Results and analysis

Table of contents

1. Implementation overview
   1. Histograms
   2. Tuples
2. Analysis files
3. Note about python import in ROOT
4. References

Implementation overview

As the simulation runs, it keeps track of three main elements in relation to DNA damage.

• Energy depositions in each chromosome
• Energy depositions and track length in the cell
• DNA damage in the DNA geometry

At the end of each event, the DNA damage events are collected and analysed, reconstructing the damage pattern in the DNA and assigning it a complexity.

In the damage model, a probability is assigned that certain events cause strand breaks (i.e. \(Pr(\ce{e^{-}_{aq}} + \mathrm{Sugar} \rightarrow \mathrm{SSB})\)). These conditions are tested at the end of each event when the analysis runs to determine the damage that occurs.

At the completion of the run, the following outputs in a ROOT file are saved:

Histograms

• SSB counter (ssb_counts)
• Deposit energy in SSBs (ssb_energies_ev)
• DNA fragment size
• Strand interaction positions

Tuples

• Primary Source
  1. Primary
  2. Energy
  3. PosX in um
  4. PosY in um
  5. PosZ in um
  6. Momentum X
  7. Momentum Y
  8. Momentum Ζ
• Source (Break Source Frequency)
  1. Primary
  2. Energy
  3. None
  4. SSBd
  5. SSBi
  6. SSBm
  7. DSBd
  8. DSBi
  9. DSBm
  10. DSBh
• Damage (DNA damage locations)
  1. Event
  2. Primary
  3. Energy
  4. TypeClassification
  5. SourceClassification
  6. Position_x_um
  7. Position_y_um
  8. Position_z_um
  9. Size_nm
  10. FragmentLength
  11. BaseDamage
  12. StrandDamage
  13. DirectBreaks
  14. IndirectBreaks
  15. EaqBaseHits
  16. EaqStrandHits
  17. OHBaseHits
  18. OHStrandHits
  19. HBaseHits
  20. HStrandHits
  21. EnergyDeposited_eV
  22. InducedBreaks
  23. Chain
  24. Strand
  25. BasePair
  26. Name

Analysis files

In multithreading mode, ROOT data files (molecular-dna_t*.root) associated with the threads are created. ROOT6.x should be installed to analyse these ROOT data files.

Several ROOT macro files are provided to join the ROOT data files into an unique ROOT data file (molecular-dna.root) and analyse the results:

• cylinders.C : to plot damage from cylinders geometry
• plasmid.C : to plot damage from plasmid geometry
• ecoli.C : to plot damage from E.coli geometry
• human_cell.C, human_cell_alphas.C and human_cell_chromosomes.C: to plot damage and fragments distribution from human cell geometries (as in [3] for human_cell_alphas.C, as in [4] for human_cell_chromosomes.C)

root cylinders.C

These macros calculate mean quantities and the associated standard error of the mean (SEM) [5].

User can also join the ROOT files (molecular-dna_t*.root) using the following command :

hadd -O -f molecular-dna.root molecular-dna_t*.root

A python macro file is provided to modify ROOT output in SDD [2] file format:

• createSDD.py : to use it, insert the command “python3 createSDD.py”. If error with ROOT, simply source /path/to/root/bin/thisroot.(c)sh, do “pip install pyroot” and try again.

Note about python import in ROOT

If python cannot import ROOT, please configure your ROOT version to include PyROOT.

For further instruction refer to the documentation of ROOT, paragraph 19.1.4.2, see link.

References

[1] Computational modelling of lowenergy electron-induced DNA damage by early physical and chemical events, H. Nikjoo et al., Int. J. Radiat. Biol. 71 (1997) 467–483 - link

[2] A new standard DNA damage (SDD) data format, J. Schuemann et al., Rad. Res. 191 (2019) 76-92 - link

[3] Geant4-DNA simulation of human cancer cells irradiation with helium ion beams, K. Chatzipapas et al., Phys. Med. 112 (2023) 102613 - link

[4] Development of a novel computational technique to create DNA and cell geometrical models for Geant4-DNA, K. Chatzipapas et al., Phys. Med. 127 (2024) 104389 - link

[5] What to use to express the variability of data: Standard deviation or standard error of mean?, Mohini P. Barde, Prajakt J. Barde, Perspectives in Clinical Research 3(3) (2012) 113-116 - link