全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用：99美元
投递稿件

查看量下载量

相关文章

更多...
PLOS ONE  2013 

A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses

DOI: 10.1371/journal.pone.0068888

Full-Text   Cite this paper   Add to My Lib

Abstract:

Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is therefore an excellent tool for multi-scale simulations.

 References

[1]  Peters A, Palay SL, Webster H deF (1991) The fine structure of the nervous system. Neurons and their supporting cells. 3rd ed. New York: Oxford University Press. 494 p.
[2]  Harris KM, Weinberg RJ (2012) Ultrastructure of Synapses in the Mammalian Brain. Cold Spring Harb Perspect Biol 4: 5. Available: http://cshperspectives.cshlp.org/content？/4/5/a005587. Accessed 2013 June 11.
[3]  Schikorski T, Stevens CF (1997) Quantitative ultrastructural analysis of hippocampal excitatory synapses. J Neurosci 17: 5858–5867.
[4]  Murthy VN, Schikorski T, Stevens CF, Zhu Y (2001) Inactivity produces increases in neurotransmitter release and synapse size. Neuron 32: 673–682.
[5]  Nusser Z, Lujan R, Laube G, Roberts JD, Molnar E, et al. (1998) Cell type and pathway dependence of synaptic AMPA receptor number and variability in the hippocampus. Neuron 21: 545–559.
[6]  Kharazia VN, Weinberg RJ (1999) Immunogold localization of AMPA and NMDA receptors in somatic sensory cortex of albino rat. J Comp Neurol 412: 292–302.
[7]  Takumi Y, Ramírez-León V, Laake P, Rinvik E, Ottersen OP (1999) Different modes of expression of AMPA and NMDA receptors in hippocampal synapses. Nat Neurosci 2: 618–624.
[8]  Tarusawa E, Matsui K, Budisantoso T, Molnár E, Watanabe M, et al. (2009) Input-specific intrasynaptic arrangements of ionotropic glutamate receptors and their impact on postsynaptic responses. J Neurosci 29: 12896–12908.
[9]  Fuxe K, Dahlstr？m A, H？istad M, Marcellino D, Jansson A, et al. (2007) From the Golgi-Cajal mapping to the transmitter-based characterization of the neuronal networks leading to two modes of brain communication: wiring and volume transmission. Brain Res Rev 55: 17–54.
[10]  Syková E, Nicholson C (2008) Diffusion in brain extracellular space. Physiol Rev 88: 1277–1340.
[11]  Rusakov DA, Savtchenko LP, Zheng K, Henley JM (2011) Shaping the synaptic signal: molecular mobility inside and outside the cleft. Trends in Neurosciences 34: 359–369.
[12]  Boucher J, Kroger H, Sik A (2010) Realistic modelling of receptor activation in hippocampal excitatory synapses: analysis of multivesicular release, release location, temperature and synaptic cross-talk. Brain Structure & Function 215: 49–65.
[13]  Renner M, Domanov Y, Sandrin F, Izeddin I, Bassereau P, et al. (2011) Lateral diffusion on tubular membranes: quantification of measurements bias. PLoS ONE 6: e25731.
[14]  Shrivastava IH, Sansom MSP (2000) Simulations of Ion Permeation Through a Potassium Channel: Molecular Dynamics of KcsA in a Phospholipid Bilayer. Biophysical Journal 78(2): 557–570.
[15]  Stiles JR, Bartol TM (2001) Monte Carlo Methods for Simulating Realistic Synaptic Microphysiology Using MCell. Computational Neuroscience: Realistic Modeling for Experimentalists: 87–127.
[16]  Kerr RA, Bartol TM, Kaminsky B, Dittrich M, Chang J-CJ, et al. (2008) Fast Monte Carlo Simulation Methods for Biological Reaction-Diffusion Systems in Solution and on Surfaces. SIAM J Sci Comput 30: 3126–3149.
[17]  Plimpton SJ, Slepoy A (2005) Microbial cell modeling via reacting diffusive particles. Journal of Physics Conference Series 16: 305–309.
[18]  Andrews SS, Bray D (2004) Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Physical Biology 1: 137–151.
[19]  Andrews SS, Addy NJ, Brent R, Arkin AP (2010) Detailed Simulations of Cell Biology with Smoldyn 2.1. PLoS Computational Biology 6: 10.
[20]  Carnevale NT, Hines ML (2006) The NEURON Book. Cambridge University Press. 480 p.
[21]  Hines ML, Carnevale NT (2001) NEURON: a tool for neuroscientists. Neuroscientist 7: 123–135.
[22]  Bower JM, Beeman D (1998) The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System. 2nd ed. Springer. 458 p.
[23]  Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, et al. (2007) Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience 23: 349–398.
[24]  Weinan E, Engquist B (2003) Multiscale modeling and computation. Notices Amer Math Soc 50: 1062–1070.
[25]  Drasdo D, Ramis-Conde I, Byrne H, Radszuweit M, Krinner A, et al.. (2011) Multi-scale modeling of cells: concepts and open questions. Proceedings of the 8th European Conference on Mathematical and Theoretical Biology. Kraków. Available: http://www.impan.pl/~ecmtb11/showabstrac？t.php?id=Drasdo_Dirk2. Accessed 11 June 2013.
[26]  Morra G, Meli M, Colombo G (2008) Molecular dynamics simulations of proteins and peptides: from folding to drug design. Current protein peptide science 9: 181–196.
[27]  Bouteiller JMC, Allam SL, Hu EY, Greget R, Ambert N, et al. (2011) Integrated Multiscale Modeling of the Nervous System: Predicting Changes in Hippocampal Network Activity by a Positive AMPA Receptor Modulator. IEEE Transactions on Biomedical Engineering 58: 3008–3011.
[28]  Praprotnik M, Delle L (2013) Multiscale molecular modeling. Methods Mol. Biol. 924: 567–583.
[29]  Savtchenko LP, Rusakov DA (2007) The optimal height of the synaptic cleft. Proc Natl Acad Sci USA 104: 1823–1828.
[30]  el-Kareh AW, Braunstein SL, Secomb TW (1993) Effect of cell arrangement and interstitial volume fraction on the diffusivity of monoclonal antibodies in tissue. Biophysical Journal 64: 1638–1646.
[31]  Lehre KP, Danbolt NC (1998) The number of glutamate transporter subtype molecules at glutamatergic synapses: chemical and stereological quantification in young adult rat brain. J Neurosci 18: 8751–8757.
[32]  Franks KM, Bartol TM, Sejnowski TJ (2002) A Monte Carlo model reveals independent signaling at central glutamatergic synapses. Biophys J 83: 2333–2348.
[33]  Nicholson C, Syková E (1998) Extracellular space structure revealed by diffusion analysis. Trends in Neurosciences 21: 207–215.
[34]  Thorne RG, Nicholson C (2006) In vivo diffusion analysis with quantum dots and dextrans predicts the width of brain extracellular space. Proceedings of the National Academy of Sciences of the United States of America 103: 5567–5572.
[35]  Stiles JR, Bartol TM (2013) Mcell: A montecarlo simulator of cellular microphysiology. Available: http://www.mcell.cnl.salk.edu/. Accessed 2013 June 11.
[36]  Bruns D, Jahn R (1995) Real-time measurement of transmitter release from single synaptic vesicles. Nature 377: 62–65.
[37]  Atasoy D, Ertunc M, Moulder KL, Blackwell J, Chung C, et al. (2008) Spontaneous and evoked glutamate release activates two populations of NMDA receptors with limited overlap. J Neurosci 28: 10151–10166.
[38]  Allam SL, Ghaderi VS, Bouteiller JMC, Legendre A, Ambert N, et al. (2012) A Computational Model to Investigate Astrocytic Glutamate Uptake Influence on Synaptic Transmission and Neuronal Spiking. Front Comput Neurosci 6: 1–16.
[39]  Jonas P, Major G, Sakmann B (1993) Quantal components of unitary EPSCs at the mossy fibre synapse on CA3 pyramidal cells of rat hippocampus. J Physiol (Lond) 472: 615–663.
[40]  CeSViMa – Centro de Supercomputación y Visualización de Madrid (2013). Available: http://www.cesvima.upm.es/.Accessed 2013 June 11.
[41]  Rusakov DA, Kullmann DM (1998) Extrasynaptic glutamate diffusion in the hippocampus: ultrastructural constraints, uptake, and receptor activation. J Neurosci 18: 3158–3170.
[42]  Zheng K, Scimemi A, Rusakov DA (2008) Receptor actions of synaptically released glutamate: the role of transporters on the scale from nanometers to microns. Biophys J 95: 4584–4596.
[43]  Momiyama A, Silver R, Hausser M, Notomi T, Wu Y, et al. (2003) The density of AMPA receptors activated by a transmitter quantum at the climbing fibre-Purkinje cell synapse in immature rats. The Journal of Physiology 549: 75–92.
[44]  MATLAB – The Language of Technical Computing (2013). Available: http://www.mathworks.com/products/matlab.Accessed 2013 June 11.
[45]  Lindley DV (1987) Regression and correlation analysis. New Palgrave: A Dictionary of Economics 4: 120–123.
[46]  Witten IH, Frank E, Hall MA (2011) Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. 664 p.
[47]  Dasarathy BV (1991) Nearest neighbor (NN) norms: NN pattern classification techniques. Los Alamitos, CA: IEEE Computer Society Press. 435 p.
[48]  Haykin SS (1999) Neural Networks: A Comprehensive Foundation. New York: Prentice Hall International. 842 p.
[49]  Quinlan JR (1992) Learning with continuous classes. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence: 343–348.
[50]  Wang Y, Witten IH (1996) Induction of model trees for predicting continuous classes. Poster papers of the 9th European Conference on Machine Learning. Available: http://researchcommons.waikato.ac.nz/han？dle/10289/1183. Accessed 2013 June 11.
[51]  Friedman JH (1991) Multivariate Adaptive Regression Splines. Annals of Statistics 19(1): 1–67.
[52]  Friedman JH, Stuetzle W (1981) Projection pursuit regression. Journal of the American Statistical Association 76: 817–823.
[53]  Wolpert DH (1996) The lack of a priori distinctions between learning algorithms. Neural Comput 8: 1341–1390.
[54]  The R Project for Statistical Computing (2013). Available: http://www.r-project.org/.Accessed 2013 June 11.
[55]  Rusakov DA, Kullmann DM (1998) Extrasynaptic glutamate diffusion in the hippocampus: ultrastructural constraints, uptake, and receptor activation. J Neurosci 18: 3158–3170.
[56]  Nielsen TA, DiGregorio DA, Silver RA (2004) Modulation of glutamate mobility reveals the mechanism underlying slow-rising AMPAR EPSCs and the diffusion coefficient in the synaptic cleft. Neuron 42: 757–771.
[57]  Savtchenko LP, Sylantyev S, Rusakov DA (2013) Central synapses release a resource-efficient amount of glutamate. Nat Neurosci 16: 10–12.
[58]  Alonso-Nanclares L, Gonzalez-Soriano J, Rodriguez JR, DeFelipe J (2008) Gender differences in human cortical synaptic density. Proceedings of the National Academy of Sciences of the United States of America 105: 14615–14619.
[59]  Schnell S, Grima R, Maini PK (2010) Multiscale modeling in biology. American Scientist 95: 134.
[60]  Chaplain MAJ (2011) Multiscale mathematical modelling in biology and medicine. IMA Journal of Applied Mathematics 76: 371–388.
[61]  Southern J, Pitt-Francis J, Whiteley J, Stokeley D, Kobashi H, et al. (2008) Multi-scale computational modelling in biology and physiology. Progress in Biophysics and Molecular Biology 96: 60–89.
[62]  Lu G, Kaxiras E (2004) An Overview of Multiscale Simulations of Materials. Available: http://arxiv.org/abs/cond-mat/0401073. Accessed 2013 June 11.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133