1. Computer modelling using cellular automata of the survival fraction of cell populations under irradiation Morgiane Richard
Examples of glioma cells T98g and U373 and hamster fibroblast cells V79
Krakow

2. Table of contents People and places involved.
Organisation and justification of the project.
Biological background of the project.
Relevance of the chosen model, cellular automata.

3. Actors of the project Miss Morgiane Richard Supervisors:
Dr. N.F. Kirkby, University of Surrey
Prof. R.P. Webb, University of Surrey
Dr. K.J. Kirkby, University of Surrey

4. Location of the project
University of Surrey, Guildford, Great Britain
Guildford

5. How will the project unfold
Computer modelling of cell behaviour under irradiation
Experimental validation of models at the Gray Cancer Institute, Cambridge or at the University of Surrey, Ion Beam Center.

6. A big issue behind
Radiotherapy has been widely used for curing cancer.
Aim of radiation: to kill cancer cells, and destroy the tumour, without affecting healthy tissues.
Results in radiotherapy can still be optimised.

7. Effect of radiation on cells
Biological processes following cells irradiation:
DNA damage:
Double strand breaks
Single strand breaks
Irradiation: X-ray, γ
Cell
Repair processes:
Base Excision Repair
Homologous Recombination
Non Homologous End Joining

8. Effect of radiation on cells
Two effects have been discovered
Low-Dose Hyper Radiosensitivity (LDHRS), coupled with Increased Radioresistance (IRR).
The bystander effect.

9. Effect of radiation on cells
Illustration of hyper radiosensitivity

10. Effect of radiation on cells
LDHRS/IRR not fully understood yet
Irradiation effects:
Depend on intrinsic characteristic of cells.
Trigger intra-cellular signals.

11. Effect of radiation on cells
Bystander effects: non-irradiated cells are affected.
Irradiation effects:
Involve non-irradiated cells.
Spread through inter-cellular signals
Petri dish

12. Cellular automata (CA)
Definition:
Network of cells at an initial state,
Finite set of rules,
A neighbourhood: a concentration field (signals…).
NB: a CA cell is NOT always a biological cell!

13. Cellular automata Time evolution: State(t+1) depends on state(t).
Changes according to the rules and the neighbourhood.

14. Cellular automata Example of 1D CA:
Infinite line of cells, all of them at 0 and one at 1.
Neighbourhood: two neighbour cells.
Rule: ct+1(i)= ct(i+1)+ct(i-1) mod 2

15. Cellular automata 1 Example of 1D CA: Time evolution of the CA: T = 01
T = 0
T = 1
T = 2
T = 3

16. Cellular automata for biological cells
11 states: G0, G1, S, G2, M +/- DNA damage and D?
Simple transitions: S->M and M->2G1
Check points G1->S and G2->M
Effect of radiation in each state?
Transmission and reception of signals?

17. Cellular automata Conclusion: possibility of modelling
Intrinsic effects of radiation
Bystander effects of radiation

18. Questions?

19. Simple cell model

20. Survival curve of a population

21. Population model against experimental data
Exp. data Short et al, GCI