Citation

If you find MolClustPy useful, please cite

Chattaraj, A., I. Nalagandla, L. M. Loew and M. L. Blinov, MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters. Bioinformatics, 2023: p. btad385. Publication Link

Biological studies where the package was applied

Analysis of multivalent biomolecular clustering, similar to Nephrin-Nck-NWASP

Chattaraj, A., M.L. Blinov, and L.M. Loew, The solubility product extends the buffering concept to heterotypic biomolecular condensates. eLife, 2021. 10: p. e67176. PubMed Link
Chattaraj, A. and L.M. Loew, The maximum solubility product marks the threshold for condensation of multivalent biomolecules. Biophysical Journal, 2023. 122(9): p. 1678-1690. PubMed Link

Theoretical background

Rule-based modeling and BioNetGen

Faeder, J. R., Blinov, M. L., & Hlavacek, W. S. (2009). Rule-based modeling of biochemical systems with BioNetGen. In Systems biology (pp. 113-167). Humana Press. PubMed Link
Harris, L.A., et al., BioNetGen 2.2: advances in rule-based modeling. Bioinformatics, 2016. 32(21): p. 3366-3368. PubMed Link
Yang, J., et al., Kinetic Monte Carlo method for rule-based modeling of biochemical networks. Physical Review E, 2008. 78(3): p. 031910. PubMed Link
Sneddon, M.W., J.R. Faeder, and T. Emonet, Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat Methods, 2011. 8(2): p. 177-83. PubMed Link
Andrews, S.S., Rule-Based Modeling Using Wildcards in the Smoldyn Simulator. Methods Mol Biol, 2019. 1945: p. 179-202. PubMed Link

Rule-based modeling with Virtual Cell

Schaff, J. C., Vasilescu, D., Moraru, I. I., Loew, L. M., & Blinov, M. L. (2016). Rule-based modeling with Virtual Cell. Bioinformatics, 32(18), 2880-2882. PubMed Link
Blinov, M.L., et al., Compartmental and Spatial Rule-Based Modeling with Virtual Cell. Biophys J, 2017. 113(7): p. 1365-1372.PubMed Link
Moraru, I. I., Schaff, J. C., Slepchenko, B. M., Blinov, M. L., Morgan, F., Lakshminarayana, A., … & Loew, L. M. (2008). Virtual Cell modelling and simulation software environment. IET systems biology, 2(5), 352-362. PubMed Link

Clustering of multivalent proteins

Mayer, B. J., Blinov, M. L., & Loew, L. M. (2009). Molecular machines or pleiomorphic ensembles: signaling complexes revisited. Journal of biology, 8(9), 1-8.
Suderman, R. and E.J. Deeds, Machines vs. Ensembles: Effective MAPK Signaling through Heterogeneous Sets of Protein Complexes. PLoS Comput Biol, 2013. 9(10): p. e1003278.
Falkenberg, C.V., M.L. Blinov, and L.M. Loew, Pleomorphic ensembles: formation of large clusters composed of weakly interacting multivalent molecules. Biophysical journal, 2013. 105(11): p. 2451-2460.
Falkenberg, C.V., J.H. Carson, and M.L. Blinov, Multivalent Molecules as Modulators of RNA Granule Size and Composition. Biophys J, 2017. 113(2): p. 235-245.
Goldman, J., S. Andrews, and D. Bray, Size and composition of membrane protein clusters predicted by Monte Carlo analysis. Eur Biophys J, 2004. 33(6): p. 506-12.
Nag, A., et al., Modeling and Simulation of Aggregation of Membrane Protein LAT with Molecular Variability in the Number of Binding Sites for Cytosolic Grb2-SOS1-Grb2. PLOS ONE, 2012. 7(3): p. e28758
Hsieh, M.Y., et al., Spatio-temporal modeling of signaling protein recruitment to EGFR. BMC Syst Biol, 2010. 4: p. 57.
Nag, A., et al., Aggregation of membrane proteins by cytosolic cross-linkers: theory and simulation of the LAT-Grb2-SOS1 system. Biophys J, 2009. 96(7): p. 2604-23.
Nag, A., et al., A detailed mathematical model predicts that serial engagement of IgE-Fc epsilon RI complexes can enhance Syk activation in mast cells. J Immunol, 2010. 185(6): p. 3268-76.
Marquez-Lago, T.T. and S. Steinberg, Stochastic model of ERK-mediated progesterone receptor translocation, clustering and transcriptional activity. Sci Rep, 2022. 12(1): p. 11791.
Wollman, A.J.M., et al., Critical roles for EGFR and EGFR-HER2 clusters in EGF binding of SW620 human carcinoma cells. J R Soc Interface, 2022. 19(190): p. 20220088.