1. Anatoly B. Kolomeisky Department of Chemistry MECHANISMS AND TOPOLOGY DETERMINATION OF COMPLEX NETWORKS FROM FIRST-PASSAGE THEORETICAL APPROACH

2. Chemical and Biological Networks Molecular interaction map of the regulatory network that controls the mammalian cell cycle and DNA repair systems K.W. Kohn, Mol. Biol. Cell 10, 2703 (1999) Colors indicate different types of interactions

3. Chemical and Biological Networks Typical experiments probes event that start at the state i and end at the state j Inverse problem: difficult to solve because more than 1 mechanism could fit experimental data

4. Experimental Examples: Dynamics of linear Myosin V and rotary F1-ATPase molecular motors L.S. Milescu, et al., Biophys. J., 91, 1156 (2006)

5. Chemical and Biological Networks Typical experiments probes event that start at the state i and end at the state j It has been long recognized that experimental measurements of events is associated with first- passage problems Well developed subject – see books by van Kampen and Redner- but rarely applied

6. Current Theoretical Methods Monte Carlo computer simulations- very popular and convenient for small networks Problems: 1)Computational cost becomes too large for large networks and high- resolution data 2)Some minimal information on networks is needed in order to decrease the number of systems to be simulated 3)More than one model can fit data

7. Current Theoretical Methods Matrix method (maximum-likelihood) utilizes probability theory to partition all states into several groups to simplify the analysis; has been applied for single ion- channel measurements Problems: does not work for complex networks with multiple transitions L.S. Milescu, et al., Biophys. J., 91, 1156 (2006)

8. Current Theoretical Methods Absorbing Boundary Methods- absorbing boundary conditions are assumed so the modified problem is easier to solve Problems: 1) requires some knowledge of network topology; 2) not efficient for calculating average values J.-C. Liao, et al., PNAS USA, 104, 3171 (2007)

9. Our Method Our hypothesis: Dwell-time distributions of events that start at some initial state and end at some final state contain all information on the local structure of the network and dynamic transitions between these states Explanations: At early times – the distribution is dominated by the shortest pathway At transient times – the effect of other pathways starts to build up At large times – dynamics is averaged over all pathways

10. Our Method Consider 1D networks Define a first-passage probability function f N,m (t) – probability that events that starts at m at t=0 will end up for the first time at N at time t Backward master equations

11. Our Method 1D networks Our idea to use Laplace transformations All dynamic properties of the system can be calculated explicitly at all times Consider first the simplest case: homogeneous 1D networks with u m =u and w m =w

12. Results Homogeneous 1D networks: N=10 0 12 3 456 7 8 9 10 at t<<1

13. Results Inhomogeneous 1D networks (general u j and w j ) – from analytical calculations at early times N=10

14. Results Inhomogeneous 1D networks (general u j and w j ) – from analytical calculations: at large times π N,m ; τ N,m - probability and mean dwell time to reach the final state

15. Results 1D networks with irreversible detachments Example: photon blinking in single- molecule fluorescence measurements Our analytical calculations show that irreversible detachments do not change early-time and large-time behavior

16. Results Our analytical calculations show that irreversible detachments do not change early-time behavior 1D networks with irreversible detachments N=10

17. Results More complex networks with different topology Networks with branches Our hypothesis is tested via Monte Carlo computer simulations since analytical calculations are too difficult Networks with parallel pathways

18. Results Computer simulations fully agree with our hypothesis- early time behavior is dominated by the shortest pathway between the initial and final states

19. Application of the Method Example 1: Unwinding of DNA by hepatitis C virus helicase NS3 S. Myong, et al., Science 317, 513 (2007) Single-molecule fluorescence experiments show that NS3 unwinds DNA in 3-bp steps, although structural studies suggest that 1 bp is the elementary step

20. Application of the Method Unwinding of DNA by hepatitis C virus helicase NS3 S. Myong, et al., Science 317, 513 (2007) Dwell-time distributions of unwinding events have been measured

21. Application of the Method Unwinding of DNA by hepatitis C virus helicase NS3 S. Myong, et al., Science 317, 513 (2007) Our method (model- free) also predicts 2 hidden states during the unwinding Caution must be used in interpretation

22. Application of the Method Example 2: Motility of myosin-V motor proteins A. Yildiz, et al., Science, 300, 2601 (2003)

23. Application of the Method Motility of myosin-V motor proteins A. Yildiz, et al., Science, 300, 2601 (2003) Single-molecule trajectories with fluorescent label on one of the motor protein’s heads Supports hand- over-hand mechanism and predicts a hidden state

24. Application of the Method Motility of myosin-V motor proteins A. Yildiz, et al., Science, 300, 2601 (2003) Distribution of dwell times

25. Application of the Method Motility of myosin-V motor proteins A. Yildiz, et al., Science, 300, 2601 (2003) Our method predicts 1 hidden state, in agreement with hand-over-hand mechanism

26. Discussions: Microscopic origin of our analysis: 1)At early times only the fastest events are recorded and they proceed along the shortest pathway 2)At large times dynamics of reaching the final state is averaged over all pathways 3)Results do not depend on absolute values of transition rates In principle, all dynamic and structural information on the network can be obtained by analyzing experimental data with varying initial and final states

27. CONCLUSIONS 1)A new theoretical approach to predict properties of chemical and biological networks is developed 2)It is based on hypothesis that topology determines the first- passage distributions 3)It is argued that distributions at early times explicitly depend on the number of intermediate states along the shortest pathway. 4)The hypothesis is supported by analytical calculations on 1D networks and by Monte Carlo computer simulations on more complex networks 5)The method is illustrated by analyzing 2 motor protein systems

28. Acknowledgements: The work done by postdoc Xin Li Financial Support: NSF, NIH, The Welch Foundation