An Aggregation Technique for the Transient Analysis of Stiff Markov Chains
IEEE Transactions on Computers
Computing Poisson probabilities
Communications of the ACM
Efficient descriptor-vector multiplications in stochastic automata networks
Journal of the ACM (JACM)
The ubiquitous Kronecker product
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. III: linear algebra
Efficient Computation and Representation of Large Reachability Sets for Composed Automata
Discrete Event Dynamic Systems
A Toolbox for the Analysis of Discrete Event Dynamic Systems
CAV '99 Proceedings of the 11th International Conference on Computer Aided Verification
INFORMS Journal on Computing
The Kronecker product and stochastic automata networks
Journal of Computational and Applied Mathematics
SMART: The Stochastic Model checking Analyzer for Reliability and Timing
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
Modelling of Biochemical Reactions by Stochastic Automata Networks
Electronic Notes in Theoretical Computer Science (ENTCS)
Brief Communication: Discrete-time stochastic modeling and simulation of biochemical networks
Computational Biology and Chemistry
A numerical aggregation algorithm for the enzyme-catalyzed substrate conversion
CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
Review: Stochastic approaches for modelling in vivo reactions
Computational Biology and Chemistry
Approximation of Event Probabilities in Noisy Cellular Processes
CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
Concurrency and composition in a stochastic world
CONCUR'10 Proceedings of the 21st international conference on Concurrency theory
Approximation of event probabilities in noisy cellular processes
Theoretical Computer Science
Streamlined formulation of adaptive explicit-implicit tau-leaping with automatic tau selection
Winter Simulation Conference
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Stochastic models of biological networks properly take the randomness of molecular dynamics in living cells into account. Numerical solution approaches inspired by computational methods from applied probability can efficiently yield accurate results and have significant advantages compared to stochastic simulation. Examples for the success of non-simulative numerical analysis techniques in systems biology confirm the enormous potential.