how to build a synapse from molecules & membranes (suhita nadkarni)
TRANSCRIPT
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In-silico experiments on small synapses using Monte Carlo simulations
(How to build a synapse from molecules and membranes)
Suhita Nadkarni
Indian Institute of Science Education and Research
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How do synapses work?How does synaptic structure influence synaptic function?
Motivation
•Topology of synaptic and perisynaptic space (neuropil !!!)•Organization of pre- and postsynaptic cytoplasmic organelles•Distributions of receptors, ion channels, enzymes, transporters...•Biochemical reaction networks and their dynamics•Cells are not well mixed & numbers of molecules can be small• Simulations must include stochastic spatiotemporal dynamics
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Scientific Computing EnablesThe Scientific Discovery Cycle
“What I cannot create, I do not understand” -- Richard Feynman
Philosophical Approach: Understanding through simulation
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100nM resting Ca2+ levelPMCA pumps and Na+/Ca2+ exchangerspostsynaptic L & R type VDCCsNR2A and NR2B type NMDA receptors at the PSDAMPA receptors at the PSDDiffusible Calbindin-D28k in the cytosolCalmodulinCAMKIICalcineurinSERCA pumpsMitochondrial Ca2+ pumpspresynaptic P/Q type VDCCspresynaptic vesicular release pathway with SNARE complex
Realistic Model of Synapses in Hippocampal Neuropil
The simulation includes:
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Model Constraints
Realistic synaptic morphology from ssTEM reconstruction of hippocampal neuropil(collaboration with Kristen Harris, UT Austin)
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Mesh Improvement
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Model Constraints
Experimental observations of evoked vesicular release:
Goda & Stevens, PNAS,1994
Dobrunz, Huang & Stevens, PNAS,1997
single stimulus paired-pulse stimulus
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Model ConstraintsExperimental estimates of reaction kinetics and subcellular distributions for important molecules
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Model Constraint: kinetics of calcium sensor in SNARE complex
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MCell: Monte Carlo Simulator of Cellular Microphysiology
•Models realistic 3D reaction/diffusion systems•Rigorously validated and highly optimized stochastic Monte Carlo
methods•Tracks Brownian dynamics diffusion and reaction of individual
particles in 3D volumes and on 2D surfaces embedded in 3D•Arbitrarily complex 3D geometry -- triangle surface meshes•Arbitrarily complex reaction networks -- Markov processes and
Network-free rule-based specification modeled as discrete event-driven point processes
•High-level, flexible Model Description Language•New model building, visualization and analysis environment --
CellBlender
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Diffusion: Brownian Motion
Thermal Velocity:
For Water:
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But, how far do the water molecules go between collisions?
1. Density of water ≅ 1g/cc or 1000g/liter.
2. Molecular weight of water ≅ 18g/mole.
3. From 1 and 2, the molarity of water = 1000/18 = 55.56 moles/liter4. 55.56 x 6.022 x 1023 molecules/mole = 3.3458 * 1025 or 3.3458 * 1022 molecules/cc5. Average volume of each molecule is: 1/ 3.3458 * 1022 ≅ 3.0 * 10-23 cc/molecule
6. Assuming that each molecule corresponds to a spherical space:
Diffusion: Brownian Motion
in 0.3 ps !!!
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Diffusion: Fick’s Second Law
Describes the change of concentration with time resulting from the process of diffusion:
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Free Diffusion: Concentration in Space and Time
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Diffusion: Random Directions
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Grid-Free Random Walk Diffusion
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Surface Mesh Representation
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Diffusion with Boundaries
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Unimolecular Reactions
Distribution of first-order decay lifetimes is:
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Bimolecular Reactions: Rate of Encounter
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Bimolecular Reactions: Volume/Surface
Probability of reaction between diffusing volume molecule and a surface molecule:
From rate of encounter: From Mass Action:
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Bimolecular Reactions: Collision Detection
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Not just 2, but 3 time-scales of release!
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Multi-vesicular release, one vesicle at a time!
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Best fit PPF: ~70 VDCCs @ ~300 nm
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Best fit RR: ~70 VDCCs @ ~300 nm
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One possible role of presynaptic plasticity
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Optimal Simulator Domains
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Future Directions for MCell
•Advanced model-building and visualization environment
• Space-filling molecules -- macromolecular crowding, self-assembly of filaments, scaffolds etc...
•Dynamic cell geometry
•Hybrid FE-FV/Monte Carlo for electrodiffusion, cytomechanics, etc...
•Hybrid SMP/MPI parallelization
•Multiscale simulations spanning molecules to neural circuits -- Blue Brain Project +++
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Acknowledgements
SalkTom BartolTerry Sejnowski
Chuck StevensJay CogganCourtney LopreoreKevin FranksDan KellerJustin KinneyJed WingEugene KimElaine ZhangBen Regner
UCSDMark EllismanMaryann MartoneEduardo EsquenaziStephan Lamont
Herb LevineWouter Jan-Rappel
Darwin Berg
UCSFPeter Sargent
PSCJoel StilesBoris KaminskyJack ChangMarkus Dittrich
Janelia FarmRex Kerr
CaltechMary KennedyTami UrsemMelanie StefanShirley Pepke
UT AustinKristen HarrisChandra Bajaj
NIH, NSF and Wellcome-DBT India Alliance
CornellMika SalpeterEd SalpeterBruce Land