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Molecular mechanisms of long-term memory
Spine
Shaft of Dendrite
Axon
Presynaptic PostsynapticSynapse
PSD
LTP: an increase in synaptic strength
Long-term potentiation (LTP)
Time (mins)0 60
Pos
tsyn
apti
c cu
rren
tLTP protocol induces postynaptic
influx of Ca2+
Bliss and Lomo J Physiol, 1973
LTP: an increase in synaptic strength
Long-term potentiation (LTP)
Time (mins)0 60
Pos
tsyn
apti
c cu
rren
tLTP protocol induces postynaptic
influx of Ca2+
Lledo et al PNAS 1995, Giese et al Science 1998
with CaMKII inhibitor or knockout
Calcium-calmodulin dependent kinase II (CaMKII)
One holoenzyme = 12 subunits
Kolodziej et al. J Biol Chem 2000
Model of bistability in the CaMKII-PP1 system: autocatalytic activation and saturating inactivation.
P0 P1
P1 P2
slow
fast
a) Autophosphorylation of CaMKII (2 rings per holoenzyme):
Lisman and Zhabotinsky, Neuron 2001
E = phosphatase, PP1
b) Dephosphorylation of CaMKII by PP1 (saturating inactivation)
k2
k1
k-1
Total rate of dephosphorylation can never exceed k2.[PP1]
Leads to cooperativity as rate per subunit goes down
Stability in spite of turnover
Bistability in total phosphorylation of CaMKII
0 12NNo. of active subunits
Tot
al r
eact
ion
rate
0
Rate of phosphorylation
Rate of dephosphoryation
[Ca2+]=0.1M (basal level)
Phosphorylation dominates at high calcium
0 12NNo. of active subunits
Tot
al r
eact
ion
rate
0
Rate of phosphorylation
Rate of dephosphoryation
[Ca2+] = 2M (for LTP)
The “Normal” State of Affairs(one stable state, no bistability)
How to get bistability
1) Autocatalysis: k+ increases with [C]2) Saturation: total rate down, (k-)[C], is limited
Reaction pathways
14 configurations of phosphorylated subunits per ring
P0 P1 P2 P3 P4 P5 P6
Phosphorylation to clockwise neighbors
P0 P1 P2 P3 P4 P5 P6
Phosphorylation to clockwise neighbors
P0 P1 P2 P3 P4 P5 P6
Random dephosphorylation by PP1
P0 P1 P2 P3 P4 P5 P6
Random dephosphorylation by PP1
P0 P1 P2 P3 P4 P5 P6
Random turnover included
P0 P1 P2 P3 P4 P5 P6
Stability of DOWN state
= PP1 enzyme
Stability of DOWN state
= PP1 enzyme
Stability of DOWN state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Protein turnover
= PP1 enzyme
Stability of UP state with turnover
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Stability of UP state
= PP1 enzyme
Small numbers of CaMKII holoenzymes in PSD
Petersen et al. J Neurosci 2003
Stochastic implementation of reactions, of rates Ri(t)
using small numbers of molecules via Gillespie's algorithm:
1) Variable time-steps, ∆t: P(∆t) = ∑Ri exp(-∆t ∑R
i)
2) Probability of specific reaction: P(Ri) = R
i/∑R
i
3) Update numbers of molecules according to reaction chosen
4) Update reaction rates using new concentrations
5) Repeat step 1)
Simulation methods
Time (yrs)0
010 20
1
Fra
ctio
n of
sub
unit
s ph
osph
oryl
ated
Pulse of high Ca2+ here
System of 20 holoenzymes undergoes stable LTP
Time (mins)
Fra
ctio
n of
sub
unit
s ph
osph
oryl
ated
Slow transient dynamics revealed
Spontaneous transitions in system with 16 holoenzymes
Time (yrs)
Fra
ctio
n of
sub
unit
s ph
osph
oryl
ated
Spontaneous transitions in system with 4 holoenzymes
Time (days)
Fra
ctio
n of
sub
unit
s ph
osph
oryl
ated
Average lifetime between transitions increases exponentially with system size
Large-N limit, like hopping over a potential barrier
0 12NNo. of active subunits
Rea
ctio
n ra
tes
Eff
ecti
ve p
oten
tial
1) Chemical reactions in biology:x-axis = “reaction coordinate”
= amount of protein phosphorylation
2) Networks of neurons that “fire” action potentials:x-axis = average firing rate of a group of neurons
Why is this important?
Transition between states = loss of memoryTransition times determine memory decay times.
Something like physics
Barrier height depends on area between “rate on” and “rate off” curves, which scales with system size.
Physics analogy: barriers with noise ...
Rate of transition over barrier decreases exponentially with barrier height ... (like thermal physics, with a potential barrier, U and thermal noise energy proportional to kT )
Inherent noise because reactions take place one molecule at a time.
?
General result for memory systems
Time between transitions increases exponentially with scale of the system.
Scale = number of molecules in a biochemical system = number of neurons in a network
Rolling dice analogy: number of rolls needed, each with with probability, p to get N rolls in row, probability is pN
time to wait increases as (1/p)N = exp[N.ln(1/p)]
Change of concentration ratios affects balance between UP and DOWN states.
System of 8 CaMKII holoenzymes:
Time (yrs) Time (yrs)
Pho
spho
ryla
tion
fra
ctio
n
7 PP1 enzymes 9 PP1 enzymes
Number of PP1 enzymes
Ave
rage
life
tim
e of
sta
te
10 yrs
1 yr
1 mth
1 day
Optimal system lifetime is a trade-off between lifetimes of UP and DOWN states
UP state lifetime
DOWN state lifetime
Number of PP1 enzymes
Ave
rage
life
tim
e of
sta
te
10 yrs
1 yr
1 mth
1 day
Optimal system lifetime is a trade-off between lifetimes of UP and DOWN states
UP state lifetime
DOWN state lifetime
Analysis: Separate time-scale for ring switching
Turnover
Preceding a switch down In stable UP state
Time (hrs) Time (hrs)
Tot
al n
o. o
f ac
tive
sub
unit
s
No.
of
acti
ve s
ubun
its,
sin
gle
ring
Turnover
Analysis: Separate time-scale for ring switching
GoalRapid speed-up by converting system to 1D and solving analytically.
MethodEssentially a mean-field theory.
Justification Changes to and from P0 (unphosphorylated state) are slow.
Analysis: Project system to 1D
1) Number of rings “on” with any activation, n.
2) Assume average number, P, of subunits phosphorylated for all rings “on”.
3) Calculate reaction rates for one ring, assuming contibution of others is (n-1)P.
4) Calculate average time in configurations with these reaction rates.
5) Hence calculate new value of P.
6) Repeat Step 2 until convergence.
7) Calculate rate to switch “on”, r+n, and “off”, r-
n.
8) Continue with new value of n.
Analysis: Project system to 1D
1) Number of rings “on” with any activation, n.
2) Assume average number, P, of subunits phosphorylated for all rings “on”.
3) Calculate reaction rates for one ring, assuming contibution of others is (n-1)P.
4) Calculate average time in configurations with these reaction rates.
5) Hence calculate new value of P.
6) Repeat Step 2 until convergence.
7) Calculate rate to switch “on”, r+n, and “off”, r-
n.
8) Continue with new value of n.
Analysis: Project system to 1D
1) Number of rings “on” with any activation, n.
2) Assume average number, P, of subunits phosphorylated for all rings “on”.
3) Calculate reaction rates for one ring, assuming contibution of others is (n-1)P.
4) Calculate average time in configurations with these reaction rates.
5) Hence calculate new value of P.
6) Repeat Step 2 until convergence.
7) Calculate rate to switch “on”, r+n, and “off”, r-
n.
8) Continue with new value of n.
Analysis: Project system to 1D
1) Number of rings “on” with any activation, n.
2) Assume average number, P, of subunits phosphorylated for all rings “on”.
3) Calculate reaction rates for one ring, assuming contibution of others is (n-1)P.
4) Calculate average time in configurations with these reaction rates.
5) Hence calculate new value of P.
6) Repeat Step 2 until convergence.
7) Calculate rate to switch “on”, r+n, and “off”, r-
n.
8) Continue with new value of n.
Analysis: Project system to 1D
1) Number of rings “on” with any activation, n.
2) Assume average number, P, of subunits phosphorylated for all rings “on”.
3) Calculate reaction rates for one ring, assuming contibution of others is (n-1)P.
4) Calculate average time in configurations with these reaction rates.
5) Hence calculate new value of P.
6) Repeat Step 2 until convergence.
7) Calculate rate to switch “on”, r+n, and “off”, r-
n.
8) Continue with new value of n.
Analysis: Project system to 1D
1) Number of rings “on” with any activation, n.
2) Assume average number, P, of subunits phosphorylated for all rings “on”.
3) Calculate reaction rates for one ring, assuming contibution of others is (n-1)P.
4) Calculate average time in configurations with these reaction rates.
5) Hence calculate new value of P.
6) Repeat Step 2 until convergence.
7) Calculate rate to switch “on”, r+n, and “off”, r-
n.
8) Continue with new value of n.
Analysis: Project system to 1D
1) Number of rings “on” with any activation, n.
2) Assume average number, P, of subunits phosphorylated for all rings “on”.
3) Calculate reaction rates for one ring, assuming contibution of others is (n-1)P.
4) Calculate average time in configurations with these reaction rates.
5) Hence calculate new value of P.
6) Repeat Step 2 until convergence.
7) Calculate rate to switch “on”, r+n, and “off”, r-
n.
8) Continue with new value of n.
Analysis: Project system to 1D
1) Number of rings “on” with any activation, n.
2) Assume average number, P, of subunits phosphorylated for all rings “on”.
3) Calculate reaction rates for one ring, assuming contibution of others is (n-1)P.
4) Calculate average time in configurations with these reaction rates.
5) Hence calculate new value of P.
6) Repeat Step 2 until convergence.
7) Calculate rate to switch “on”, r+n, and “off”, r-
n.
8) Continue with new value of n.
Analysis: Solve 1D model exactly
Time to hop from N0 to N
1
Use: r+n T
n = 1 + r-
n+1T
n+1 for N
0 ≤ n < N
1
r+n T
n = r-
n+1T
n+1 for n < N
0
Tn = 0 for n ≥ N
1
Average total time for transition, Ttot
= ∑Tn
N0
N1n n+1n-1 n+2
r-n+1
r+n
r-n+2r-
n
r+n-1 r+
n+1
Number of PP1 enzymes
Ave
rage
life
tim
e of
sta
te
10 yrs
1 yr
1 mth
1 day
Optimal system lifetime is a trade-off between lifetimes of UP and DOWN states
UP state lifetime
DOWN state lifetime
Number of PP1 enzymes
Ave
rage
life
tim
e of
sta
te
10 yrs
1 yr
1 mth
1 day
Optimal system lifetime is a trade-off between lifetimes of UP and DOWN states
UP state lifetime
DOWN state lifetime