplify our model.

The transcriptional activation are accessed by the equation:

The transcription and translation are accesed by the equations:

Model simulations

Figure - Modeling simulation of IMA system

The first simulation of our model showed that the outside concentration of cobalt reaches an equilibrium with the inside cobalt concentration of the bacterial cell. After a while, the Yellow Fluorescent Protein starts to appear. After that, we wanted to know how the system would behave with different concentrations of cobalt.

Figure - Modeling simulation of IMA system with different concentrations of cobalt and 90 uM of Albumin protein

Simulating this situation it seems that YFP origin velocity reaches a limit for the higher concentrations of cobalt, as we see that there is just a little difference between the curves of 100, 125 e 150 uM of cobalt concentration. Thus we tested whether our model would be capable of differentiating the situations of higher and lower concentrations of normal albumin, as exists in healthy and affected individuals, respectively, according their heart condition.

Figure - Modeling simulation of IMA system with different concentrations of Albumin

The model could differentiate between the different concentrations of albumin, as shown in the graph above.

However, the pattern of our experimental results were different from the pattern obtained in our modeling. So, further caracterization and evaluation of our model is needed in order to obtain a better representation of the reality of our system.

Model optimization

We have adapted an Evolutionary Algorithm to seek for the constants that best fit our model to the experimental results.

When we have functions that have an nonlinear behaviour, seeking for global optimal value, can be challenging and many types of functions and mathematical models have no known methods for this purpose. In these cases, it is fairly common to use stochastic methods, like Evolutionary Algorithms, to find interesting results within functions and models.

An [http://en.wikipedia.org/wiki/Evolutionary_algorithm Evolutionary Algorithm] is a method in which each solution represents an individual and by crossing and mutating them, we can reach solutions that better fit our purpose.

An schema, about how the Evolutionary Algorithm used our optimization works, is presented below:

Figure - Evolutionary Algorithm schema [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6557822&isnumber=6557545 Coelho, et al, 2013]

To fit the constants to the modeling, we compared the curve in our modeling to the curve obtained from experimental data and tried to reduce the difference between the curves.

Despite the fact that the algorithm were able to reduce the difference between the curves, it couldn't find the curve we needed, because the modeling of YFP behaviour is not representing the experimental data behaviour.

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