1. CELLULAR AUTOMATA RULES GENERATOR FOR MICROBIAL COMMUNITIES CALIFORNIA STATE UNIVERSITY, SAN BERNARDINO SCHOOL OF COMPUTER SCIENCE & ENGINEERING By Melissa Quintana

2. Microbial Community April 1999 – Removal of Microbial Life September 2003 – Regrowth

3. Current Research Dr. Penelope Boston Explorations of extreme environments Microbiologist – Studies Microbial Communities Courtesy of Dr. Penelope Boston

4. Cellular Automata11011 001 0 = death 1 = life Total sum = 5 Rule : if total sum is 5 or less the cell state lives.110111 001

5. Cellular Automata Dr. Schubert Samples Cellular Automata Rules Radius of three Series of 20 to represent growth over a period of time

6. Goal – Extract Radius Use image analysis to produce a visual representation of cellular automata specifications. for i=1:prod(size(a1)) if (a1(i)==0 & b1(i)==1) then Live(a2(i)+1)=Live(a2(i)+1)+1 elseif (a1(i) - b1(i)>0) then Die(a2(i)+1)=Die(a2(i)+1)+1 elseif (a1(i)==1 & b1(i)==1) then StableTwo(a2(i)+1)=StableTwo(a2(i) +1)+1; end

7. What is the radius of effect? The radius of effect of Cellular Automata Why is it important?

8. Goal-Estimate the Rules Estimated Rules Program Estimated Rules

9. What are Rules? Game of Life 1 represents a neighbor 0 represents no life Any live cell with fewer than two live neighbors dies, as if caused by under-population. <2 = Death 100 010 000 100 000 000 Any live cell with more than three live neighbors dies, as if by overcrowding. >3 = Death 111 011 000 111 001 000 Any live cell with two or three live neighbors lives on to the next generation. 2 or 3 = life 100 110 000 100 110 000 Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction. Exactly 3 = Life 000 000 111 000 010 111

10. Importance of the Study Discover the rules without knowing the rules. Correlate the rules with patterns. Overall understanding of what and how much of the environmental factors contribute to the results of the growth.

11. Visual Identification 1-3435-50 Life Death Water Soil Biomass Weather Randomness Over-crowding Correlate the rules with the patterns with an understanding of the surrounding environmental factors. AirSediments (animals, plants)Hot and Cold Temperatures

12. Thesis Project Three Phases – Phase One Testing Calculations Identifying the Radius of effect – Phase Two Identifying an approximation of the Rules – Phase Three Identifying an approximation of the Rules from pictures Samples – Cellular Automata – Pictures SciLab

13. First Phase – Predefined Matrix Predefined Matrix A = [110100111;100000100;111001001; 110110000;110100110;001011001; 100010011;111100010;000100000]; 110100111 100000100 111001001 110110000 110100110 001011001 100010011 111100010 000100000

14. Calculate and Store 1101000 1000010 0011001 100110 0100010 1001001 0110101 = 20 1 1234567891011121314151617181920 0000000 1000010 0011001 100110 0100010 1001001 0110101 = 17 11 0000111 1000010 0011001 100110 0100010 1001001 0110101 = 20 12 0000111 1000010 0011001 100100 0000000 0000000 0000000 = 10 112 0000111 1000010 0011001 100100 0000000 0000011 1111111 = 17 122 0000000 0000000 0110001 100100 0000000 0000000 0000000 = 5 1122 1111111 0000000 0110001 100100 0000010 0000000 1111111 = 20 1123

15. Calculation Output Manual Verification 7. 12. 24. 22. 10. 4. 1. 0. 1. 0. 2. 4. 10. 8. 15. 5. 10. 12. 6. 4. 2. 1. 0.

16. Program Function Cellular Automaton Was used that had specific rules assigned to it. Series of 20 to represent growth and time. Function First program was turned into a function. The function was called on every time series to produce Histogram Analysis.

17. Radius 1 Output Radius of effect = 1 Calculation area

18. Radius 2 Output Radius of effect = 2 Calculation area

19. Radius 3 Output Radius of effect = 3 Calculation area

20. Second Phase Created to compare against existing estimates from Cellular Automaton of a static image. Live Center Dead Center 14 - 34 35 - 45

21. Cellular Automata

22. Calculate and Store (1 ST Series) 1234567…. 20 1111111 0000000 0110001 1001100 0000010 0000000 1111111 = 20 20 123456789101112........ Center Cell state – Live (1) or dead (0) 1 123456789101112........ 1111111 1100100 0110001 1000100 0010011 1000101 1111111 = 28 2028 10 1111111 1111100 0110001 1011100 0010011 1010101 1111111 = 32 202832 101

23. For all Generations (2 nd Series) 1234567…. 20 1111111 0000000 0110001 1001100 0000010 0000000 1111111 = 20 20 123456789101112........ Center Cell state – Live (1) or dead (0) 1 123456789101112........ 1111111 1100100 0110001 1000100 0010011 1000101 1111111 = 28 2028 10 1111111 1111100 0110001 1011100 0010011 1010101 1111111 = 32 202832 101

24. Comparison of Selected Generations 1234567…. 20 t = 20If t == ? then 1234567…. 20 274675…. Vector with radius summed values Series 5 Matrix calculation ResultsSeries 6 Matrix calculation Results Vector with radius summed values 20283251149…. Vector with radius cell states 011010….110010 0-1 1-1 1-0 0-0 A dead cell becomes alive A live cell remains alive A live cell becomes dead A dead cell Remains dead 1234567.1234567.1234567.1234567. 0-1 1-1 1-0 0-0

25. Program Output Live State 0-1 Dead State 1-0 Stable State 1-1 Stable State 0-0

26. Static Versus Dynamic Live State 0-1 Stable State 1-1 Dead State 1-0 Stable State 0-0 Dynamic Stable1-13 Live14-35 Die36-45 Live Center Dead Center 14 - 34 35 - 45 Static

27. 3rd Phase – Using Pictures

28. Image Preparation Paint – Clip and Resize pictures – Resize according to the radius Scilab Image Processing toolbox – Converts the image into a matrix – [Apr1999]=imread('C:\program files\scilab- 4.1.2\contrib\siptoolbox\images\April_1999_Color_W106xH103.jpg') 0.340.210.110.420.12 0.190.110.130.220.19 0.110.13 0.440.12 0.490.33 0.290.330.190.210.22 0.390.210.130.340.46 0.220.44 0.360.19 0.340.17

29. Thresholding 0.340.210.110.420.120.110.420.12 0.190.110.130.220.190.130.220.19 0.220.390.140.110.130.140.110.13 0.230.330.430.440.120.430.440.12 0.390.440.490.330.440.490.33 0.220.390.140.110.130.140.110.13 0.230.330.430.440.120.430.440.12 0.390.440.490.330.440.490.33 0.120.390.440.490.330.440.490.33 = Summed value of all cells/(max cell value* radius^2) Round Value If < 0.5 Value = 0 If > 0.5 Value = 1 110 100111010 111101000 001001110 100011000 000001110 101010101 011100101 011001101 100110011 101

30. Calculate and Store This is completed for both picture matrix 1111111 0000000 0110001 1001100 0000010 0000000 1111111 = 20 20 123456789101112........ Center Cell state – Live (1) or dead (0) 1 123456789101112........ 1111111 1100100 0110001 1000100 0010011 1000101 1111111 = 28 2028 10 1111111 1111100 0110001 1011100 0010011 1010101 1111111 = 32 202832 101

31. Compare 274675…. Vector with radius summed values First Picture Matrix calculation ResultsSecond Picture Matrix calculation Results Vector with radius summed values 20283251149…. Vector with radius cell states 011010….110010 0-1 1-1 1-0 0-0 A dead cell becomes alive A live cell remains alive A live cell becomes dead A dead cell Remains dead 1234567.1234567.1234567.1234567. 0-1 1-1 1-0 0-0 April 1999 September 2003

32. 4 th Program - Output Live State 0-1 Dead State 1-0 Stable State 1-1 Stable State 0-0

33. Picture Rules Live State 0-1 Dead State 1-0 Stable State 0-0 Pictures Live1 - 49 Die17-50 Stable State 1-1

34. Picture Results Too long of a time period High value summed range producing life High value summed ranged producing death

35. Future Studies Future Research – Compare all series comparisons Missing rules – More samples What should represent a series? Long Term Goals – Correlate the rules with patterns – Aid in ongoing efforts

36. Test for Missing Rules 1234567891011121314151617181920 Compare COMPARE

37. Identify an Appropriate Time Series 1234567891011121314151617181920 Approximately 4 years

38. Goal Estimated Rules Program Estimated Rules

39. Visual Identification 1-3435-50 Life Death Water Soil Biomass Weather Randomness Over-crowding Correlate the rules with the patterns with an understanding of the surrounding environmental factors. AirSediments (animals, plants)Hot and Cold Temperatures

40. Conclusion Learning more about microbial communities and supporting other’s in their efforts will enable us to equip ourselves with knowledge to be used when the opportunity for future endeavors arise.

41. Committee Members Dr. Keith Schubert Dr. Richard Botting Dr. Ernesto Gomez Melissa Quintana quintanm@coyote.csusb.edu