Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Formal Methods in System Design - Special issue on The First Federated Logic Conference (FLOC'96), part II
Model checking
Symbolic Model Checking for Probabilistic Processes
ICALP '97 Proceedings of the 24th International Colloquium on Automata, Languages and Programming
Probabilistic Verification of Discrete Event Systems Using Acceptance Sampling
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Simulation and verification for computational modelling of signalling pathways
Proceedings of the 38th conference on Winter simulation
The temporal logic of programs
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Predicting experimental quantities in protein folding kinetics using stochastic roadmap simulation
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
PRISM: a tool for automatic verification of probabilistic systems
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Symbolic model checking for sequential circuit verification
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Temporal Logics for Phylogenetic Analysis via Model Checking
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 0.00 |
We present a novel approach for predicting protein folding kinetics using techniques from the field of model checking. This represents the first time model checking has been applied to a problem in the field of structural biology. The protein's energy landscape is encoded symbolically using Binary Decision Diagrams and related data structures. Questions regarding the kinetics of folding are encoded as formulas in the temporal logic CTL. Model checking algorithms are then used to make quantitative predictions about the kinetics of folding. We show that our approach scales to state spaces as large as 1023 when using exact algorithms for model checking. This is at least 14 orders of magnitude larger than the number of configurations considered by comparable techniques. Furthermore, our approach scales to state spaces at least as large as 1032 unique configurations when using approximation algorithms for model checking. We tested our method on 19 test proteins. The quantitative predictions regarding folding rates for these test proteins are in good agreement with experimentally measured values, achieving a correlation coefficient of 0.87.