1. Stochastic Computing with Biomolecular Automata R. Adar, Y. Benenson, G. Linshiz, A. Rosner, N. Tishby, and E. Shapiro PNAS, vol. 101, no. 27, pp. 9960-9965, July 2004. Cho, Dong-Yeon

2. © 2004 SNU CSE Biointelligence Lab 2 Introduction Stochastic Computing  The choice between several alternative computation paths (each with a prescribed probability)  Random number generator  Biomolecular computer  A stochastic biomolecular computer would be more suitable for this biomedical tasks than a deterministic one. A Design Principle for Stochastic Computers

3. © 2004 SNU CSE Biointelligence Lab 3 Deterministic and Stochastic Finite Automata

4. © 2004 SNU CSE Biointelligence Lab 4 Previous Work

5. © 2004 SNU CSE Biointelligence Lab 5 Results (1/6) Calibration  We performed calibration with the four-symbol inputs aaab and bbba.  Computation trees used for the calibration of transition probabilities

6. © 2004 SNU CSE Biointelligence Lab 6 Results (2/6)  To determine the function mapping relative concentrations of transition molecules to transition probabilities  Transitions processing the symbol a provide a linear mapping.  Transitions processing b reveal a convex mapping.  This is apparently caused by the software molecules that result in state S1 (T4 and T8) having a higher reaction rate than the competing software molecules that result in state S0 (T3 and T7).

7. © 2004 SNU CSE Biointelligence Lab 7 Results (3/6)  To verify that the system is insensitive to fluctuations in input concentration  Using the input bbba and T1-T2 transition pair  The computation is insensitive to the different input concentrations used and the transition probability is determined solely by the ration between the transition molecules.

8. © 2004 SNU CSE Biointelligence Lab 8 Results (4/6)  Sensitivity of the probability distribution to the absolute concentration of the software molecules  The transition probability is indeed relatively insensitive to the absolute software concentrations and is defined mostly by the relative concentration ratio.

9. © 2004 SNU CSE Biointelligence Lab 9 Results (5/6) Running 4 Programs with 9 Inputs bbba, aaab, abbbbbba, babbaaab, baaaaaab, babbabbbbbba, baaaabbbbbba, abbbbabbaaab, abbbbaaaaaab

10. © 2004 SNU CSE Biointelligence Lab 10 Results (6/6)