abe 5643c biological systems modeling 6... · 1) to review how temperature affects biological and...
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Modeling More Complex SystemsABE 5646
Week 6, Spring 2010Week Descr iption Reading Mater ial
6 Modeling Temperature Effects on Biological Systems• Effects on chemical reaction rates• Effects on biological activity, general• Effects on developmental processes in plants and other biological organisms• Degree-day models: basis for and use of
Keen & Spain (1992), Ch. 12Handout; degree-days
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Weeks 6 Objectives
1) To review how temperature affects biological andagricultural systems
2) To gain insight into modeling temperature effects on biological and biophysical systems a) Continuous processesb) Discrete events
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Topics on Temperature Effects
• Temperature in the environment• Heat flow, heat balances• Temperature control (physical, biological)• Chemical reactions• Enzyme activity• Biological activity• Developmental processes in biological systems• Degree-days
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Temperature in the Environment
• Affects all biological systems, • Thus it is usually an important environment variable
that changes over time (exceptions?)• It interacts with other environmental variables• Measurements of T are normally required• May be possible to compute T vs. time for certain
model uses or times• May interact with other model components and thus
may need to be modeled dynamically as a state variable
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Estimating Temperature of the Environment
• Annual variation in daily mean temperature
]365
)(2sin[()( campannualday
ttTTtT −⋅+= π
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Estimating Temperature of the Environment
• Temperature variations during a day
Parton and Logan. 1981. A model for diurnal variation in soil and air temperature. Agric. For. Meteorol. 23:205-216.
]2
)2/12(sin[)()( minmaxmin pddtTTTtT
++−
⋅−+= πDaytimed = daylength, hp = time from noon when maximum temperature occurs, h
)/exp(1)/)(exp()()/exp()( minmin
C
CsunsetsunsetCsunset
TnTttTTTnTTtT
−−−−⋅−+−⋅−
=
n = night length (24-d), hTC = nocturnal time coefficient, about 4 h
Night time
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Temperature during a day
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Temperature
• May vary with system dynamics• May have to model temperature
dynamically
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Heat Loss and Changes in Temperature
• From Week 3 lectures:
Where:T(t) is temperature of an object, oCTE is environment temperature, oCk is a heat transfer or cooling
constant, with units 1/t
])([)(ETtTk
dttdT
−−=
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Cooling Simulation, Ex. 1-6
ktEE eTTTtT −−+= ))0(()(
Exact Solution
Numerical solutionT(t)=T(t-1)-k*[T(t-1)-TE]For t > 0.0; initial conditions T(0)
Temperature of Body
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
0.0 10.0 20.0 30.0 40.0 50.0
Time, hours
Tem
per
atu
re,
C
exact approximate
Environmental Temperature
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But, Prior Equations Do Not Show Heat FlowStar ting with heat:
WhereΔQ = change in heat of an object, caloriesm = mass of the object, gcp = specific heat, a property of the mass, calories g-1 oC-1
T = temperature, oC
and heat is the extensive variable that flows from one object to another; temperature is an intensive variable related to the heat content of a body
dtdTmc
dtdQand
TmcQ
p
p
=
∆=∆
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Written differently
])([)(
])([)(
Ep
Ep
TtTcm
kdt
tdTor
TtTkdt
tdTcm
−⋅
−=
−−=⋅
with, k = the conduction heat transfer coefficientand now, units of k are (calories oC-1 time-1)
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Set Point Temperature Control
Heater (on/off)h=0 if T>Tset + tol
hmax if T<Tset - tol
])([)(E
pp
TtTcm
kcm
hdt
tdT−
⋅−
⋅=
T
h=Heater Heat
Heat Loss
TE
Tset , tolhmax
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Proportional Temperature ControlProportional control (lags in sensing
temperature & heater output)h = hmax (Tc - Ts) if Ts<Tc
otherwise, h = 0Ts = temperature of sensorTc = control or desired temperature
)]()([)(
)]()([])([)(
tTtTcm
kdt
tdT
tTtTcm
kTtTcm
kcm
hdt
tdT
ssps
ss
ssps
sE
pp
−⋅
=
−⋅
−−⋅
−⋅
=
T
h=Heater Heat
Heat Loss
Ts
Sensor
hmax
Tc
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Implications
• Simple examples show lags in temperature control• Both demonstrate very simple forms of feedback control• Biological control systems are similar
– Feedback control of body temperature– Usually, more like proportional control such that internal heat
production, for example, varies in response to a biological setpoint or range
– Lags also exist between a state and output from the biological control system (sensing, metabolism, muscle response, etc.)
– What are other examples of biological control systems?
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Temperature and Chemical Reactions
• Arrhenius equation• Q10 approach• Also used directly in
some enzyme-substrate reactions
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Arrhenius EquationConsider the equation:
fi kFlowS S P
wherefi = flow rate of S into reactionK(T) = rate constant, dependent
on temperature
STkfdtdS
i ⋅−= )(
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Arrhenius Equation
• Assumes that substrate (S) must reach an activated state (Ea) for the reaction to occur
• The faster that S reaches the activation state, the faster the reaction rate
See Thornley and Johnson (Chapter 5)
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Arrhenius Equation
A = a constantEa = Energy of activationR = gas constantT = Absolute
Temperature, 0K
STkfdtdS
i ⋅−= )(
)exp(RTEAk a−=
Ea/R typically in range of 5,000 to 15,000 K
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Q10 Approximation
• Q10 is the factor at which the rate constant, k, increases for a temperature increment of 10 0K
• Empirical, based on observations
• Also 3 parameters to estimate ]10/)[(
10rTT
rQkk −=
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Conceptual Pools of Enzymes
From Curry and Feldman, 1987. Texas A&M University Press
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Enzyme Reactions
• Have optimal temperature range
• Enzymes assumed to be in inactive state (denatured), initial active state, and activated state (Ea), depending on temperature
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Enzyme Reaction Rate
)//exp(1)/exp()(
RTHRSRTEATk a
∆−∆+−=
ΔH = enthalpy of reactionΔS = entropy of reactionOthers same as in Arrhenius equation
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Enzyme Reaction Rates vs. Temperature
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Enzyme Reaction Rates vs. Temperature
Parameters must be estimated
(4) from data[A,
Ea/R,ΔH/R,ΔS/R]
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Estimation of Enzyme Parameters
From Curry and Feldman, 1987. Texas A&M University Press
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Other Equations for Temperature Effects on Biological Systems
O’Neill et al. (1972)
))(1(400
)/4011(
)exp(
max10
22
max
max
max
max
opt
opt
opt
opt
xT
TTQW
WWx
TTTT
V
TTTTU
withxVUkk
−−=
++=
−
−=
−−
=
=
Keen and Spain, pp193-4
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Other Equations for Temperature Effects on Biological Systems
Logan (1988)
Keen and Spain, pp193-4
opt
T
TTTT
TTwith
kkk
−−
=
−=
−−
+=
max
minmax2
min1
22
21
21
1
)(
)exp(
τ
τ
τττ
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Temperature Effects on Enzyme-Mediated Reactions
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Discussion
• Arrhenius• Q10
• Enzyme, biological reactions• Practical implications
– Parameter estimation– Normalization– Implementation
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Temperature and Development
• Temperature has major influence on developmental processes of plants, insects, other organisms
• Duration of time between egg and pupae in insects may vary between 8 and 40 days, depending on temperature
• Similarly, duration of time from planting to emergence, time from emergence to 1st flower, time needed to complete its lifecycle to maturity, etc. depend strongly on temperature
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Experiments to Measure Development
Typically, temperature treatments, each having a constant temperature
Time measured until a particular development stage (duration)
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Development Rates Computed as Reciprocal of Duration
Development “rate” is the inverse of days required to complete a phase
kd(T) = [1/(Dur(T)]Units are (1/time)
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Modeling DevelopmentIf temperature varies with time, one can integrate development rate
(which is dependent on temperature) until cumulative development is equal to 1.0
The general relationship for computing a development index, hij is:
hij = index of progress along the development pathway from i to j.The length of the phase is ti – tj, also referred to as Developmental time
hij starts at 0.0 at ti and progresses until 1.0 at tj when stage j occurs
dtkh dttij
j
i⋅∫=
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Modeling DevelopmentAnother way to write this equation is:
Where kd is a function of Temperature (or kd(T), and T may vary with time (then kd=kd(T(t)). This is in the same form as our rate equations, and can be solved using Euler. This will be shown later on, with a Δt=1
How would kd vary with Temperature? How would Temperature vary with time?
dij k
dtdh
=
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Modeling GerminationFrom figure 5.12 in Thornley and
Johnson, kd = slope*(T-TB)kd = 0.0588*(T-276) (approx.)where T units are 0Kand slope units are 1/(0C-t)orkd = 0.0588*(T-3.0)If T is 0C
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Modeling Germination
dtTTh
exampleourinor
dtkh
Bt
ij
dttij
j
j
i
)](0588.0[
:
0 −⋅∫=
⋅∫=
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Modeling Germination
tTh
EulerviaedapproximatbecanwhichdtTTh
j
j
toij
Bt
ij
∆⋅−⋅=
−⋅∫=
∑ )]3(0588.0[
:
)](0588.0[0
When the summation (hij) = 1.0, germination occursTB = base temperature, 0K or 0C
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Modeling Germination
• The 0.0588 can be moved outside the summation sign (it is constant),
• And if Δt=1, then we can re-write the equation as:
• When hij = 1, germination occurs, and at this time, the left side of the equation above is (1/.0588)=17.0
• Note that units of the left side of the equation are in 0C-time; if time is in days, units are 0C-days
1)3(0588.0
⋅−=∑ Th
jto
ij
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Modeling Development Using Degree-Days
)( Bto TTDD j −=∑
The previous equation can be generalized to show the widely-used degree-day model
Where DD = degree daysand the summation is carried out until a Threshold is reachedWhen the threshold is reached (on day tj), development is complete and the germination (or other event) occurs on that day
In the example above, the threshold would be 17.0
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Degree Days
• Check units on DD, what are they?• What are the assumptions of this method?• Can it be further generalized?• How can you compute the parameters
needed to use DD to model time to first flower, time to germination, time to maturity, time between egg and larvae, ???
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Modeling Development
What if kd is not linear? We have seen how enzyme kinetics depend on temperature, and the internal biological reactions that lead to development are enzyme reactions
dtkh dttij
j
i⋅∫=
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Modeling Development
What if kd is not linear? In many models, kd is approximated by a piecewise linear function:
dtkh dttij
j
i⋅∫=
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Development in Varying Temperature Environments
• Assumption is that development continues at the temperature it experiences, with no lags, memory effect, etc.
• Choice of using daily average vs. hourly temperatures will affect results
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Discussion
• Temperature in the environment• Heat flow, heat balances• Temperature control (physical, biological)• Chemical reactions• Enzyme activity• Biological activity• Developmental processes in biological systems• Degree-days
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Homework Set 4ABE 5646
Due on February 19, 20101. Work camel body temperature simulation problem 12-3 in Keen &
Spain, pp. 187-1882. Work problem 12-4 in Keen & Spain on enzyme-catalyzed reactions, p.
191 3. Use the O’Neill temperature function with the following parameters:
Q10 = 2.1 Tmax = 40Topt = 28 kmax = 0.025 (units of d-1)
a. Plot rate (kT) vs. Temperature (T) over the range of 0 to 40 oC.b. Assume that we want to use a linear approximation to this function for
temperatures below 30 oC, what are the slope and intercept values?c. Explain how you could use this approximation with a degree-day model.
What would the threshold be for development?
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Questions?