A compositional approach to performance modelling
A compositional approach to performance modelling
`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
XS-systems: eXtended S-Systems and Algebraic Differential Automata for Modeling Cellular Behavior
HiPC '02 Proceedings of the 9th International Conference on High Performance Computing
XS-systems: eXtended S-Systems and Algebraic Differential Automata for Modeling Cellular Behavior
HiPC '02 Proceedings of the 9th International Conference on High Performance Computing
The Factored Frontier Algorithm for Approximate Inference in DBNs
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
PRISM: Probabilistic Symbolic Model Checker
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
COPASI---a COmplex PAthway SImulator
Bioinformatics
Probabilistic model checking of complex biological pathways
Theoretical Computer Science
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Relating continuous and discrete PEPA models of signalling pathways
Theoretical Computer Science
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Some Investigations Concerning the CTMC and the ODE Model Derived From Bio-PEPA
Electronic Notes in Theoretical Computer Science (ENTCS)
Probabilistic Approximations of Signaling Pathway Dynamics
CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Probability: Theory and Examples
Probability: Theory and Examples
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Analysis of signalling pathways using continuous time markov chains
Transactions on Computational Systems Biology VI
Transactions on Computational Systems Biology VII
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
A hybrid factored frontier algorithm for dynamic Bayesian network models of biopathways
Proceedings of the 9th International Conference on Computational Methods in Systems Biology
A Hybrid Factored Frontier Algorithm for Dynamic Bayesian Networks with a Biopathways Application
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Dynamic bayesian networks: a factored model of probabilistic dynamics
ATVA'12 Proceedings of the 10th international conference on Automated Technology for Verification and Analysis
GPU code generation for ODE-based applications with phased shared-data access patterns
ACM Transactions on Architecture and Code Optimization (TACO)
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Bio-chemical networks are often modeled as systems of ordinary differential equations (ODEs). Such systems will not admit closed form solutions and hence numerical simulations will have to be used to perform analyses. However, the number of simulations required to carry out tasks such as parameter estimation can become very large. To get around this, we propose a discrete probabilistic approximation of the ODEs dynamics. We do so by discretizing the value and the time domain and assuming a distribution of initial states w.r.t. the discretization. Then we sample a representative set of initial states according to the assumed initial distribution and generate a corresponding set of trajectories through numerical simulations. Finally, using the structure of the signaling pathway we encode these trajectories compactly as a dynamic Bayesian network. This approximation of the signaling pathway dynamics has several advantages. First, the discretized nature of the approximation helps to bridge the gap between the accuracy of the results obtained by ODE simulation and the limited precision of experimental data used for model construction and verification. Second and more importantly, many interesting pathway properties can be analyzed efficiently through standard Bayesian inference techniques instead of resorting to a large number of ODE simulations. We have tested our method on ODE models of the EGF-NGF signaling pathway [1] and the segmentation clock pathway [2]. The results are very promising in terms of accuracy and efficiency.