%0 Journal Article %1 Senne2012EMMA %A Senne, Martin %A Trendelkamp-Schroer, Benjamin %A Mey, Antonia S.J.S. %A Schütte, Christof %A Noé, Frank %D 2012 %J Journal of Chemical Theory and Computation %K markov-model-analysis markov-state-models python software %N 7 %P 2223-2238 %R 10.1021/ct300274u %T EMMA: A Software Package for Markov Model Building and Analysis %U /brokenurl# http://dx.doi.org/10.1021/ct300274u %V 8 %X The study of folding and conformational changes of macromolecules by molecular dynamics simulations often requires the generation of large amounts of simulation data that are difficult to analyze. Markov (state) models (MSMs) address this challenge by providing a systematic way to decompose the state space of the molecular system into substates and to estimate a transition matrix containing the transition probabilities between these substates. This transition matrix can be analyzed to reveal the metastable, i.e., long-living, states of the system, its slowest relaxation time scales, and transition pathways and rates, e.g., from unfolded to folded, or from dissociated to bound states. Markov models can also be used to calculate spectroscopic data and thus serve as a way to reconcile experimental and simulation data. To reduce the technical burden of constructing, validating, and analyzing such MSMs, we provide the software framework EMMA that is freely available at https://simtk.org/home/emma.

@article{Senne2012EMMA, abstract = { The study of folding and conformational changes of macromolecules by molecular dynamics simulations often requires the generation of large amounts of simulation data that are difficult to analyze. Markov (state) models (MSMs) address this challenge by providing a systematic way to decompose the state space of the molecular system into substates and to estimate a transition matrix containing the transition probabilities between these substates. This transition matrix can be analyzed to reveal the metastable, i.e., long-living, states of the system, its slowest relaxation time scales, and transition pathways and rates, e.g., from unfolded to folded, or from dissociated to bound states. Markov models can also be used to calculate spectroscopic data and thus serve as a way to reconcile experimental and simulation data. To reduce the technical burden of constructing, validating, and analyzing such MSMs, we provide the software framework EMMA that is freely available at https://simtk.org/home/emma. }, added-at = {2016-06-05T00:13:21.000+0200}, author = {Senne, Martin and Trendelkamp-Schroer, Benjamin and Mey, Antonia S.J.S. and Schütte, Christof and Noé, Frank}, biburl = {https://www.bibsonomy.org/bibtex/2b516970cb83d4ef1158b8b9174e7351a/salotz}, description = {EMMA: A Software Package for Markov Model Building and Analysis - Journal of Chemical Theory and Computation (ACS Publications)}, doi = {10.1021/ct300274u}, eprint = {http://dx.doi.org/10.1021/ct300274u}, interhash = {b2de7ef013797229d050f643b8047b11}, intrahash = {b516970cb83d4ef1158b8b9174e7351a}, journal = {Journal of Chemical Theory and Computation}, keywords = {markov-model-analysis markov-state-models python software}, note = {PMID: 26588955}, number = 7, pages = {2223-2238}, timestamp = {2016-06-05T00:13:21.000+0200}, title = {EMMA: A Software Package for Markov Model Building and Analysis}, url = {/brokenurl# http://dx.doi.org/10.1021/ct300274u }, volume = 8, year = 2012 }