| Datasets
C. Chang, M. Garcia-Alcala, L. Saiz, J.M.G. Vilar, and P. Cluzel (2022), Data for: Robustness of DNA Looping Across Multiple Cell Divisions in Individual Bacteria, Harvard Dataverse. https://doi.org/10.7910/DVN/DN0VAJ.
COVID19 Web App J.M.G. Vilar and L. Saiz (2020), COVID-19 Dynamics, https://covid19dynamics.herokuapp.com/.
Open Source Software Packages J.M.G. Vilar and L. Saiz (2023), Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series, https://github.com/DIDNN/NPIs. J.M.G. Vilar and L. Saiz (2019), Scaling and Regression over Nearest Neighbors for Single-Cell Signaling Prediction in Breast Cancer, https://www.synapse.org/#!Synapse:syn21188145. J.M.G. Vilar and L. Saiz (2017), Combined ridge, support-vector, and random-forest regression approach for odor prediction, https://www.synapse.org/#!Synapse:syn3794504. D. Nicklas and L. Saiz (2013), “TherapySim” (released on December 25th, 2013 for Python; TherapySim website: http://sourceforge.net/projects/therapysim) is a Python package to predict potential therapeutic targets in signaling networks. Software can be downloaded at http://sourceforge.net/projects/therapysim and the corresponding tutorials are available at http://sourceforge.net/projects/therapysim/. J.M.G. Vilar and L. Saiz (2010), “CplexA” (latest version released on December 12th, 2012 for Mathematica, Python, and Matlab; first released on April 19th, 2010 for Mathematica; CplexA website: http://cplexa.sourceforge.net/) is a Mathematica, Python, and Matlab package to compute the probabilities and average properties of macromolecular assembly and its effects in gene regulation. Software can be downloaded at http://cplexa.sourceforge.net/ and the corresponding Mathematica, Matlab, and Python implementation tutorials are available at http://sourceforge.net/projects/cplexa/.
Articles in journals J.M.G. Vilar and L. Saiz, Actionable forecasting as a determinant of function in noisy biological systems, arXiv, 2404.07895 (2024). National Academies of Sciences, Engineering, and Medicine [including L. Saiz as contributor], Report of the Committee on Proposal Evaluation for Allocation of Supercomputing Time for the Study of Molecular Dynamics: Fourteenth Round. Washington, DC: The National Academies Press, https://doi.org/10.17226/27336 (2023). J.M.G. Vilar and L. Saiz, Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series, Science Advances, 9:eadf0673 (2023).
J.M.G. Vilar and L. Saiz, Multi-landmark alignment of genomic signals reveals conserved expression patterns across transcription start sites, Scientific Reports, 13:10835 (2023). J.M.G. Vilar, J.M. Rubi, and L. Saiz, Chaperone-driven entropic separation of amyloid nanofilament bundles, bioRxiv, 2023.05.24.542046 (2023). J.M.G. Vilar and L. Saiz, The unreasonable effectiveness of equilibrium gene regulation through the cell cycle, bioRxiv, 2023.03.31.535089 (2023). National Academies of Sciences, Engineering, and Medicine [including L. Saiz as contributor], Report of the Committee on Proposal Evaluation for Allocation of Supercomputing Time for the Study of Molecular Dynamics: Thirteenth Round. Washington, DC: The National Academies Press, https://doi.org/10.17226/26746 (2022). C. Chang, M. Garcia-Alcala, L. Saiz, J.M.G. Vilar, and P. Cluzel, Robustness of DNA Looping Across Multiple Cell Divisions in Individual Bacteria, Proc. Natl. Acad. Sci. USA, 119(33):e2200061119 National Academies of Sciences, Engineering, and Medicine [including L. Saiz as contributor], Report of the Committee on Proposal Evaluation for Allocation of Supercomputing Time for the Study of Molecular Dynamics: Twelfth Round. Washington, DC: The National Academies Press, https://doi.org/10.17226/26317 (2021). A. Gabor, M. Tognetti, A. Driessen, J. Tanevski, B. Guo, W. Cao, H. Shen, T. Yu, V. Chung, Single Cell Signaling in Breast Cancer DREAM Consortium [including L. Saiz], B. Bodenmiller, and J. Saez-Rodriguez, Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd, Mol. Syst. Biol., 17:e10402 (2021). J.M.G. Vilar and L. Saiz, Reliably quantifying the evolving worldwide dynamic state of the COVID-19 outbreak from death records, clinical parametrization, and demographic data, Scientific Reports, 11, 19952 (2021). J.M.G. Vilar and L. Saiz, Ascertaining the initiation of epidemic resurgences: an application to the COVID-19 second surges in Europe and the Northeast United States, R. Soc. Open Sci., 8:210773 (2021). A. Franco, P. Gracia, A. Colom, J.D. Camino, J.A Fernández-Higuero, N. Orozco, A. Dulebo, L. Saiz, N. Cremades, J.M.G. Vilar, A. Prado, and A. Muga, All-or-none amyloid disassembly via chaperone-triggered fibril unzipping favors clearance of alpha-synuclein toxic species, Proc. Natl. Acad. Sci. USA, 118(36):e2105548118 (2021). I. Jeddi and L. Saiz, Computational Design of Single-Stranded DNA Hairpin Aptamers Immobilized on a Biosensor Substrate, Scientific Reports, 11, 10984 (2021). National Academies of Sciences, Engineering, and Medicine [including L. Saiz as contributor], Report of the Committee on Proposal Evaluation for Allocation of Supercomputing Time for the Study of Molecular Dynamics: Eleventh Round. Washington, DC: The National Academies Press, https://doi.org/10.17226/25910 (2020). National Academies of Sciences, Engineering, and Medicine [including L. Saiz as contributor], Report of the Committee on Proposal Evaluation for Allocation of Supercomputing Time for the Study of Molecular Dynamics: Tenth Round. Washington, DC: The National Academies Press, https://doi.org/10.17226/25577 (2019). M.L. Fanani, J.V. Busto, J. Sot, J.L. Abad, G. Fabrías, L. Saiz, J.M.G. Vilar, F.M. Goñi, B. Maggio, and A. Alonso, Clearly Detectable, Kinetically Restricted Solid-Solid Phase Transition in cis-Ceramide Monolayers, Langmuir, 34, 11749-11758 (2018). J.M.G. Vilar and L. Saiz, Computing at the front-end by receptor networks, Cell Systems, 5, 316-318 (2017). L. Saiz, Insights into signaling and the functional complexity of biological membranes, J. Membr. Biol., 250, 335-336 (2017). I. Jeddi and L. Saiz, Three-dimensional modeling of single stranded DNA hairpins for aptamer-based biosensors, Scientific Reports, 7, 1178 (2017). J. Doudna, R. Bar-Ziv, J. Elf, V. Noireaux, J. Berro, L. Saiz, D. Vavylonis, J.-L. Faulon, and P. Fordyce, How Will Kinetics and Thermodynamics Inform Our Future Efforts to Understand and Build Biological Systems?, Cell Systems, 4, 144-146 (2017). A. Keller, R.C. Gerkin, Y. Guan, A. Dhurandhar, G. Turu, B. Szalai, J.D. Mainland, Y. Ihara, C.W. Yu., R. Wolfinger, C. Vens, L. Schietgat, K. De Grave, R. Norel, G. Stolovitzky, G.A. Cecchi, L.B. Vosshall, P. Meyer, DREAM Olfaction Prediction Consortium [including L. Saiz], Predicting human olfactory perception from chemical features of odor molecules, Science, 355, 820-826 (2017).
J.M.G. Vilar and L. Saiz, Suppression and enhancement of transcriptional noise by DNA looping, Phys. Rev. E, 89, 062703 (2014). S.K. Sinha and L. Saiz, Determinants of protein-ligand complex formation in the thyroid hormone receptor α: a Molecular Dynamics simulation study, Comp. Theor. Chem., 1038, 57-66 (2014). Q. Mei and L. Saiz, Literature-based automated reconstruction, expansion, and refinement of the TGF-β superfamily ligand-receptor network, J. Membr. Biol., 247, 381-386 (2014). D. Nicklas and L. Saiz, In silico identification of potential therapeutic targets in the TGF-β signal transduction pathway, Mol. BioSyst., 10 (3), 537-548 (2014). D. Nicklas and L. Saiz, Characterization of negative feedback network motifs in the TGF-β signaling pathway, PLoS ONE, 8(12): e83531 (2013). J.M.G. Vilar and L. Saiz, Reliable prediction of complex phenotypes from a modular design in free energy space: an extensive exploration of the lac operon, ACS Synthetic Biology, 2, 576–586 (2013). D. Nicklas and L. Saiz, Computational modeling of smad-mediated negative feedback and crosstalk in the TGF-β superfamily network, J. R. Soc. Interface, 10, 20130363 (2013). J.M.G. Vilar and L. Saiz, Systems Biophysics of Gene Expression, Biophys. J., 104, 2574-2585 (2013).
L. Saiz, The physics of protein-DNA interaction networks in the control of gene expression, Journal of Physics: Condensed Matter, 24, 193102 (2012). J.M.G. Vilar and L. Saiz, Trafficking coordinate description of intracellular transport control of signaling networks, Biophysical Journal, 101, 2315-2323 (2011).
J.M.G. Vilar and L. Saiz, Control of gene expression by modulated self-assembly, Nucleic Acids Research, 39, 6854-6863 (2011). Supplementary Data: [pdf].
J.M.G. Vilar and L. Saiz, CplexA: a Mathematica package to study macromolecular-assembly control of gene expression, Bioinformatics, 26, 2060-2061 (2010). Supplementary Information: Supplementary Data files 1 [pdf] and 2 [nb]. L. Saiz and J.M.G. Vilar, Protein-protein/DNA interaction networks: versatile macromolecular structures for the control of gene expression, IET Systems Biology, 2, 247-255 (2008). L. Saiz and J.M.G. Vilar, Ab initio thermodynamic modeling of distal multisite transcription regulation, Nucleic Acids Research, 36, 726-731 (2008). L. Saiz and J.M.G. Vilar, Multilevel deconstruction of the in vivo behavior of looped DNA-protein complexes, PLoS ONE, 2(4): e355. doi:10.1371/journal.pone.0000355 (2007). S. Vemparala, L. Saiz, R.G. Eckenhoff, and M.L. Klein, Partitioning of anesthetics into a lipid bilayer and their interaction with membrane-bound peptide bundles, Biophys. J., 91, 2815-2825 (2006). J.M.G. Vilar and L. Saiz, Multiprotein DNA looping, Phys. Rev. Lett., 96, 238103 (2006). L. Saiz and J.M.G. Vilar, DNA looping: the consequences and its control, Curr. Opin. Struct. Biol., 16, 344-350 (2006). L. Saiz and J. M. G. Vilar, Stochastic dynamics of macromolecular-assembly networks, Molecular Systems Biology, 2, 2006.0024 (2006). Leonor Saiz, J. Miguel Rubi, and Jose M.G. Vilar, Inferring the in vivo looping properties of DNA, Proc. Natl. Acad. Sci. USA, 102, 17642-17645 (2005). Jose M.G. Vilar and Leonor Saiz, DNA looping in gene regulation: From the assembly of macromolecular complexes to the control of transcriptional noise, Current Opinion in Genetics & Development, 15, 136-144 (2005). Leonor Saiz and M.L. Klein, The transmembrane domain of the acetylcholine receptor: Insights from simulations on synthetic peptide models, Biophys. J., 88, 959-970 (2005). M. Pickholz, Leonor Saiz, and M.L. Klein, Concentration effects of volatile anesthetics on the properties of model membranes: A coarse-grain approach, Biophys. J., 88, 1524-1534 (2005). Leonor Saiz, S. Bandyopadhyay, and M.L. Klein, Effect of the pore region of a transmembrane ion-channel on the physical properties of a simple membrane, J. Phys. Chem. B, 108, 2608-2613 (2004). L. Koubi, Leonor Saiz, M. Tarek, D. Scharf, and M.L. Klein, Influence of anesthetic and nonimmobilizer molecules on the physical properties of a polyunsaturated lipid bilayer, J. Phys. Chem. B 107, 14500-14508 (2003). Leonor Saiz, S. Bandyopadhyay, and M.L. Klein, Towards an understanding of complex biological membranes from atomistic molecular dynamics simulations, Bioscience Reports 22, 151-173 (2002). Leonor Saiz and M.L. Klein, Computer simulation studies of model biological membranes, Accounts of Chemical Research 35, 482-489 (2002). E. Guardia, J. Marti, J.A. Padro, Leonor Saiz, and A.V. Komolkin, Dynamics in hydrogen bonded liquids: water and alcohols, J. Mol. Liq. 96-97, 3-17 (2002). Leonor Saiz and M.L. Klein, Electrostatic interactions in a neutral model phospholipid bilayer by molecular dynamics simulations, J. Chem. Phys. 116, 3052-3057 (2002). Leonor Saiz and M.L. Klein, Influence of highly polyunsaturated lipid acyl chains of biomembranes on the NMR order parameters, J. Am. Chem. Soc. 123, 7381-7387 (2001). Leonor Saiz and M.L. Klein, Structural properties of a highly polyunsaturated lipid bilayer from molecular dynamics simulations, Biophys. J. 81, 204-216 (2001). This work appears also on the cover of the July 2001 issue of the Biophys. J. Leonor Saiz, J. M. G. Vilar, and J. Miguel Rubi, Field-induced force-suppression in ferromagnetic colloids, Physica A 293, 51-58 (2001). Leonor Saiz, J.A. Padro, and E. Guardia, Structure of liquid ethylene glycol: A molecular dynamics simulation study with different force fields, J. Chem. Phys. 114, 3187-3199 (2001). L. Saiz, E. Guardia, and J.A. Padro, Dielectric properties of liquid ethanol. A computer simulation study, J. Chem. Phys. 113, 2814-2822 (2000). L. Saiz, J.A. Padro, and E. Guardia, Dynamics and hydrogen bonding in liquid ethanol, Mol. Phys. 97, 897-905 (1999). J.A. Padro, L. Saiz, and E. Guardia, Hydrogen bonding in liquid alcohols: A computer simulation study, J. Molec. Structure 416, 243-248 (1997). L. Saiz, J.A. Padro, and E. Guardia, Structure and dynamics of liquid ethanol, J. Phys. Chem. B 101, 78-86 (1997). |