Comparing Potential Energy

A potential energy measurement can be described as a summary statistic for the relative position and orientation of each particle in a simulation. As particle positions and orientations are updated in each simulation step the potential energy of the simulation will change. In an nBLOCK sim- ulation potential energy is a function of the FENE spring potential between nucleotide backbone sites and nucleotides bound to nanoparticles, hydrogen bonds between base pairs, the intra-strand stacking potential, the cross stacking potential, the Lennard Jones excluded volume potential, and a modulating potential that encourages helicity of dsDNA [11, 13]. Each of these potentials, for all particles in the simulation, contributes to the total PE of the system.

Our methods of comparison were borrowed from the testing utilities of oxDNA. We also mim- icked the strand topologies from these tests. The original strand topologies used to compare the

(a) (b)

Figure 4.3: Maxwell probability plots. Figure Explanation. The Y-axis is scaled to match the distribution of the gathered KE measurements. The X-axis shows the values corresponding to quantiles of the Maxwell distribution. The blue dots in (a) and (b) represent one thousand KE samples taken at an interval of one thousand time steps from the simulation of a two-hundred particle diatomic nBLOCK assembly. The straight red line is plotted through the center of mass for this points. Because there are many overlapping points in these plots we further quantify the result using the square root of the coefficient of determination r which is shown in each figure. An r value of 1.0 would indicate that the variance in the KE samples is completely predictable by the Maxwell distribution. As such the reported r values demonstrate the KE distribution of our nBLOCK simulations are a good fit the Maxwell distribution.

mean potential energy of DNA simulations include a fifteen base pair ssDNA composed of pure Adenine (Poly-A-15) and a fully hybridized hairpin composed of eighteen base pairs. To closely approximate these tests in the nBLOCK model we generated a Poly-A nBLOCK with fifteen nu- cleotides and a fully hybridized diatomic nBLOCK assembly. These tests allowed for the com- parison of PE between separately seeded simulations by removing the contribution of hydrogen bonding to the potential energy of the system. As might be expected the distribution of PE values for the hairpin loop has a smaller scale factor than what is sampled from the Poly-A ssDNA. This is because the range of motion for the nucleotides within the hybridized hairpin is restricted. The Poly-A topology has a wider range of conformations it can sample from but cannot get stuck in any intermediary states due to hydrogen bonding between Watson-Crick complements.

To gather statistics for PE validation we ran a series simulations using identical initial configu- rations of nBLOCK assemblies on both the host and device. These simulations were parameterized

with a temperature T = 293K, a step size of δt = 0.005, a Verlet-skin of length 0.5, 103 Newtonian steps, the diffusion coefficient set to 2.5, and a [NA+]-concentration of 0.5. During these simula- tions potential energy values were logged at fixed intervals. For each simulation we calculated the mean of the potential energy values reported by the host and by the device. A confidence interval ±5√σ

n around the mean potential energy of the host simulation was then constructed. Our valida- tion tests then checked whether or not the mean potential energy of the GPU simulation fell within this interval.

While we have translated the Poly-A and hairpin topologies of the original tests to similar topologies for the nBLOCK computational model there are two important differences in the pa- rameterization of these tests that should be considered. First, for nBLOCK simulations on the host and device we used a Verlet skin with a value of 0.5. This is in contrast to the Verlet skin of the original DNA simulations which used a value of 0.05. We used this increased Verlet skin size to compensate for the large diameter of the nanoparticles. Because we used a larger Verlet skin for our nBLOCK simulations the area that was included in each nucleotide’s non-bonded neighbor list was greater than the area around each nucleotide in the original tests. Due to this larger volume the potential energy for an nBLOCK simulation would have had on average a greater number of pairwise particle interactions. Second, the original tests were designed to test the PE between sep- arate simulations on the CPU, whereas our tests were used to compare the results between a CPU nBLOCK simulation and a GPU nBLOCK simulation.

Additionally, while we can use probability plots to compare the distribution of logged KE values against the Maxwell distribution there are in general no known standard statistical methods for the comparison of PE distributions [18]. That is, while we can collect samples of the PE distribution from our simulations the population distribution of PE is unknown.

The differences in parameterization between the original tests and our own along with having no distribution to make strict statistical comparisons against prevent us from determining whether our results for PE validation are statistically significant. Nonetheless, we can state that the mean PE of the device side implementation is within the designated confidence interval in eleven of the

twelve test cases presented in Figure 4.4, which provides strong evidence that our GPU implemen- tation is accurately matching the CPU implementation. Additionally, the results for the nBLOCK duplex assembly also demonstrate that our nBLOCK GPU implementation methodologies allow hybridization to occur and to be maintained as expected.