Difference between revisions of "Template:Team:Groningen/CONTENT/ARTICLES/Modeling poly-gamma-glutamic acid as a cation exchange membrane"

Some bacteria naturally have the first property. In this case they usually form a biofilm, which is an aggregate of bacteria in the slime they produce. One example of such a bacteria is the wild type Bacillus subtilis. While this bacteria makes a biofilm, our experiments show that this biofilm is not robust enough to withstand being in water for an extended period. In addition for the time that the biofilm is still intact it does not function as an ion exchange membrane.

Now, if one wants to add or improve the desirable properties to the biofilm, it is required to understand how the biofilm works. After all if one does not, then it is hard (if not impossible) to make a decent hypothesis to base the experiments in the lab on. What makes understanding the biofilm hard is that the exact composition of the the biofilm is not known, since it depends on the Bacillus Subtilis strain, what genes are expressed, how the bacteria specialize and how heterogeneous the biofilm is.<a href="#reps" class="cite">1</a> In addition, some genes related to the production of the biofilm are not well understood. While some are known to be responsible for the production of extracellular polymers, the length of the polymers in the biofilm varies a lot. This varying length makes characterization of these polymers hard.

While studying the literature on Bacillus Subtilis biofilms we found that poly-γ-glutamic acid (PGA) might help us with both properties. It helps B. Subtilis stick together, because PGA helps with the underwater growth.<a href="#runw" class="cite">2</a> This happens probably by increasing the number of sites available for salt bridges. It may also help the aggregation of B. Subtilis, because PGA forms aggregates even in dilute solutions.<a href="#rdilsol" class="cite">3</a> Poly-γ-glutamic acid may also form an obstacle for some ions, because is has a high number of fixed charges (as can been seen in its structure as shown in Figure <a href="#pgachem">1</a>). With a model of PGA one could gain insight in how it aggregates and how it could improve ion conductivity. Naturally there are multiple ways to approach modeling, usually though one takes a top-down approach or a bottom-up approach.

When using the top-down approach, the models are usually based on the biomass, individuals (bacteria) or particles. These are useful to study the detachment, mass transfer and species distributions in biofilms, but fail when they are applied in bigger systems like whole reactors.<a href="#rbiomod" class="cite">4</a><a href="#rbiomod2" class="cite">5</a> Furthermore, many parameters required for such models are hard to gather experimentally. Since this project is concerned with a complete system like a reverse electrodialysis cell and the electrostatic interactions, which are not included, this type of model would not be a good fit. While there are models for the growth of the biofilm, there are also models about the ion permeability of membranes. These models give good results with materials that have known geometries, but are less reliable for materials with pore sizes that are small relative to the Debye length or if the materials in question are heterogeneous.

Our model is based on the the bottom-up approach. Since poly-γ-glutamic acid was identified as a molecule that might have a positive contribution to our modified biofilm, the properties of this polymer were modeled. With molecular dynamics one is able to model the interactions between molecules and atoms. Thus molecular dynamics is able to simulate the interactions between the PGA molecules and to simulate the interaction of PGA with ions. Unfortunately, these simulations take a lot of time and computational power. Luckily it is possible to reduce the time needed by using the MARTINI coarse grained force field due to Marrink et al.<a href="#rmartini" class="cite">6</a> While ion exchange membranes and proton exchange membranes have been studied using molecular dynamics before, this has never been done using MARTINI, and certainly not for poly-γ-glutamic acid.<a href="#rnafion" class="cite">7</a><a href="#rproton" class="cite">8</a>

An introduction to molecular dynamics can be found in the supporting material, as well as some additional information on the creation of the protocols in the methods. In the methods section (<a href="#smethods">link<a>) protocols are detailed for the parameterize poly-γ-glutamic acid, the aggregation molecules, the membrane formation and the reverse electrodialysis cell simulations. In the results section we will report our findings and discuss them, and we will also mention possible future work. In the appendix an overview is given of the specific tools and scripts we have written to create, modify or analyse topologies and trajectories.

The modeling of poly-γ-glutamic acid consisted of the carrying out the following steps in order: parameterization of PGA, aggregation of PGA, membrane formation and ion flow simulations with PGA, cellulose and cellulose phosphate.

Our main efforts in the parameterization of poly-γ-glutamic acid were focussed on the bonded parameters. Interactions are bonded if they are part of a bond, an angle or a dihedral. Note that this property is not transitive. The final parameters for bonded interactions of PGA in the coarse grained model can be seen in Table a href="#tbeads">TODO<a>, <a href="#tbonds">TODO<a> and <a href="#tangles">TODO<a>.

The corresponding mapping can be seen in Figure <polygammaglutamicacidbeads.png>. The bead types were chosen based on expected interaction strength. However changing the bead types of the backbone of the monomer did not have any effect on the end-to-end distance, bond and angle distributions(data not shown). In addition the shape of the bond distributions is similar but shifted, since the average bond lengths are a little bit higher in the coarse grained case (data not shown). The angle distributions are shown in Figure <TODO anglet, angleg>. The fit is not perfect, but it should be sufficient. Changing the force constants of the angles did not affect the distributions much, except if the constants were changed to a big value like 500 kJ/mol. However if the angle force constants are this big, then the distributions are too narrow. The distribution of the end-to-end distances, as can be seen in Figure <e2e(small).png>, are comparable with each other. The only differences are that the the peak around 0.9 nm in the coarse grained graph is more distinct than in the all atomic one. Furthermore, the distribution is narrower. The latter difference together with the angle distributions indicates that either the interactions between beads are too strong or the interactions of the backbone with the water is too weak. To fix this one would have to change the nonbonded interactions of the beads in PGA.

Next, if multiple molecules are simulated together, they aggregate (as shown in Movie <a href="#movaggr">1</a>. The water-holding capacity of the membrane made of poly-γ-glutamic acid was compared with values found in literature. As can be seen in Figure <a href="#fcross">TODO<a> the amount of water for PGA is approximately in agreement with the expected water-holding capacity of 56.9%. This property was not checked for cellulose and cellulose phosphate membranes. Comparing the other two cross sections with PGA one can see that the PGA membrane contains more water than one from cellulose, but less than one from cellulose phosphate.

Finally simulations were prepared with two membranes consisting of a single type of molecule. A typical example of this kind of system can be seen in Movie <boxrot.mkv>. The concentrations in the simulations are equal to the concentrations used in the lab. In Movies <pga.mkv>, <cell.mkv> and <cellp.mkv> the first 50 ns of the simulations are shown. With these simulations show what kind of influence the fixed charges have on the ion conductivity. While the effectiveness of PGA can be compared based on these videos, it is hard to make definitive statements about the flow rate of ions. Therefore we also calculated the net number of ions moving from salt water to fresh water and vice versa and then normalized the resulting value over the total number of the respective ions. The result can be seen in Figure <naclwflowbc(large).png>. As is shown PGA and cellulose phosphate let only sodium ions through while blocking the chloride ions. The cellulose allows both sodium and chloride ions to pass through. Another noteworthy thing is that in the beginning the PGA lets through the sodium at the same rate as cellulose. It is unclear if this is so because of the different charge, pore size or width in comparison with cellulose phosphate. In the figure the water flow is also shown. The graphs show that the water moves from the fresh water to the salt water. The main reason for this effect is that the water has to fill the space left by the ions moving from salt to fresh water.