Biyofizikte Davranışsal Modeller
Özet
Referanslar
Schulten K. Lectures in theoretical biophysics. 2000.
Knüpfer C, Beckstein C, Dittrich P, et al. Structure, function, and behaviour of computational models in systems biology. BMC Systems Biology. 2013;7:43. doi:10.1186/1752-0509-7-43.
Ingber DE, Wang N, Stamenović D. Tensegrity, cellular biophysics, and the mechanics of living systems. Reports on Progress in Physics. 2014;77(4):046603.
Lim CT, Zhou EH, Quek ST. Mechanical models for living cells—a review. Journal of Biomechanics. 2006;39(2):195-216.
Alert R, Trepat X. Physical models of collective cell migration. Annual Review of Condensed Matter Physics. 2020;11(1):77-101.
Wilkinson DJ. Stochastic modelling for quantitative description of heterogeneous biological systems. Nature Reviews Genetics. 2009;10(2):122-133.
Eungdamrong NJ, Iyengar R. Computational approaches for modeling regulatory cellular networks. Trends in Cell Biology. 2004;14(12):661-669.
Suresh S. Biomechanics and biophysics of cancer cells. Acta Biomaterialia. 2007;3(4):413-438.
Emon B, Bauer J, Jain Y, Jung B, Saif T. Biophysics of tumor microenvironment and cancer metastasis—a mini review. Computational and Structural Biotechnology Journal. 2018;16:279-287.
Moore D, Walker SI, Levin M. Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem. Convergent Science Physical Oncology. 2017;3(4):043001.
Yankeelov TE. Integrating imaging data into predictive biomathematical and biophysical models of cancer. International Scholarly Research Notices. 2012;2012:287394.
Jarrett AM, Lima EA, Hormuth DA, et al. Mathematical models of tumor cell proliferation: a review of the literature. Expert Review of Anticancer Therapy. 2018;18(12):1271-1286.
Enderling H, Chaplain MAJ. Mathematical modeling of tumor growth and treatment. Current Pharmaceutical Design. 2014;20(30):4934-4940.
Cheng X, Smith JC. Biological membrane organization and cellular signaling. Chemical Reviews. 2019;119(9):5849-5880.
Horwitz R. Cellular biophysics. Biophysical Journal. 2016;110(5):993-996.
Peetla C, Stine A, Labhasetwar V. Biophysical interactions with model lipid membranes: applications in drug discovery and drug delivery. Molecular Pharmaceutics. 2009;6(5):1264-1276.
Marrink SJ, Corradi V, Souza PC, et al. Computational modeling of realistic cell membranes. Chemical Reviews. 2019;119(9):6184-6226.
Chabanon M, Stachowiak JC, Rangamani P. Systems biology of cellular membranes: a convergence with biophysics. Wiley Interdisciplinary Reviews: Systems Biology and Medicine. 2017;9(5):e1386.
Sterratt DC. Goldman-Hodgkin-Katz equations. In: Jaeger D, Jung R, eds. Encyclopedia of Computational Neuroscience. New York, NY: Springer; 2022. p. 1523-1525.
Clay JR. Determining K+ channel activation curves from K+ channel currents often requires the Goldman–Hodgkin–Katz equation. Frontiers in Cellular Neuroscience. 2009;3:20.
Renart A, Machens CK. Variability in neural activity and behavior. Current Opinion in Neurobiology. 2014;25:211-220.
Turner BM, Forstmann BU, Wagenmakers EJ, et al. A Bayesian framework for simultaneously modeling neural and behavioral data. NeuroImage. 2013;72:193-206.
Mueller JK, Tyler WJ. A quantitative overview of biophysical forces impinging on neural function. Physical Biology. 2014;11(5):051001.
Catterall WA, Raman IM, Robinson HP, et al. The Hodgkin-Huxley heritage: from channels to circuits. Journal of Neuroscience. 2012;32(41):14064-14073.
Wang J, Chen L, Fei X. Analysis and control of the bifurcation of Hodgkin–Huxley model. Chaos, Solitons & Fractals. 2007;31(1):247-256.Guckenheimer J, Oliva RA. Chaos in the Hodgkin-Huxley model. SIAM Journal on Applied Dynamical Systems. 2002;1(1):105-114.
Guckenheimer J, Oliva RA. Chaos in the Hodgkin-Huxley model. SIAM Journal on Applied Dynamical Systems. 2002;1(1):105-114.
Xu B, Binczak S, Jacquir S, et al. Parameters analysis of FitzHugh-Nagumo model for a reliable simulation. Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2014:4334-4337.
Izhikevich EM, FitzHugh R. Fitzhugh-Nagumo model. Scholarpedia. 2006;1(9):1349.
Liu YH, Wang XJ. Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. Journal of Computational Neuroscience. 2001;10:25-45.
Lansky P, Sanda P, He J. The parameters of the stochastic leaky integrate-and-fire neuronal model. Journal of Computational Neuroscience. 2006;21:211-223.
Ditlevsen S, Greenwood P. The Morris–Lecar neuron model embeds a leaky integrate-and-fire model. Journal of Mathematical Biology. 2013;67(2):239-259.
Vazquez R. Izhikevich neuron model and its application in pattern recognition. Australian Journal of Intelligent Information Processing Systems. 2010;11(1):35-40.
Teka WW, Upadhyay RK, Mondal A. Spiking and bursting patterns of fractional-order Izhikevich model. Communications in Nonlinear Science and Numerical Simulation. 2018;56:161-176.
Diehl F, White RS, Stein W, et al. Motor circuit-specific burst patterns drive different muscle and behavior patterns. Journal of Neuroscience. 2013;33(29):12013-12029.
Scovil CY, Ronsky JL. Sensitivity of a Hill-based muscle model to perturbations in model parameters. Journal of Biomechanics. 2006;39(11):2055-2063.
Houdijk H, Bobbert MF, De Haan A. Evaluation of a Hill-based muscle model for the energy cost and efficiency of muscular contraction. Journal of Biomechanics. 2006;39(3):536-543.
Siebert T, Stutzig N, Rode C. A Hill-type muscle model expansion accounting for effects of varying transverse muscle load. Journal of Biomechanics. 2018;66:57-62.
Milićević B, Ivanović M, Stojanović B, et al. Huxley muscle model surrogates for high-speed multi-scale simulations of cardiac contraction. Computers in Biology and Medicine. 2022;149:105963.
Negroni JA, Lascano EC. Simulation of steady state and transient cardiac muscle response experiments with a Huxley-based contraction model. Journal of Molecular and Cellular Cardiology
Cifrek M, Medved V, Tonković S, Ostojić S. Surface EMG based muscle fatigue evaluation in biomechanics. Clinical Biomechanics. 2009;24(4):327-340.
Son J, Hwang S, Kim Y. An EMG-based muscle force monitoring system. Journal of Mechanical Science and Technology. 2010;24:2099-2105.
Hayashibe M, Guiraud D, Poignet P. EMG-to-force estimation with full-scale physiology based muscle model. Proceedings of 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2009:1621-1626.
Balasubramanian V, Adalarasu K. EMG-based analysis of change in muscle activity during simulated driving. Journal of Bodywork and Movement Therapies. 2007;11(2):151-158.
Dillon PF. Biophysics: A Physiological Approach. Cambridge: Cambridge University Press; 2012.
McCulloch AD. Systems biophysics: multiscale biophysical modeling of organ systems. Biophysical Journal. 2016;110(5):1023-1027.
Rajendra Acharya U, Paul Joseph K, Kannathal N, Lim CM, Suri JS. Heart rate variability: a review. Medical and Biological Engineering and Computing. 2006;44:1031-1051.
Karim N, Hasan JA, Ali SS. Heart rate variability-a review. Journal of Basic & Applied Sciences. 2011;7(1).
Vanderlei LCM, Pastre CM, Hoshi RA, Carvalho TDD, Godoy MFD. Basic notions of heart rate variability and its clinical applicability. Brazilian Journal of Cardiovascular Surgery. 2009;24:205-217.
Stauss HM. Heart rate variability. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 2003;285(5):R927-R931.
Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Frontiers in Public Health. 2017;5:258.
Wirkner CS, Richter S, Watling L, Thiel L. Circulatory system and respiration. Natural History of Crustacea. 2013;1:376-412.
Zhou H, Lai N, Saidel GM, Cabrera ME. Multiscale modeling of respiration. IEEE Engineering in Medicine and Biology Magazine. 2009;28(3):34-40.
Neelakantan S, Xin Y, Gaver DP, et al. Computational lung modelling in respiratory medicine. Journal of The Royal Society Interface. 2022;19(191):20220062.
Furst B, González‐Alonso J. The heart, a secondary organ in the control of blood circulation. Experimental Physiology. 2023.
Fedoseev VB. Distribution of blood components in the poiseuille flow. Russian Journal of Biomechanics. 2020;24(3):390-397.
Aboelkassem Y, Virag Z. A hybrid Windkessel-Womersley model for blood flow in arteries. Journal of Theoretical Biology. 2019;462:499-513.
Pias SC. Biophysical modelling for insight into oxygen diffusion, distribution, and measurement. International Society on Oxygen Transport to Tissue. 2023:9-14.
Pittman RN. Oxygen transport in the microcirculation and its regulation. Microcirculation. 2013;20(2):117-137.
Palumbo P, Ditlevsen S, Bertuzzi A, De Gaetano A. Mathematical modeling of the glucose–insulin system: a review. Mathematical Biosciences. 2013;244(2):69-81.
Bergman RN. Minimal model: perspective from 2005. Hormone Research. 2006;64(Suppl. 3):8-15.
Andersen KE, Højbjerre M. A Bayesian approach to Bergman’s minimal model. Proceedings of the International Workshop on Artificial Intelligence and Statistics. 2003:1-8.
Turvey MT, Fonseca S. Nature of motor control: perspectives and issues. In: Sternad D, ed. Progress in Motor Control: A Multidisciplinary Perspective. New York: Springer; 2009:93-123.
Bayer A, Schmitt S, Günther M, Haeufle DFB. The influence of biophysical muscle properties on simulating fast human arm movements. Computer Methods in Biomechanics and Biomedical Engineering. 2017;20(8):803-821.
Admiraal MA, Kusters MJ, Gielen SC. Modeling kinematics and dynamics of human arm movements. Motor Control. 2004;8(3):312-338.
Kawato M, Wolpert D. Internal models for motor control. In: Novartis Foundation Symposium 218. Sensory Guidance of Movement. Chichester, UK: John Wiley & Sons, Ltd.; 2007:291-307.
Diedrichsen J, Shadmehr R, Ivry RB. The coordination of movement: optimal feedback control and beyond. Trends in Cognitive Sciences. 2010;14(1):31-39.