Biodynamics 2013
description
Transcript of Biodynamics 2013
A Hybrid Experimental/Modeling Approach to Studying Pituitary Cell
Dynamics
Richard Bertram
Department of Mathematicsand
Programs in Neuroscience and Molecular Biophysics Florida State University, Tallahassee, FL.
Biodynamics 2013
Funding: National Science Foundation DMS1220063
Joël Tabak Patrick Fletcher
Maurizio Tomaiuolo(Univ. Pennsylvania)
Five types of anterior pituitary endocrine cells
1. Lactotrophs: secrete prolactin
2. Somatotrophs: secrete growth hormone
3. Gonadotrophs: secrete luteinizing hormone andfollicle stimulating hormone
4. Corticotrophs: secrete ACTH
5. Thyrotrophs: secrete thyroid stimulating hormone
Goal
Use mathematical modeling and analysis to help understand the electrical activity of the different types of endocrine pituitary cells
Van Goor et al., J. Neurosci., 2001:5902, 2001
Spiking, littlesecretion Bursting,
much moresecretion
What makes pituitary cells burst and secrete?
Equations
voltage
IK activation
cytosolic calcium
IBK activation
Ionic current have an Ohmic form, e.g.,
Conductance and time constants are all parameters.There are more parameters in the equilibrium functions.
Pituitary cells are very heterogeneousThe mix of ionic currents within a single cell type is highly variable, as shown with the GH4C1 cell line…
Cell 1
Cell 2
Cell 3
Why does heterogeneity matter?
One spiking model cell
Another spiking model cell
A third spikingmodel cell
Tomaiuolo et al., Biophys. J., 103:2021, 2012
What we’d like to do…
Parameterize the model to an individual cell,while still patched onto that cell. Then differentcells will have different parameter vectors.
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Time (sec)
V (mV)
period
activesilent
amplitude
min
peaks
Set parameters to fit features of the voltage trace,rather than the voltage trace itself
Also fit voltage clamp data
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I(pA
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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-150
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V(m
V)
time(sec)
Maximize where
Optimization using a genetic algorithm
p1
p2
p1
p2
p1
p2
repeat
selection
replication with mutation
Cost < $ 1000
We need rapid calibration, so use a Programmable Graphics Processing Unit (GPU)
Feature planes can be rapidly computed
100x100 grid10,000 simulations
Simulation time=70 sec eachTotal computation time=20 sec
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time (s)
V (m
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Example: Calibrate, predict, and test
The model (red) is fit to a bursting Gh4 cell (black)
gBK
BK
Peaks per Event
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Feature plane for BK conductance and time constant
Best fit to bursting data Block BK current
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time (s)
V (m
V)
BK current blocked in cell with paxillin
Actual cell (black) and model (red) convert from bursting to spiking
gBK
BK
Peaks per Event
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Feature plane for BK conductance and time constant
Best fit to bursting data Double BK time constant
The Dynamic Clamp: a tool for adding a model current to a real cell
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time (s)
V (m
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BK current added via D-clamp, with τBK=10 ms
Adding BK current with slow activation converts bursting to spiking
Black=cell
Red=model
gBK
BK
Peaks per Event
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Feature plane for BK conductance and time constant
Best fit to bursting data Increase BK conductance
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time (s)
V (m
V)
BK conductance added to a bursting cell
Bursting persists, with more spikes per burst
Black=cell
Red=model
Prediction: Reducing K(dr) conductance increases the burstiness
Diameter: active phase durationColor: number of spikes per active phase
Teka et al., J. Math. Neurosci., 1:12, 2011
Prediction tested using D-clamp
Control cell
Subtract some K(dr) current
Subtract more K(dr)current
Summary
Bertram et al., in press
The “traditional” approach
Our new approach
Thank you