The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Building Markov state models using optimal transport theory
Open accessPublished version of a preprint
Abstract
Markov State Models (MSMs) describe the rates and routes in conformational dynamics of biomolecules. Computational estimation of MSMs can be expensive because molecular simulations are slow to find and sample the rare transient events. We describe here an efficient approximate way to determine MSM rate matrices by combining maximum caliber (maximizing path entropies) with optimal transport theory (minimizing some path cost function, as when routing trucks on transportation networks) to patch together transient dynamical information from multiple non-equilibrium simulations. We give toy examples.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Dill, K. A., Ozkan, S. B., Shell, M. S., & Weikl, T. R. (2008) · Annual Review of Biophysics · doi:10.1146/annurev.biophys.37.092707.153558 · Cited by 1,163
Self-citationMarkov state models of biomolecular conformational dynamics
Chodera, J. D., & Noé, F. (2014) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2014.04.002 · Cited by 776
Flagged first, then by citations
Research works printed but not linked: 28
- 2018, J. Am. Chem. Soc., 140, 2386, 10.1021/jacs.7b12191 · DOI
- 2015, Biochim. Biophys. Acta (BBA) - Gen. Subj., 1850, 872, 10.1016/j.bbagen.2014.10.019 · DOI · Paper page
- 2018, J. Chem. Phys., 148, 010901, 10.1063/1.5012990 · DOI
- 2014, J. Chem. Theory Comput., 10, 3002, 10.1021/ct5001389 · DOI
- 2015, Phys. Rev. E, 92, 042149, 10.1103/physreve.92.042149 · DOI
- 2018, J. Chem. Phys., 148, 091101, 10.1063/1.5023232 · DOI
- 2015, J. Chem. Theory Comput., 11, 5464, 10.1021/acs.jctc.5b00537 · DOI
- 2018, J. Chem. Theory Comput., 14, 1111, 10.1021/acs.jctc.7b01126 · DOI
- 2016, Proc. Natl. Acad. Sci. U. S. A., 113, 2839, 10.1073/pnas.1600917113 · DOI · Paper page
- 2017, J. Chem. Phys., 147, 152701, 10.1063/1.4983727 · DOI
- 2018, J. Chem. Phys., 149, 234105, 10.1063/1.5064856 · DOI
- 2018, J. Chem. Phys., 149, 224101, 10.1063/1.5060960 · DOI
- 2018, J. Chem. Theory Comput., 15, 25, 10.1021/acs.jctc.8b00563 · DOI
- 2016, J. Chem. Theory Comput., 12, 5768, 10.1021/acs.jctc.6b00938 · DOI
- 2017, Biophys. J., 113, 785, 10.1016/j.bpj.2017.07.009 · DOI
- 2009, Proc. Natl. Acad. Sci. U. S. A., 106, 19011, 10.1073/pnas.0905466106 · DOI · Paper page
- 2017, J. Chem. Phys., 146, 094104, 10.1063/1.4976518 · DOI
- 2017, J. Chem. Phys., 146, 154104, 10.1063/1.4979344 · DOI
- 2008, Optimal Transport: Old and New
- 2018, SIAM J. Imaging Sci., 11, 643, 10.1137/17m1140431 · DOI
- G. Schiebinger et al., preprint bioRxiv:191056 (2017).
- 2016, Optimal Transport Methods in Economics
- G. Peyré and M. Cuturi, “Computational Optimal Transport,” arXiv:1803.00567 (2018). · DOI
- 2013, Sinkhorn distances: Lightspeed computation of optimal transport, 2292
- P. Dixit, E. Lyashenko, M. Niepel, and D. Vitkup, preprint bioRxiv:137513 (2017).
- G. Tkacik, E. Schneidman, I. Berry, J. Michael, and W. Bialek, preprint arXiv:q-bio/0611072 (2006). · arXiv
- T. Broderick and M. Dud, e-print arXiv:0712.2437v2 (2007). · arXiv
- 2018, Computation, 6, 15, 10.3390/computation6010015 · DOI
Metadata
- Authors
- Dixit, P. D., & Dill, K. A.
- Affiliations
- Columbia University (US)
- Stony Brook University (US)
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2019-02-06
- Volume
- 150
- Issue
- 5
- Pages
- 054105-054105
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2913155865
- Cited by
- 9
- References
- 2
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 3 · 2019: 2
- FWCI
- 0.58
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- The Journal of Chemical Physics (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Royal College of Surgeons in Ireland (submittedVersion, cc-by-nc-nd)
- Funders
- NSF (PHY1205881)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Advanced Thermodynamics and Statistical Mechanics
- Keywords
- Markov state models, optimal transport, maximum caliber, path entropy, conformational dynamics, biomolecules, rate matrix, non-equilibrium simulation, path cost minimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review