@inproceedings{
 title = {Parameter estimation for stochastic hybrid models of biochemical reaction networks},
 type = {inproceedings},
 year = {2012},
 keywords = {[Biochemical reactions, Parameter identification,},
 id = {36bc46d5-f3ac-37d0-8543-7c1b37f42dc4},
 created = {2017-01-02T09:34:03.000Z},
 file_attached = {false},
 profile_id = {bbb99b2d-2278-3254-820f-2de6d915ce63},
 last_modified = {2017-03-22T13:51:34.979Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {The dynamics of biochemical reaction networks can be accurately described by stochastic hybrid models, where we assume that large chemical populations evolve deterministically and continuously over time while small populations change through random discrete reactions. We propose an algorithm for estimating the parameters of a given biochemical reaction network based on a stochastic hybrid model. We assume that noisy time series measurements of the chemical populations are available and follow a maximum likelihood approach to calibrate the parameters. We numerically approximate the likelihood and its derivatives for concrete values of the parameters and show that, based on this approximation, the maximization of the likelihood can be done efficiently. We substantiate the usefulness of our approach by applying it to several case studies from systems biology. Copyright 2012 ACM.},
 bibtype = {inproceedings},
 author = {Mikeev, L. and Wolf, V.},
 doi = {10.1145/2185632.2185657},
 booktitle = {HSCC'12 - Proceedings of the 15th ACM International Conference on Hybrid Systems: Computation and Control}
}

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