{"month":"02","date_created":"2018-12-11T11:54:25Z","citation":{"short":"J. Ruess, J. Lygeros, ACM Transactions on Modeling and Computer Simulation 25 (2015).","ama":"Ruess J, Lygeros J. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. <i>ACM Transactions on Modeling and Computer Simulation</i>. 2015;25(2). doi:<a href=\"https://doi.org/10.1145/2688906\">10.1145/2688906</a>","mla":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” <i>ACM Transactions on Modeling and Computer Simulation</i>, vol. 25, no. 2, 8, ACM, 2015, doi:<a href=\"https://doi.org/10.1145/2688906\">10.1145/2688906</a>.","ieee":"J. Ruess and J. Lygeros, “Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks,” <i>ACM Transactions on Modeling and Computer Simulation</i>, vol. 25, no. 2. ACM, 2015.","chicago":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” <i>ACM Transactions on Modeling and Computer Simulation</i>. ACM, 2015. <a href=\"https://doi.org/10.1145/2688906\">https://doi.org/10.1145/2688906</a>.","ista":"Ruess J, Lygeros J. 2015. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 25(2), 8.","apa":"Ruess, J., &#38; Lygeros, J. (2015). Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. <i>ACM Transactions on Modeling and Computer Simulation</i>. ACM. <a href=\"https://doi.org/10.1145/2688906\">https://doi.org/10.1145/2688906</a>"},"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"acknowledgement":"HYCON2; EC; European Commission\r\n","date_published":"2015-02-01T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"01","status":"public","year":"2015","publisher":"ACM","language":[{"iso":"eng"}],"intvolume":"        25","type":"journal_article","publist_id":"5238","date_updated":"2021-01-12T06:53:41Z","volume":25,"oa_version":"None","abstract":[{"text":"Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of themolecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation that governs the time evolution of the probability distribution of the system. This, however, is rarely possible, and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this article is to base methods on only some low-order moments of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance.","lang":"eng"}],"article_number":"8","publication_status":"published","issue":"2","publication":"ACM Transactions on Modeling and Computer Simulation","quality_controlled":"1","scopus_import":1,"title":"Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks","_id":"1861","doi":"10.1145/2688906","author":[{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","last_name":"Ruess","orcid":"0000-0003-1615-3282","full_name":"Ruess, Jakob","first_name":"Jakob"},{"last_name":"Lygeros","full_name":"Lygeros, John","first_name":"John"}]}