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Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments.

Proceedings of the National Academy of Sciences of the United States of America

Noé F, Doose S, Daidone I, Löllmann M, Sauer M, Chodera JD, Smith JC.
PMID: 21368203
Proc Natl Acad Sci U S A. 2011 Mar 22;108(12):4822-7. doi: 10.1073/pnas.1004646108. Epub 2011 Mar 02.

There is a gap between kinetic experiment and simulation in their views of the dynamics of complex biomolecular systems. Whereas experiments typically reveal only a few readily discernible exponential relaxations, simulations often indicate complex multistate behavior. Here, a theoretical...

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Noé F, Doose S, Daidone I, et al. Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments. Proc Natl Acad Sci U S A. 2011;108(12):4822-7doi: 10.1073/pnas.1004646108.
Noé, F., Doose, S., Daidone, I., Löllmann, M., Sauer, M., Chodera, J. D., & Smith, J. C. (2011). Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments. Proceedings of the National Academy of Sciences of the United States of America, 108(12), 4822-7. https://doi.org/10.1073/pnas.1004646108
Noé, Frank, et al. "Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments." Proceedings of the National Academy of Sciences of the United States of America vol. 108,12 (2011): 4822-7. doi: https://doi.org/10.1073/pnas.1004646108
Noé F, Doose S, Daidone I, Löllmann M, Sauer M, Chodera JD, Smith JC. Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments. Proc Natl Acad Sci U S A. 2011 Mar 22;108(12):4822-7. doi: 10.1073/pnas.1004646108. Epub 2011 Mar 02. PMID: 21368203; PMCID: PMC3064371.
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Competition for catalytic resources alters biological network dynamics.

Physical review letters

Rondelez Y.
PMID: 22304295
Phys Rev Lett. 2012 Jan 06;108(1):018102. doi: 10.1103/PhysRevLett.108.018102. Epub 2012 Jan 05.

Genetic regulation networks orchestrate many complex cellular behaviors. Dynamic operations that take place within cells are thus dependent on the gene expression machinery, enabled by powerful enzymes such as polymerases, ribosomes, or nucleases. These generalist enzymes typically process many...

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Rondelez Y. Competition for catalytic resources alters biological network dynamics. Phys Rev Lett. 2012;108(1):018102doi: 10.1103/PhysRevLett.108.018102.
Rondelez, Y. (2012). Competition for catalytic resources alters biological network dynamics. Physical review letters, 108(1), 018102. https://doi.org/10.1103/PhysRevLett.108.018102
Rondelez, Yannick. "Competition for catalytic resources alters biological network dynamics." Physical review letters vol. 108,1 (2012): 018102. doi: https://doi.org/10.1103/PhysRevLett.108.018102
Rondelez Y. Competition for catalytic resources alters biological network dynamics. Phys Rev Lett. 2012 Jan 06;108(1):018102. doi: 10.1103/PhysRevLett.108.018102. Epub 2012 Jan 05. PMID: 22304295.
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Metabolic control theory: a structural approach.

Journal of theoretical biology

Reder C.
PMID: 3267767
J Theor Biol. 1988 Nov 21;135(2):175-201. doi: 10.1016/s0022-5193(88)80073-0.

In the general framework of metabolic control theory, we describe a method of mathematical modelling that provides a way of analysing the sensitivity of a metabolic system to perturbations of the environment or of the internal state of this...

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Reder C. Metabolic control theory: a structural approach. J Theor Biol. 1988;135(2):175-201doi: 10.1016/s0022-5193(88)80073-0.
Reder, C. (1988). Metabolic control theory: a structural approach. Journal of theoretical biology, 135(2), 175-201. https://doi.org/10.1016/s0022-5193(88)80073-0
Reder, C. "Metabolic control theory: a structural approach." Journal of theoretical biology vol. 135,2 (1988): 175-201. doi: https://doi.org/10.1016/s0022-5193(88)80073-0
Reder C. Metabolic control theory: a structural approach. J Theor Biol. 1988 Nov 21;135(2):175-201. doi: 10.1016/s0022-5193(88)80073-0. PMID: 3267767.
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The initial rate of change in distribution volume is the sum of intercompartmental clearances.

Journal of pharmaceutical sciences

Avram MJ, Henthorn TK, Shanks CA, Krejcie TC.
PMID: 3783465
J Pharm Sci. 1986 Sep;75(9):919-20. doi: 10.1002/jps.2600750922.

No abstract available.

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Avram MJ, Henthorn TK, Shanks CA, et al. The initial rate of change in distribution volume is the sum of intercompartmental clearances. J Pharm Sci. 1986;75(9):919-20doi: 10.1002/jps.2600750922.
Avram, M. J., Henthorn, T. K., Shanks, C. A., & Krejcie, T. C. (1986). The initial rate of change in distribution volume is the sum of intercompartmental clearances. Journal of pharmaceutical sciences, 75(9), 919-20. https://doi.org/10.1002/jps.2600750922
Avram, M J, et al. "The initial rate of change in distribution volume is the sum of intercompartmental clearances." Journal of pharmaceutical sciences vol. 75,9 (1986): 919-20. doi: https://doi.org/10.1002/jps.2600750922
Avram MJ, Henthorn TK, Shanks CA, Krejcie TC. The initial rate of change in distribution volume is the sum of intercompartmental clearances. J Pharm Sci. 1986 Sep;75(9):919-20. doi: 10.1002/jps.2600750922. PMID: 3783465.
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Cellular determinants of metabolite concentration ranges.

PLoS computational biology

Küken A, Eloundou-Mbebi JMO, Basler G, Nikoloski Z.
PMID: 30677015
PLoS Comput Biol. 2019 Jan 24;15(1):e1006687. doi: 10.1371/journal.pcbi.1006687. eCollection 2019 Jan.

Cellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component's concentration resides in a given range remain elusive. We present network properties which suffice to...

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Küken A, Eloundou-Mbebi JMO, Basler G, et al. Cellular determinants of metabolite concentration ranges. PLoS Comput Biol. 2019;15(1):e1006687doi: 10.1371/journal.pcbi.1006687.
Küken, A., Eloundou-Mbebi, J. M. O., Basler, G., & Nikoloski, Z. (2019). Cellular determinants of metabolite concentration ranges. PLoS computational biology, 15(1), e1006687. https://doi.org/10.1371/journal.pcbi.1006687
Küken, Anika, et al. "Cellular determinants of metabolite concentration ranges." PLoS computational biology vol. 15,1 (2019): e1006687. doi: https://doi.org/10.1371/journal.pcbi.1006687
Küken A, Eloundou-Mbebi JMO, Basler G, Nikoloski Z. Cellular determinants of metabolite concentration ranges. PLoS Comput Biol. 2019 Jan 24;15(1):e1006687. doi: 10.1371/journal.pcbi.1006687. eCollection 2019 Jan. PMID: 30677015; PMCID: PMC6345444.
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Boltzmann equation modelling of Learning Dynamics: Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

Physics of life reviews

Shizgal B.
PMID: 26830701
Phys Life Rev. 2016 Mar;16:154-5. doi: 10.1016/j.plrev.2016.01.004. Epub 2016 Jan 22.

No abstract available.

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Shizgal B. Boltzmann equation modelling of Learning Dynamics: Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al. Phys Life Rev. 2016;16:154-5doi: 10.1016/j.plrev.2016.01.004.
Shizgal, B. (2016). Boltzmann equation modelling of Learning Dynamics: Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al. Physics of life reviews, 16154-5. https://doi.org/10.1016/j.plrev.2016.01.004
Shizgal, Bernie. "Boltzmann equation modelling of Learning Dynamics: Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al." Physics of life reviews vol. 16 (2016): 154-5. doi: https://doi.org/10.1016/j.plrev.2016.01.004
Shizgal B. Boltzmann equation modelling of Learning Dynamics: Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al. Phys Life Rev. 2016 Mar;16:154-5. doi: 10.1016/j.plrev.2016.01.004. Epub 2016 Jan 22. PMID: 26830701.
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FISIK: Framework for the Inference of In Situ Interaction Kinetics from Single-Molecule Imaging Data.

Biophysical journal

de Oliveira LR, Jaqaman K.
PMID: 31443908
Biophys J. 2019 Sep 17;117(6):1012-1028. doi: 10.1016/j.bpj.2019.07.050. Epub 2019 Aug 06.

Recent experimental and computational developments have been pushing the limits of live-cell single-molecule imaging, enabling the monitoring of intermolecular interactions in their native environment with high spatiotemporal resolution. However, interactions are captured only for the labeled subset of molecules,...

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de Oliveira LR, Jaqaman K. FISIK: Framework for the Inference of In Situ Interaction Kinetics from Single-Molecule Imaging Data. Biophys J. 2019;117(6):1012-1028doi: 10.1016/j.bpj.2019.07.050.
de Oliveira, L. R., & Jaqaman, K. (2019). FISIK: Framework for the Inference of In Situ Interaction Kinetics from Single-Molecule Imaging Data. Biophysical journal, 117(6), 1012-1028. https://doi.org/10.1016/j.bpj.2019.07.050
de Oliveira, Luciana R, and Jaqaman, Khuloud. "FISIK: Framework for the Inference of In Situ Interaction Kinetics from Single-Molecule Imaging Data." Biophysical journal vol. 117,6 (2019): 1012-1028. doi: https://doi.org/10.1016/j.bpj.2019.07.050
de Oliveira LR, Jaqaman K. FISIK: Framework for the Inference of In Situ Interaction Kinetics from Single-Molecule Imaging Data. Biophys J. 2019 Sep 17;117(6):1012-1028. doi: 10.1016/j.bpj.2019.07.050. Epub 2019 Aug 06. PMID: 31443908; PMCID: PMC6818184.
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It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology.

PLoS computational biology

Korsbo N, Jönsson H.
PMID: 32598362
PLoS Comput Biol. 2020 Jun 29;16(6):e1007982. doi: 10.1371/journal.pcbi.1007982. eCollection 2020 Jun.

Thoughtful use of simplifying assumptions is crucial to make systems biology models tractable while still representative of the underlying biology. A useful simplification can elucidate the core dynamics of a system. A poorly chosen assumption can, however, either render...

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Korsbo N, Jönsson H. It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology. PLoS Comput Biol. 2020;16(6):e1007982doi: 10.1371/journal.pcbi.1007982.
Korsbo, N., & Jönsson, H. (2020). It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology. PLoS computational biology, 16(6), e1007982. https://doi.org/10.1371/journal.pcbi.1007982
Korsbo, Niklas, and Jönsson, Henrik. "It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology." PLoS computational biology vol. 16,6 (2020): e1007982. doi: https://doi.org/10.1371/journal.pcbi.1007982
Korsbo N, Jönsson H. It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology. PLoS Comput Biol. 2020 Jun 29;16(6):e1007982. doi: 10.1371/journal.pcbi.1007982. eCollection 2020 Jun. PMID: 32598362; PMCID: PMC7351226.
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The (kinetic) theory of active particles applied to learning dynamics: Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

Physics of life reviews

Nieto J.
PMID: 26830117
Phys Life Rev. 2016 Mar;16:152-3. doi: 10.1016/j.plrev.2016.01.017. Epub 2016 Jan 26.

No abstract available.

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Nieto J. The (kinetic) theory of active particles applied to learning dynamics: Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al. Phys Life Rev. 2016;16:152-3doi: 10.1016/j.plrev.2016.01.017.
Nieto, J. (2016). The (kinetic) theory of active particles applied to learning dynamics: Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al. Physics of life reviews, 16152-3. https://doi.org/10.1016/j.plrev.2016.01.017
Nieto, J. "The (kinetic) theory of active particles applied to learning dynamics: Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al." Physics of life reviews vol. 16 (2016): 152-3. doi: https://doi.org/10.1016/j.plrev.2016.01.017
Nieto J. The (kinetic) theory of active particles applied to learning dynamics: Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al. Phys Life Rev. 2016 Mar;16:152-3. doi: 10.1016/j.plrev.2016.01.017. Epub 2016 Jan 26. PMID: 26830117.
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Finite Element Framework for Computational Fluid Dynamics in FEBio.

Journal of biomechanical engineering

Ateshian GA, Shim JJ, Maas SA, Weiss JA.
PMID: 29238817
J Biomech Eng. 2018 Feb 01;140(2). doi: 10.1115/1.4038716.

The mechanics of biological fluids is an important topic in biomechanics, often requiring the use of computational tools to analyze problems with realistic geometries and material properties. This study describes the formulation and implementation of a finite element framework...

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Ateshian GA, Shim JJ, Maas SA, et al. Finite Element Framework for Computational Fluid Dynamics in FEBio. J Biomech Eng. 2018;140(2)doi: 10.1115/1.4038716.
Ateshian, G. A., Shim, J. J., Maas, S. A., & Weiss, J. A. (2018). Finite Element Framework for Computational Fluid Dynamics in FEBio. Journal of biomechanical engineering, 140(2), . https://doi.org/10.1115/1.4038716
Ateshian, Gerard A, et al. "Finite Element Framework for Computational Fluid Dynamics in FEBio." Journal of biomechanical engineering vol. 140,2 (2018). doi: https://doi.org/10.1115/1.4038716
Ateshian GA, Shim JJ, Maas SA, Weiss JA. Finite Element Framework for Computational Fluid Dynamics in FEBio. J Biomech Eng. 2018 Feb 01;140(2). doi: 10.1115/1.4038716. PMID: 29238817; PMCID: PMC5816258.
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LK-DFBA: a linear programming-based modeling strategy for capturing dynamics and metabolite-dependent regulation in metabolism.

BMC bioinformatics

Dromms RA, Lee JY, Styczynski MP.
PMID: 32122331
BMC Bioinformatics. 2020 Mar 02;21(1):93. doi: 10.1186/s12859-020-3422-0.

BACKGROUND: The systems-scale analysis of cellular metabolites, "metabolomics," provides data ideal for applications in metabolic engineering. However, many of the computational tools for strain design are built around Flux Balance Analysis (FBA), which makes assumptions that preclude direct integration...

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Dromms RA, Lee JY, Styczynski MP. LK-DFBA: a linear programming-based modeling strategy for capturing dynamics and metabolite-dependent regulation in metabolism. BMC Bioinformatics. 2020;21(1):93doi: 10.1186/s12859-020-3422-0.
Dromms, R. A., Lee, J. Y., & Styczynski, M. P. (2020). LK-DFBA: a linear programming-based modeling strategy for capturing dynamics and metabolite-dependent regulation in metabolism. BMC bioinformatics, 21(1), 93. https://doi.org/10.1186/s12859-020-3422-0
Dromms, Robert A, et al. "LK-DFBA: a linear programming-based modeling strategy for capturing dynamics and metabolite-dependent regulation in metabolism." BMC bioinformatics vol. 21,1 (2020): 93. doi: https://doi.org/10.1186/s12859-020-3422-0
Dromms RA, Lee JY, Styczynski MP. LK-DFBA: a linear programming-based modeling strategy for capturing dynamics and metabolite-dependent regulation in metabolism. BMC Bioinformatics. 2020 Mar 02;21(1):93. doi: 10.1186/s12859-020-3422-0. PMID: 32122331; PMCID: PMC7053146.
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Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations.

Metabolic engineering

Hameri T, Fengos G, Ataman M, Miskovic L, Hatzimanikatis V.
PMID: 30455161
Metab Eng. 2019 Mar;52:29-41. doi: 10.1016/j.ymben.2018.10.005. Epub 2018 Oct 26.

Large-scale kinetic models are used for designing, predicting, and understanding the metabolic responses of living cells. Kinetic models are particularly attractive for the biosynthesis of target molecules in cells as they are typically better than other types of models...

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Hameri T, Fengos G, Ataman M, et al. Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations. Metab Eng. 2018;52:29-41doi: 10.1016/j.ymben.2018.10.005.
Hameri, T., Fengos, G., Ataman, M., Miskovic, L., & Hatzimanikatis, V. (2019). Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations. Metabolic engineering, 5229-41. https://doi.org/10.1016/j.ymben.2018.10.005
Hameri, Tuure, et al. "Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations." Metabolic engineering vol. 52 (2019): 29-41. doi: https://doi.org/10.1016/j.ymben.2018.10.005
Hameri T, Fengos G, Ataman M, Miskovic L, Hatzimanikatis V. Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations. Metab Eng. 2019 Mar;52:29-41. doi: 10.1016/j.ymben.2018.10.005. Epub 2018 Oct 26. PMID: 30455161.
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Showing 1 to 12 of 279 entries