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Stochastic Models for Chemical Reactions
Specifying Markov chains
• Specifying infinitesimal behavior
• Chemical reactions
• Poisson processes
• Counting processes
• Markov chains
• Martingale properties
• Master equation
• Equivalence of formulations
• Simulation
• Classical scaling
• Central limit theorem
• Diffusion approximation
Multiscale limits
• Multiscale models
• Time scales
• Model of viral infection
• Enzyme reaction
• Michaelis-Menten equation
• Central limit theorem
• Diffusion approximation
• Heat shock model
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See References, in particular Anderson and Kurtz (2015).
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Specifying infinitesimal behaviorAn ordinary differential equation is specified by describing how afunction should vary over a small period of time
X(t+ ∆t)−X(t) ≈ F (X(t))∆t
A more precise description (consider a telescoping sum)
X(t) = X(0) +
∫ t
0
F (X(s))ds
The first fundamental question: Does this infinitesimal descriptionuniquely determine the function? (Of course, we must specify X(0)along with F .)
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Infinitesimal behavior for jump processes
We are interested in functions that are piecewise constant and ran-dom. Changes, when they occur, won’t be small
X(t+ ∆t)−X(t) ≈ ζ
What is small?
P{X(t+ ∆t)−X(t) = ζ|Ft} ≈ λζ(t)∆t
The probability of seeing a jump of a particular size.
Can we specify the λζ in some way that determines X? For the ODE,F depended on X . Maybe λζ should depend on X .
P{X(t+ ∆t)−X(t) = ζ|Ft} ≈ λζ(X(t))∆t
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Chemical networksModeling counts of individual chemical species: X(t) will be the vec-tor whose components give the numbers of molecules of each chem-ical species.
For a binary reaction S1 + S2 ⇀ S3 or S1 + S2 ⇀ S3 + S4
λk(x) = κ′kx1x2 ζk =
−1−110
or
−1−111
For S1 ⇀ S2 or S1 ⇀ S2 + S3,
λk(x) = κ′kx1.
For 2S1 ⇀ S2,
λk(x) = κ′kx1(x1 − 1) ζk =
(−21
)
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Poisson processesSimplest case:
Only one possible jump: ζ = 1
λ is constant
P{Yλ(t+ ∆t)− Yλ(t) = 1|FYλt } ≈ λ∆t
A more precise description:
• Yλ is constant except for jumps of +1.
• Disjoint increments of Yλ are independent (λ is not random).
• The distribution of Yλ(t+ a)−Yλ(t) does not depend on t (λ doesnot depend on t).
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Characterization of the Poisson process
These assumptions imply
P{Yλ(t+ a)− Yλ(t) = k} = e−λa(λa)k
k!,
soP{Yλ(t+ ∆t)− Yλ(t) = 1|FYλ
t } = λ∆te−λ∆t ≈ λ∆t.
Note that if Y = Y1 is a Poisson process with parameter 1, thenYλ(t) ≡ Y (λt) is a Poisson process with parameter λ. So, λ is a rate.
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Counting processesNext simplest case:
Only one possible jump ζ = 1.
P{X(t + ∆t) − X(t) = 1|Ft} ≈ λ(t)∆t where λ depends on t and israndom. λ is called the intensity of X .
To specify a model, specify λ as a function of X and perhaps anotherstochastic process ξ (e.g. λ(t) = β(X(t), ξ(t)).
To make this more precise (take X(0) = 0)
X(t) = Y (
∫ t
0
β(X(s), ξ(s))ds)
where Y is a unit Poisson process.
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Markov chainsA process X is Markov if
E[f(X(t+ a))|FXt ] = E[f(X(t+ a))|X(t)], t, a ≥ 0.
Infinitesimal specification of a Markov chain:
P{X(t+ ∆t)−X(t) = ζk|FXt } ≈ βk(X(t))∆t
(For simplicity assume only finitely many possible jumps ζk.)
X(t) = X(0) +∑k
Rk(t)ζk,
Rk a counting process counting the times X takes jump ζk.
P{Rk(t+ ∆t)−Rk(t) = 1|FXt } ≈ βk(X(t))∆t
so, for independent unit Poisson processes Yk,
X(t) = X(0) +∑k
Yk(
∫ t
0
βk(X(s))ds)ζk
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Enzyme reaction
A+ E AE ⇀ E +B
XE(t) = XE(0)− Y1(
∫ t
0
κ1XA(s)XE(s)ds) + Y2(κ2
∫ t
0
XAE(s)ds)
+Y3(κ3
∫ t
0
XAE(s)ds)
XA(t) = XA(0)− Y1(
∫ t
0
κ1XA(s)XE(s)ds)
+Y2(κ2
∫ t
0
XAE(s)ds)
The remainder of the system is determined by
XAE(t) = M−XE(t) XB(t) = XB(0)+XA(0)+XAE(0)−XA(t)−XAE(t)
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Martingale properties
X(t) = X(0) +∑k
Rk(t)ζk = X(0) +∑k
Yk(
∫ t
0
βk(X(s))ds)ζk
Under moment conditions, setting Yk(u) = Yk(u)− u
Rk(t) ≡ Rk(t)−∫ t
0
βk(X(s))ds = Yk(
∫ t
0
βk(X(s))ds)
is a martingale. In particular E[Rk(t)] = 0.
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Martingale propertiesSince
f(X(t))− f(X(0)) =∑k
∫ t
0
(f(X(s−) + ζk)− f(X(s−))dRk(s),
defining Af(x) =∑
k βk(x)(f(x+ ζk)− f(x)),
f(X(t))− f(X(0))−∫ t
0
Af(X(s))ds
=∑k
∫ t
0
(f(X(s−) + ζk)− f(X(s−))dRk(s)
is a martingale. In particular
E[f(X(t))− f(X(0))−∫ t
0
Af(X(s))ds] = 0,
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Master equationSince
E[f(X(t))− f(X(0))−∫ t
0
Af(X(s))ds] = 0,
letting f(x) = 1{m}(x), E[f(X(t))] = P{X(t) = m} ≡ pm(t) satisfies
pm(t) = pm(0) +
∫ t
0
(∑k
βk(m− ζk)pm−ζk(s)− (∑k
βk(m))pm(s)
)giving the Kolmogorov forward or master equation.
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Equivalence of formulationsWe now have three ways of making the infinitesimal specification
P{X(t+ ∆t)−X(t) = ζk|FXt } ≈ βk(X(t))∆t
precise:
1. The stochastic equation: X(t) = X(0) +∑
k Yk(∫ t
0 βk(X(s))ds)ζk
2. The requirement that f(X(t)) − f(X(0)) −∫ t
0 Af(X(s))ds be amartingale for Af(x) =
∑k βk(x)(f(x+ ζk)− f(x))
3. The master equation for the one-dimensional distributions:
pm(t) = pm(0)+
∫ t
0
(∑k
βk(m− ζk)pm−ζk(s)− (∑k
βk(m))pm(s)
)Fortunately, if the solution of the stochastic equation doesn’t blowup, the three are equivalent.
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Holding timesIf X(t) = x, then assuming that none of the other reactions fire, thatis none of the Rl jump before before Rk, the time τk(t) until the nextjump of
Rk(t) = Yk(
∫ t
0
βk(X(s))ds)
is exponentially distributed
P{τk(t) > s|X(t) = x} = e−βk(x)s.
The time of the next jump is τ(t) = mink τk(t) so
P{τ(t) > s|X(t) = x} =∏k
P{τk(t) > s|X(t) = x} = e−∑βk(x)s
The calculation implies the length of time X stays in a state x (theholding time in x) before jumping is exponentially distributed withparameter
∑k βk(x).
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Jump distribution
A similar calculations gives
P{τk(t) = τ(t)|X(t) = x} =βk(x)∑l βl(x)
.
Note that this expression does not depend on the value of τ(t).
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Gillespie stochastic simulation algorithm Gillespie (1976,1977)
Suppose X(0) = Y0. Generate an expontial random variable ∆0 withparameter
∑k βk(Y0).
∆0 = − 1∑k βk(Y0)
logU01
where U01 is uniform [0, 1].
Suppose the βk are β1, . . . , βm and define γ0 = 0 and
γl(y) =
∑lk=1 βk(y)∑mk=1 βk(y)
With an independent uniform [0, 1], U02, generate the next state by
X(∆0) = Y1 =m∑l=1
1(γl−1(Y0),γl(Y0)](U02)(Y0 + ζl).
Repeat.
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Algorithms suggested by the stochastic equationSimulating the unit Poisson processes and simply solving the time-change equation
X(t) = X(0) +∑k
Yk(
∫ t
0
βk(X(s))ds)ζk
gives the next reaction (next jump) method as defined by Gibson andBruck (2000). (See also Anderson (2007).)
Doing an Euler type approximation for the integrals gives the τ -leapmethod of Gillespie (2001). Compare
X(tm) = X(0) +m∑i=1
F (X(ti−1))(ti − ti−1)
to
X(tm) = X(0) +∑k
Yk(m∑i=1
βk(X(ti−1))(ti − ti−1))ζk
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Properties of Poisson process
Law of large numbers
Y (Nu)
N≈ u lim
N→∞
Y (Nu)
N= u
Central limit theorem
Y (Nu)−Nu√N
=Y (Nu)√
N≈ W (u)
Y (Nu)−Nu√N
⇒ W (u)
W a standard Brownian motion.
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Reaction networksWe consider a network of reactions involving s0 chemical species,S1, . . . , Ss0.
s0∑i=1
νikSi ⇀
s0∑i=1
ν ′ikSi
where the νik and ν ′ik are nonnegative integers.
νk the vector whose ith element is νik.
ν ′k the vector whose ith element is ν ′ik.
ζk = ν ′k − νk.
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Equations for the system state
The state of the system satisfies
X(t) = X(0) +∑k
Rk(t)(ν′k − νk)
= X(0) +∑k
Yk(
∫ t
0
λk(X(s))ds)ζk
For a binary reaction S1 + S2 ⇀ S3 or S1 + S2 ⇀ S3 + S4
λk(x) = κ′kx1x2
For S1 ⇀ S2 or S1 ⇀ S2 + S3,
λk(x) = κ′kx1.
For 2S1 ⇀ S2,λk(x) = κ′kx1(x1 − 1).
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Classical scalingBasic assumption: Binary reaction rate constants scale inversely withsome measure of the volume: κ′k = κkN
−10 .
N0 Avogadro’s number times the volume in liters
Ci(t) = N−10 Xi(t) is the chemical concentration and
κ′kxi = N0κkci, κ′kxixj = N0κkcicj.
so
Ci(t) = Ci(0) +∑k
N−10 Yk(N0
∫ t
0
λk(C(s))ds)(ν ′ki − νki).
Applying the law of large numbers, the concentrations can be ap-proximated by the solution of
Ci(t) = Ci(0) +∑k
∫ t
0
λk(C(s))dsζki.
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Scaling argumentsConsider a sequence of equations
CNi (t) = CN
i (0) +∑k
N−1Yk(N
∫ t
0
λk(CN(s))ds)ζki.
CN0 is the original model.
N0 is large, so CN0 might reasonably be approximated by
Ci = limN→∞
CNi
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Central limit theorem/Van Kampen ApproximationFor F (c) =
∑k λk(c)ζk where ζk = ν ′k − νk
V N(t) ≡√N(CN(t)− C(t))
≈ V N(0) +√N(∑
k
1
NYk(N
∫ t
0
λk(CN(s))ds)(ν ′k − νk)
−∫ t
0
F (C(s))ds))
= V N(0) +∑k
1√NYk(N
∫ t
0
λk(CN(s))ds)(ν ′k − νk)
+
∫ t
0
√N(F (CN(s))− F (C(s)))ds
≈ V N(0) +∑k
Wk(
∫ t
0
λk(CN(s))ds)(ν ′k − νk)
+
∫ t
0
∇F (C(s)))V N(s)ds
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Gaussian limit
V N converges to the solution of
V (t) = V (0) +∑k
Wk(
∫ t
0
λk(C(s))ds)(ν ′k− νk) +
∫ t
0
∇F (C(s)))V (s)ds
CN(t) ≈ C(t) +1√NV (t)
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Diffusion approximation
CN(t) = CN(0) +∑k
N−1Yk(N
∫ t
0
λk(CN(s))ds)(ν ′k − νk)
≈ CN(0) +∑k
N−1/2Wk(
∫ t
0
λk(CN(s))ds)(ν ′k − νk)
+
∫ t
0
F (CN(s))ds,
whereF (c) =
∑k
λk(c)(ν′k − νk).
The diffusion approximation is given by the equation
CN(t) = CN(0)+∑k
N−1/2Wk(
∫ t
0
λk(CN(s))ds)(ν ′k−νk)+
∫ t
0
F (CN(s))ds.
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Ito formulation
The time-change formulation is equivalent to the Ito equation
CN(t) = CN(0) +∑k
N−1/2
∫ t
0
√λk(CN(s))dWk(s)(ν
′k − νk)
+
∫ t
0
F (CN(s))ds
= CN(0) +∑k
N−1/2
∫ t
0
σ(CN(s))dW (s) +
∫ t
0
F (CN(s))ds,
where σ(c) is the matrix with columns√λk(c)(ν
′k − νk).
See Kurtz (1977/78), Ethier and Kurtz (1986), Chapter 10, Gardiner(2004), Chapter 7, and van Kampen (1981).
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Multiscale limits
• Multiscale models
• Time scales
• Model of viral infection
• Enzyme reaction
• Michaelis-Menten equation
• Central limit theorem
• Diffusion approximation
• Heat shock model
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Multiscale models Kang and Kurtz (2013)
Fix N0 >> 1. For each species i, define the normalized abundances (orsimply, the abundances) by
Zi(t) = N−αi0 Xi(t),
where αi ≥ 0 should be selected so that Zi = O(1). Note that theabundance may be the species number (αi = 0) or the species con-centration or something else.
The rate constants may also vary over several orders of magnitudeso scale the rate constants κ′k = κkN
βk0 so that κk = O(1).
Then
κ′kxixj = Nβk+αi+αj0 κkzizj κ′kxi(xi − 1) = Nβk+2αi
0 κkzi(zi −N−αi0 )
κ′kxi = κkNβk+αi0 zi
Note that the exponent on N0 is ρk = βk + α · νk.
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A parameterized family of models
Then, noting that νk · α =∑
i νikαi,
Zi(t) = Zi(0) +∑k
N−αi0 Yk(
∫ t
0
Nβk+νk·α0 λk(Z(s))ds)(ν ′ik − νik).
Let
ZNi (t) = Zi(0) +
∑k
N−αiYk(
∫ t
0
Nβk+νk·αλk(ZN(s))ds)(ν ′ik − νik).
Then the “true” model is Z = ZN0.
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Time-scale parameter
Let
ZN,γi (t) ≡ ZN
i (tNγ)
= Zi(0) +∑k
N−αiYk(
∫ t
0
Nγ+βk+νk·αλk(ZN,γ(s))ds)ζik.
Equation is “balanced” if
max{βk + νk · α : ζik > 0} = max{βk + νk · α : ζik < 0}
If the equation is not balanced then we need
γ + βk + νk · α ≤ αi (1)
for all i and k such that ζik 6= 0.
The time-scale of species i: γi = αi −max{βk + νk · α : ζik 6= 0}
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Example: Model of a viral infectionSrivastava, You, Summers, and Yin (2002), Haseltine and Rawlings(2002), Ball, Kurtz, Popovic, and Rempala (2006)
Three time-varying species, the viral template, the viral genome, andthe viral structural protein (indexed, 1, 2, 3 respectively).
The model involves six reactions,
S1 + stuffκ′1⇀ S1 + S2
S2κ′2⇀ S1
S1 + stuffκ′3⇀ S1 + S3
S1κ′4⇀ ∅
S3κ′5⇀ ∅
S2 + S3κ′6⇀ S4
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Stochastic system
X1(t) = X1(0) + Y2(
∫ t
0
κ′2X2(s)ds)− Y4(
∫ t
0
κ′4X1(s)ds)
X2(t) = X2(0) + Y1(
∫ t
0
κ′1X1(s)ds)− Y2(
∫ t
0
κ′2X2(s)ds)
−Y6(
∫ t
0
κ′6X2(s)X3(s)ds)
X3(t) = X3(0) + Y3(
∫ t
0
κ′3X1(s)ds)− Y5(
∫ t
0
κ′5X3(s)ds)
−Y6(
∫ t
0
κ′6X2(s)X3(s)ds)
κ′1 1 κ′4 0.25κ′2 0.025 κ′5 2κ′3 1000 κ′6 7.5× 10−6
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Figure 1: Simulation (Haseltine and Rawlings 2002)
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Balance equations for the viral model
ZN1 (t) = Z1(0) +N−α1Y2(
∫ t
0
κ2Nβ2+α2ZN
2 (s)ds)−N−α1Y4(
∫ t
0
κ4Nβ4+α1ZN
1 (s)ds)
ZN2 (t) = Z2(0) +N−α2Y1(
∫ t
0
κ1Nβ1+α1ZN
1 (s)ds)−N−α2Y2(
∫ t
0
κ2Nβ2+α2ZN
2 (s)ds)
−N−α2Y6(
∫ t
0
κ6Nβ6+α2+α3ZN
2 (s)ZN3 (s)ds)
ZN3 (t) = Z3(0) +N−α3Y3(
∫ t
0
κ3Nβ3+α1ZN
1 (s)ds)−N−α3Y5(
∫ t
0
κ5Nβ5+α3ZN
3 (s)ds)
−N−α3Y6(
∫ t
0
κ6Nβ6+α2+α3ZN
2 (s)ZN3 (s)ds)
β2 + α2 = β4 + α1
β1 + α1 = (β2 + α2) ∨ (β6 + α2 + α3)
β3 + α1 = (β5 + α3) ∨ (β6 + α2 + α3)
β3 ≥ β5 ≥ β1 ≥ β4 ≥ β2 ≥ β6
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An example
β2 + α2 = β4 + α1
β1 + α1 = (β2 + α2) ∨ (β6 + α2 + α3)
β3 + α1 = (β5 + α3) ∨ (β6 + α2 + α3)
β3 ≥ β5 ≥ β1 ≥ β4 ≥ β2 ≥ β6
β1 0 α1 0β2 −2
3 α223
β3 1 α3 1β4 0 γ1 0β5 0 γ2
23
β6 −53 γ3 0
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Scaling parameters Ball, Kurtz, Popovic, and Rempala (2006)
Each Xi is scaled according to its abundance in the system.
For N0 = 1000, X1 = O(N 00 ), X2 = O(N
2/30 ), and X3 = O(N0) and we
take Z1 = X1, Z2 = X2N−2/30 , and Z3 = X3N
−10 .
Expressing the rate constants in terms of N0 = 1000
κ′1 1 1
κ′2 0.025 2.5N−2/30
κ′3 1000 N0
κ′4 0.25 .25κ′5 2 2
κ′6 7.5× 10−6 .75N−5/30
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Normalized system
With the scaled rate constants and abundances, we have
ZN1 (t) = ZN
1 (0) + Y2(
∫ t
0
2.5ZN2 (s)ds)− Y4(
∫ t
0
.25ZN1 (s)ds)
ZN2 (t) = ZN
2 (0) +N−2/3Y1(
∫ t
0
ZN1 (s)ds)−N−2/3Y2(
∫ t
0
2.5ZN2 (s)ds)
−N−2/3Y6(
∫ t
0
.75ZN2 (s)ZN
3 (s)ds)
ZN3 (t) = ZN
3 (0) +N−1Y3(
∫ t
0
NZN1 (s)ds)−N−1Y5(
∫ t
0
2NZN3 (s)ds)
−N−1Y6(
∫ t
0
.75ZN2 (s)ZN
3 (s)ds),
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Limiting system
Passing to the limit, we have
Z1(t) = Z1(0) + Y2(
∫ t
0
2.5Z2(s)ds)− Y4(
∫ t
0
.25Z1(s)ds)
Z2(t) = Z2(0)
Z3(t) = Z3(0) +
∫ t
0
Z1(s)ds−∫ t
0
2Z3(s)ds
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Fast time-scaleDefine ZN,γ
i (t) = ZNi (Nγt). For γ = 2
3 ,
ZN,γ1 (t) = Z1(0) + Y2(
∫ t
0
2.5N 2/3ZN,γ2 (s)ds)− Y4(
∫ t
0
.25N 2/3ZN,γ1 (s)ds)
ZN,γ2 (t) = Z2(0) +N−2/3Y1(
∫ t
0
N 2/3ZN,γ1 (s)ds)
−N−2/3Y2(
∫ t
0
2.5N 2/3ZN,γ2 (s)ds)
−N−2/3Y6(N2/3
∫ t
0
.75ZN,γ2 (s)ZN,γ
3 (s)ds)
ZN,γ3 (t) = Z3(0) +N−1Y3(
∫ t
0
N 5/3ZN,γ1 (s)ds)−N−1Y5(
∫ t
0
2N 5/3ZN,γ3 (s)ds)
−N−1Y6(
∫ t
0
.75N 2/3ZN,γ2 (s)ZN,γ
3 (s)ds)
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Averaging
As N → ∞, dividing the equations for ZN,γ1 and ZN,γ
3 by N 2/3 showsthat ∫ t
0
ZN,γ1 (s)ds− 10
∫ t
0
ZN,γ2 (s)ds→ 0∫ t
0
ZN,γ3 (s)ds− 5
∫ t
0
ZN,γ2 (s)ds→ 0.
The assertion for ZN,γ3 and the fact that ZN,γ
2 is asymptotically regularimply ∫ t
0
ZN,γ2 (s)ZN,γ
3 (s)ds− 5
∫ t
0
ZN,γ2 (s)2ds→ 0.
It follows that ZN,γ2 converges to the solution of (2).
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Law of large numbers
Theorem 1 Let γ = 23 . For each δ > 0 and t > 0,
limN→∞
P{ sup0≤s≤t
|ZN,γ2 (s)− Z∞,γ2 (s)| ≥ δ} = 0,
where Z∞,γ2 is the solution of
Z∞,γ2 (t) = Z2(0) +
∫ t
0
7.5Z∞,γ2 (s)ds)−∫ t
0
3.75Z∞,γ2 (s)2ds. (2)
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Approximate modelsWe have a family of models indexed by N for which N = N0 givesthe “correct” model.
Other values of N and any limits as N →∞ (perhaps with a changeof time-scale) give approximate models. The challenge is to select theαi, but once that is done, the intial condition for index N is give by
ZNi (0) = N−αii Xi(0),
where the Xi(0) are the initial species numbers in the correct model.
If limN→∞ ZNi (·Nγ) = Z∞,γi , then we should have
Xi(t) ≈ Nαi0 Z
∞i (tN−γ0 ).
For example, in the virus model
X2(t) ≈ (1000)2/3Z∞,γ2 (t(1000)−2/3)
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Enzyme reaction (cf. Darden (1982))
Basic assumption: A small number of enzymes interacts with a largenumber O(N) of substrate molecules.
A+ E AE ⇀ E +B
XE(t) = XE(0)− Y1(
∫ t
0
κ1XA(s)XE(s)ds) + Y2(κ2
∫ t
0
XAE(s)ds)
+Y3(κ3
∫ t
0
XAE(s)ds)
XA(t) = XA(0)− Y1(
∫ t
0
κ1XA(s)XE(s)ds)
+Y2(κ2
∫ t
0
XAE(s)ds)
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Scaled model
Assume that production and dissociation reactions are fast, and thesubstrate is present in large numbers. ZN
A = N−1XNA .
XNE (t) = XE(0)− Y1(N
∫ t
0
κ1ZNA (s)XN
E (s)ds) + Y2(Nκ2
∫ t
0
XNAE(s)ds)
+Y3(Nκ3
∫ t
0
XNAE(s)ds)
ZNA (t) = ZN
A (0)−N−1Y1(N
∫ t
0
κ1ZNA (s)XN
E (s)ds)
+N−1Y2(Nκ2
∫ t
0
XNAE(s)ds)
m ≡ XNE (t) +XN
AE(t) does not depend on t.
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Averaging by the equation
Dividing the first equation by N and taking the limit
N−1XNE (t) = N−1XE(0)−N−1Y1(N
∫ t
0
κ1ZNA (s)XN
E (s)ds)
+N−1Y2(Nκ2
∫ t
0
XNAE(s)ds)
+N−1Y3(Nκ3
∫ t
0
XNAE(s)ds)
0 = limN→∞
∫ t
0
(−κ1ZNA (s)XN
E (s) + κ2XNAE(s) + κ3X
NAE(s))ds
and
limN→∞
∫ t
0
XNE (s)ds = lim
N→∞
∫ t
0
(m−XNAE(s))ds =
∫ t
0
m(κ2 + κ3)
κ1ZA(s) + κ2 + κ3ds.
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Michaelis-Menten equation
ZNA (t) = ZN
A (0)−N−1Y1(N
∫ t
0
κ1ZNA (s)XN
E (s)ds)
+N−1Y2(Nκ2
∫ t
0
XNAE(s)ds)
which converges to
ZA(t) = ZA(0)−∫ t
0
κ1ZA(s)m(κ2 + κ3)
κ1ZA(s) + κ2 + κ3ds
+κ2
∫ t
0
mκ1ZA(s)
κ1ZA(s) + κ2 + κ3ds
= ZA(0)−∫ t
0
mκ1κ3ZA(s)
κ1ZA(s) + κ2 + κ3ds
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Gaussian limit Kang, Kurtz, and Popovic (2014)
Call the limiting quadratic variation∫ t
0 σ2(ZA(s))ds.∫ t
0
σ2(ZA(s))ds
=
∫ t
0
((h(ZA(s))− 1)2(κ2 + κ3) + (h(ZA(s)) + 1)2κ2 + h(ZA(s))2κ3)
× mκ1ZA(s)
κ1ZA(s) + κ2 + κ3ds
ThenUN(t) =
√N(ZN
A (t)− Z(t))
converges to the solution of
U(t) = U(0) +W (
∫ t
0
σ2(ZA(s))ds)−∫ t
0
mκ1κ3(κ2 + κ3)
(κ2 + κ3 + κ1ZA(s))2U(s)ds
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Diffusion approximation
Rationale: Find a sequence of diffusion processes DNA such that
√N(DN
A − ZA)⇒ U.
So
DNA (t) = ZN
A (0) +1√N
∫ t
0
σ(DNA (s))dW (s)−
∫ t
0
mκ1κ3DNA (s)
κ2 + κ3 + κ1DNA (s)
ds
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Heat shock model
The following reaction network is a given as a model for the heatshock response in E. Coli by Srivastava, Peterson, and Bentley (2001).The analysis is from Kang (2012).
Reaction Intensity Reaction Intensity∅ → S8 4.00× 100 S6 + S8 → S9 3.62× 10−4XS6XS8
S2 → S3 7.00× 10−1XS2 S8 → ∅ 9.99× 10−5XS8
S3 → S2 1.30× 10−1XS3 S9 → S6 + S8 4.40× 10−5XS9
∅ → S2 7.00× 10−3XS1 ∅ → S1 1.40× 10−5
stuff + S3 → S5 + S2 6.30× 10−3XS3 S1 → ∅ 1.40× 10−6XS1
stuff + S3 → S4 + S2 4.88× 10−3XS3 S7 → S6 1.42× 10−6XS4XS7
stuff + S3 → S6 + S2 4.88× 10−3XS3 S5 → ∅ 1.80× 10−8XS5
S7 → S2 + S6 4.40× 10−4XS7 S6 → ∅ 6.40× 10−10XS6
S2 + S6 → S7 3.62× 10−4XS2XS6 S4 → ∅ 7.40× 10−11XS4
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Exponents
ρ1 = β1
ρ2 = α2 + β2
ρ3 = α3 + β3
ρ4 = α1 + β4
ρ5 = α3 + β5
ρ6 = α3 + β6
ρ7 = α3 + β7
ρ8 = α7 + β8
ρ9 = α2 + α6 + β9
ρ10 = α6 + α8 + β10
ρ11 = α8 + β11
ρ12 = α9 + β12
ρ13 = β13
ρ14 = α1 + β14
ρ15 = α4 + α7 + β15
ρ16 = α5 + β16
ρ17 = α6 + β17
ρ18 = α4 + β18
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Balance equations
{Z1} ρ13 = ρ14{Z2} max{ρ3, ρ4, ρ5, ρ6, ρ7, ρ8} = ρ2 ∨ ρ9{Z3} ρ2 = max{ρ3, ρ5, ρ6, ρ7}{Z4} ρ6 = ρ18{Z5} ρ5 = ρ16{Z6} max{ρ7, ρ8, ρ12, ρ15} = ρ9 ∨ ρ17{Z7} ρ9 = ρ8 ∨ ρ15{Z8} ρ1 ∨ ρ12 = ρ10 ∨ ρ11{Z9} ρ10 = ρ12{Z2 + Z3 + Z7} ρ4 = ρ15{Z2 + Z3} ρ4 ∨ ρ8 = ρ9{Z2 + Z7} max{ρ3, ρ4, ρ5, ρ6, ρ7} = ρ2 ∨ ρ15{Z6 + Z7 + Z9} ρ7 = ρ17{Z6 + Z9} max{ρ7, ρ8, ρ15} = ρ9 ∨ ρ17{Z6 + Z7} ρ7 ∨ ρ12 = ρ17 ∨ ρ10{Z8 + Z9} ρ1 = ρ17
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θ · Z First scale Second scale Third scale{Z1} γ ≤ 2 balanced balanced{Z2} balanced balanced balanced{Z3} balanced balanced balanced{Z4} γ ≤ 2 γ ≤ 2 balanced{Z5} γ ≤ 2 γ ≤ 2 balanced{Z6} γ ≤ 1 balanced balanced{Z7} γ ≤ 1 γ ≤ 1 balanced{Z8} γ ≤ 0 γ ≤ 1 balanced{Z9} balanced balanced balanced{Z2 + Z3 + Z7} γ ≤ 0 balanced balanced{Z2 + Z3} γ ≤ 0 γ ≤ 1 balanced{Z2 + Z7} balanced balanced balanced{Z6 + Z7 + Z9} γ ≤ 1 γ ≤ 2 γ ≤ 2{Z6 + Z9} γ ≤ 1 γ ≤ 2 γ ≤ 2{Z6 + Z7} γ ≤ 1 balanced balanced{Z8 + Z9} γ ≤ 0 γ ≤ 1 balanced
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ZN,γ1 (t) = ZN,γ
1 (0) +N−α1,γY13(
∫ t
0
κ13Nγ−2 ds)−N−α1,γY14(
∫ t
0
κ14Nγ−2+α1,γZN,γ
1 (s) ds)
ZN,γ2 (t) = ZN,γ
2 (0) +N−α2,γY3(
∫ t
0
κ3NγZN,γ
3 (s) ds) +N−α2,γY4(
∫ t
0
κ4Nγ−1ZN,γ
1 (s) ds)
+N−α2,γY5(
∫ t
0
κ5Nγ−1+α2,γZN,γ
3 (s) ds) +N−α2,γY6(
∫ t
0
κ6Nγ−1+α2,γZN,γ
3 (s) ds)
+N−α2,γY7(
∫ t
0
κ7Nγ−1+α2,γZN,γ
3 (s) ds) +N−α2,γY8(
∫ t
0
κ8Nγ−2ZN,γ
7 (s) ds)
−N−α2,γY2(
∫ t
0
κ2Nγ+α2,γZN,γ
2 (s) ds)−N−α2,γY9(
∫ t
0
κ9Nγ−2+α2,γZN,γ
2 (s)ZN,γ6 (s) ds)
ZN,γ3 (t) = ZN,γ
3 (0) +N−α2,γY2(
∫ t
0
κ2Nγ+α2,γZN,γ
2 (s) ds)
−N−α2,γY3(
∫ t
0
κ3Nγ+α2,γZN,γ
3 (s) ds)−N−α2,γY5(
∫ t
0
κ5Nγ−1+α2,γZN,γ
3 (s) ds)
−N−α2,γY6(
∫ t
0
κ6Nγ−1+α2,γZN,γ
3 (s) ds)−N−α2,γY7(
∫ t
0
κ7Nγ−1+α2,γZN,γ
3 (s) ds)
•First •Prev •Next •Go To •Go Back •Full Screen •Close •Quit 55
ZN,γ4 (t) = ZN,γ
4 (0) +N−2Y6(
∫ t
0
κ6Nγ−1+α2,γZN,γ
3 (s) ds)−N−2Y18(∫ t
0
κ18NγZN,γ
4 (s) ds)
ZN,γ5 (t) = ZN,γ
5 (0) +N−2Y5(
∫ t
0
κ5Nγ−1+α2,γZN,γ
3 (s) ds)−N−2Y16(∫ t
0
κ16NγZN,γ
5 (s) ds)
ZN,γ6 (t) = ZN,γ
6 (0) + Y7(
∫ t
0
κ7Nγ−1+α2,γZN,γ
3 (s) ds) + Y8(
∫ t
0
κ8Nγ−2ZN,γ
7 (s) ds)
+Y12(
∫ t
0
κ12Nγ−2+α8,γZN,γ
9 (s) ds) + Y15(
∫ t
0
κ15Nγ−1ZN,γ
4 (s)ZN,γ7 (s) ds)
−Y9(∫ t
0
κ9Nγ−2+α2,γZN,γ
2 (s)ZN,γ6 (s) ds)
−Y10(∫ t
0
κ10Nγ−2+α8,γZN,γ
6 (s)ZN,γ8 (s) ds)− Y17(
∫ t
0
κ17Nγ−2ZN,γ
6 (s) ds)
ZN,γ7 (t) = ZN,γ
7 (0) + Y9(
∫ t
0
κ9Nγ−2+α2,γZN,γ
2 (s)ZN,γ6 (s) ds)
−Y8(∫ t
0
κ8Nγ−2ZN,γ
7 (s) ds)
−Y15(∫ t
0
κ15Nγ−1ZN,γ
4 (s)ZN,γ7 (s) ds)
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ZN,γ8 (t) = ZN,γ
8 (0) +N−α8,γY1(
∫ t
0
κ1Nγ ds) +N−α8,γY12(
∫ t
0
κ12Nγ−2+α8,γZN,γ
9 (s) ds)
−N−α8,γY10(
∫ t
0
κ10Nγ−2+α8,γZN,γ
6 (s)ZN,γ8 (s) ds)
−N−α8,γY11(
∫ t
0
κ11Nγ−2+α8,γZN,γ
8 (s) ds)
ZN,γ9 (t) = ZN,γ
9 (0) +N−α8,γY10(
∫ t
0
κ10Nγ−2+α8,γZN,γ
6 (s)ZN,γ8 (s) ds)
−N−α8,γY12(
∫ t
0
κ12Nγ−2+α8,γZN,γ
9 (s) ds)
ZN,γ2,3 = ZN,γ
2,3 (0) +N−α2,γY4(
∫ t
0
κ4Nγ−1+α1,γZN,γ
1 (s) ds)
+N−α2,γY8(
∫ t
0
κ8Nγ−2ZN,γ
7 (s) ds)
−N−α2,γY9(
∫ t
0
κ9Nγ−2+α2,γZN,γ
2 (s)ZN,γ6 (s) ds)
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γ = 0
α1 α2 α3 α4 α5 α6 α7 α8 α9
1 0 0 2 2 0 0 0 0
For γ = 0, {ZN,02 , ZN,0
3 , ZN,08 } converge to the solution of
Z02(t) = Z0
2(0) + Y3(
∫ t
0
κ3Z03(s) ds) + Y4(
∫ t
0
κ4Z01(0) ds)
−Y2(∫ t
0
κ2Z02(s) ds)
Z03(t) = Z0
3(0) + Y2(
∫ t
0
κ2Z02(s) ds)− Y3(
∫ t
0
κ3Z03(s) ds)
Z08(t) = Z0
8(0) + Y1(
∫ t
0
κ1 ds)
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γ = 1
α1 α2 α3 α4 α5 α6 α7 α8 α9
0 0 0 2 2 0 0 1 1
For γ = 1, {ZN,12,3 , Z
N,16 , ZN,1
7 , ZN,18 } converges to the solution of
Z12,3(t) = Z1
2,3(0) + Y4(
∫ t
0
κ4Z11(0) ds)
Z16(t) = Z1
6(0) + Y7(
∫ t
0
κ7Z1
3(s) ds) + Y12(
∫ t
0
κ12Z19(0) ds)
+Y15(
∫ t
0
κ15Z14(0)Z1
7(s) ds)− Y10(∫ t
0
κ10Z16(s)Z1
8(s) ds)
Z17(t) = Z1
7(0)− Y15(∫ t
0
κ15Z14(0)Z1
7(s) ds)
Z18(t) = Z1
8(0) +
∫ t
0
κ1 ds
Z1
3(t) =κ2Z
12,3(s)
κ2 + κ3
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3:σ32+Eσ32
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3:σ32+Eσ32
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γ = 2
α1 α2 α3 α4 α5 α6 α7 α8 α9
0 1 1 2 2 0 0 2 2
For γ = 2, {ZN,21 , ZN,2
2,3 , ZN,24 , ZN,2
5 , ZN,28 , ZN,2
9 } converges to the solution of
Z21(t) = Z2
1(0) + Y13(
∫ t
0
κ13 ds)− Y14(∫ t
0
κ14Z21(s) ds)
Z22,3(t) = Z2
2,3(0) +
∫ t
0
[κ4Z
21(s)− κ9Z2
2(s)Z2
6(s))]ds
Z24(t) = Z2
4(0) +
∫ t
0
(κ6Z23(s)− κ18Z2
4(s)) ds
Z25(t) = Z2
5(0) +
∫ t
0
(κ5Z23(s)− κ16Z2
5(s)) ds
Z28(t) = Z2
8(0) +
∫ t
0
(κ1 − κ7Z23(s)− κ11Z2
8(s)) ds
Z29(t) = Z2
9(0) +
∫ t
0
κ7Z23(s) ds
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Z13(t) =
κ3Z12,3(s)
κ2 + κ3Z1
3(t) =κ2Z
12,3(s)
κ2 + κ3
Z2
6(s) =κ7Z
23(s) + κ12Z
29(s)
κ10Z28(s)
Z2
7(t) =κ9Z
22(t)Z6(t)
κ15Z24(t)
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3:σ32+Eσ32
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Things I haven’t told youKang and Kurtz (2013), Kang, Kurtz, and Popovic (2014)
In general, additional balance conditions are needed.
A systematic approach to averaging fast components.
How to derive appropriate diffusion/Langevin approximations.
Things I think would be usefulHow to automate the analysis.
Criteria for “optimal” selection of the scaling exponents.
How to systematically incorporate biological constraints.
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ReferencesDavid F. Anderson. A modified next reaction method for simulating chemical systems with time dependent
propensities and delays. J. Chem. Phys., 127(21):214107, 2007.
David F. Anderson and Thomas G. Kurtz. Stochastic analysis of biochemical systems, volume 1 of MathematicalBiosciences Institute Lecture Series. Stochastics in Biological Systems. Springer, Cham; MBI Mathematical Bio-sciences Institute, Ohio State University, Columbus, OH, 2015. ISBN 978-3-319-16894-4; 978-3-319-16895-1.
Karen Ball, Thomas G. Kurtz, Lea Popovic, and Greg Rempala. Asymptotic analysis of multiscale approxima-tions to reaction networks. Ann. Appl. Probab., 16(4):1925–1961, 2006. ISSN 1050-5164.
Thomas A. Darden. Enzyme kinetics: stochastic vs. deterministic models. In Instabilities, bifurcations, andfluctuations in chemical systems (Austin, Tex., 1980), pages 248–272. Univ. Texas Press, Austin, TX, 1982.
Stewart N. Ethier and Thomas G. Kurtz. Markov processes: Characterization and Convergence. Wiley Series inProbability and Mathematical Statistics: Probability and Mathematical Statistics. John Wiley & Sons Inc.,New York, 1986. ISBN 0-471-08186-8.
C. W. Gardiner. Handbook of stochastic methods for physics, chemistry and the natural sciences, volume 13 of SpringerSeries in Synergetics. Springer-Verlag, Berlin, third edition, 2004. ISBN 3-540-20882-8.
M. A. Gibson and J. Bruck. Efficient exact simulation of chemical systems with many species and manychannels. J. Phys. Chem. A, 104(9):1876–1889, 2000.
Daniel T. Gillespie. A general method for numerically simulating the stochastic time evolution of coupledchemical reactions. J. Computational Phys., 22(4):403–434, 1976.
Daniel T. Gillespie. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem., 81:2340–61, 1977.
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Daniel T. Gillespie. Approximate accelerated stochastic simulation of chemically reacting systems. The Journalof Chemical Physics, 115(4):1716–1733, 2001. doi: 10.1063/1.1378322. URL http://link.aip.org/link/?JCP/115/1716/1.
Eric L. Haseltine and James B. Rawlings. Approximate simulation of coupled fast and slow reactions forstochastic chemical kinetics. J. Chem. Phys., 117(15):6959–6969, 2002.
Hye-Won Kang. A multiscale approximation in a heat shock response model of e. coli. BMC Systems Biology,6(1):1–22, 2012. ISSN 1752-0509. doi: 10.1186/1752-0509-6-143. URL http://dx.doi.org/10.1186/1752-0509-6-143.
Hye-Won Kang and Thomas G. Kurtz. Separation of time-scales and model reduction for stochastic reactionnetworks. Ann. Appl. Probab., 23(2):529–583, 2013. ISSN 1050-5164. doi: 10.1214/12-AAP841. URL http://dx.doi.org/10.1214/12-AAP841.
Hye-Won Kang, Thomas G. Kurtz, and Lea Popovic. Central limit theorems and diffusion approximations formultiscale Markov chain models. Ann. Appl. Probab., 24(2):721–759, 2014. ISSN 1050-5164. doi: 10.1214/13-AAP934. URL http://dx.doi.org/10.1214/13-AAP934.
Thomas G. Kurtz. Strong approximation theorems for density dependent Markov chains. Stochastic ProcessesAppl., 6(3):223–240, 1977/78.
R. Srivastava, M. S. Peterson, and W. E. Bentley. Stochastic kinetic analysis of escherichia coli stress circuitusing sigma(32)-targeted antisense. Biotechnol. Bioeng., 75:120–129, 2001.
R. Srivastava, L. You, J. Summers, and J. Yin. Stochastic vs. deterministic modeling of intracellular viralkinetics. J. Theoret. Biol., 218(3):309–321, 2002. ISSN 0022-5193.
N. G. van Kampen. Stochastic processes in physics and chemistry. North-Holland Publishing Co., Amsterdam,1981. ISBN 0-444-86200-5. Lecture Notes in Mathematics, 888.
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Abstract