Quantitative comparison of alternative methods for coarse-graining biological networks
Open accessAbstract
Markov models and master equations are a powerful means of modeling dynamic processes like protein conformational changes. However, these models are often difficult to understand because of the enormous number of components and connections between them. Therefore, a variety of methods have been developed to facilitate understanding by coarse-graining these complex models. Here, we employ Bayesian model comparison to determine which of these coarse-graining methods provides the models that are most faithful to the original set of states. We find that the Bayesian agglomerative clustering engine and the hierarchical Nyström expansion graph (HNEG) typically provide the best performance. Surprisingly, the original Perron cluster cluster analysis (PCCA) method often provides the next best results, outperforming the newer PCCA+ method and the most probable paths algorithm. We also show that the differences between the models are qualitatively significant, rather than being minor shifts in the boundaries between states. The performance of the methods correlates well with the entropy of the resulting coarse-grainings, suggesting that finding states with more similar populations (i.e., avoiding low population states that may just be noise) gives better results.
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The performance of several coarse-graining methods has been compared, although the result depends on the choice of benchmark.
“The performance of several such methods have been compared [36], although we note that the result of such a comparison depends on the choice of benchmark.”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [36]
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- Authors
- Bowman, G. R., Meng, L., & Huang, X.
- Affiliations
- University of California, Berkeley (US)
- Hong Kong University of Science and Technology (HK)
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2013-07-08
- Volume
- 139
- Issue
- 12
- Pages
- 121905-121905
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- PMC
- PMC3724791
- OpenAlex
- W2015656580
- Cited by
- 55
- References
- 37
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 1 · 2023: 3 · 2022: 1 · 2021: 8 · 2020: 7 · 2019: 1 · 2018: 7 · 2017: 4 · 2016: 8 · 2015: 3 · 2014: 6 · 2013: 1
- FWCI
- 2.69
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- The Journal of Chemical Physics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- National Science Foundation (1048789)
- Burroughs Wellcome Fund
- Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California Berkeley
- National Natural Science Foundation of China (973 Program, 21273188)
- Hong Kong University of Science and Technology
- National Institutes of Health (NIH-R01, GM050945)
- National Key Research and Development Program of China (2013CB834703)
- National Institute of General Medical Sciences (R01GM050945)
- Division of Chemistry
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- coarse-graining, Markov model, master equation, Bayesian model selection, agglomerative clustering, PCCA, most probable path, protein conformational dynamics, state aggregation, entropy (information theory)
- MeSH terms
- Alanine, Algorithms, beta-Lactamases, Dipeptides, Kinetics, Markov Chains, Models, Molecular, Peptide Fragments, Protein Conformation, Cluster Analysis, Neurofilament Proteins, Entropy, Computational Biology
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