basic methods in theoretical biology
DESCRIPTION
Basic Methods in Theoretical Biology. 1 Methodology 2 Mathematical toolkit 3 Models for processes 4 Model-based statistics. http://www.bio.vu.nl/thb/course/tb/tb.pdf. Empirical cycle 1.1. Assumptions summarize insight 1.1. task of research: make all assumptions explicit - PowerPoint PPT PresentationTRANSCRIPT
Basic Methods in
Theoretical Biology
1 Methodology 2 Mathematical toolkit 3 Models for processes 4 Model-based statistics
http://www.bio.vu.nl/thb/course/tb/tb.pdf
Empirical cycle 1.1
Assumptions summarize insight 1.1
• task of research: make all assumptions explicit these should fully specify subsequent model formulations
• assumptions: interface between experimentalist theoretician
• discrepancy model predictions measurements: identify which assumption needs replacement
• models that give wrong predictions can be very useful to increase insight
• structure list of assumptions to replacebility (mind consistency!)
Model: definition & aims 1.1
• model: scientific statement in mathematical language “all models are wrong, some are useful”
• aims: structuring thought; the single most useful property of models: “a model is not more than you put into it” how do factors interact? (machanisms/consequences) design of experiments, interpretation of results inter-, extra-polation (prediction) decision/management (risk analysis)
• observations/measurements: require interpretation, so involve assumptions best strategy: be as explicitly as possible in assumptions
Model properties 1.1
• language errors: mathematical, dimensions, conservation laws
• properties: generic (with respect to application) realistic (precision; consistency with data) simple (math. analysis, aid in thinking) complex models are easy to make, difficult to test simple models that capture essence are difficult to make
plasticity in parameters (support, testability)
• ideals: assumptions for mechanisms (coherence, consistency) distinction action variables vs measured quantities need for core and auxiliary theory
Modelling 1 1.1
• model: scientific statement in mathematical language “all models are wrong, some are useful”
• aims: structuring thought; the single most useful property of models: “a model is not more than you put into it” how do factors interact? (machanisms/consequences) design of experiments, interpretation of results inter-, extra-polation (prediction) decision/management (risk analysis)
Modelling 2 1.1
• language errors: mathematical, dimensions, conservation laws
• properties: generic (with respect to application) realistic (precision) simple (math. analysis, aid in thinking) plasticity in parameters (support, testability)
• ideals: assumptions for mechanisms (coherence, consistency) distinction action variables/meausered quantities core/auxiliary theory
Presumptions Laws 11.1
LawsTheoriesHypothesesPresumptions
decrease in demonstrated supportamount of support is always limitedProofs only exist in mathematics
role of abstract concepts
0 large
“facts” “general theories”
no predictions possible predictions possible
Theories Models 1.1
Theory: set of coherent and consistent assumptions from which models can be derived for particular situations
Models may or may not represent theories it depends on the assumptions on which they are based
If a model itself is the assumption, it is only a description if it is inconsistent with data, and must be rejected, you have nothingIf a model that represents a theory must be rejected, a systematic search can start to assumptions that need replacementUnrealistic models can be very useful in guiding research to improve assumptions (= insight)Many models don’t need to be tested against data because they fail more important consistency testsTestability of models/theories comes in gradations
Auxiliary theory 1.1
Quantities that are easy to measure (e.g. respiration, body weight) have contributions form several processes they are not suitable as variables in explenatory models
Variables in explenatory models are not directly measurable we need auxiliary theory to link core theory to measurements
Standard DEB model: isomorph with 1 reserve & 1 structure that feeds on 1 type of food
Measurements typicallyinvolve interpretations, models 1.1
Given: “the air temperature in this room is 19 degrees Celsius” Used equipment: mercury thermometer
Assumption: the room has a temperature (spatially homogeneous)Actual measurement: height of mercury columnHeight of the mercury column temperature: model! How realistic is this model? What if the temperature is changing?
Task: make assumptions explicit and be aware of themQuestion: what is calibration?
Complex models 1.1
• hardly contribute to insight
• hardly allow parameter estimation
• hardly allow falsification
Avoid complexity by
• delineating modules
• linking modules in simple ways
• estimate parameters of modules only
Causation 1.1
Cause and effect sequences can work in chains A B C
But are problematic in networks A
B C Framework of dynamic systems allow for holistic approach
Dimension rules 1.2
• quantities left and right of = must have equal dimensions
• + and – only defined for quantities with same dimension
• ratio’s of variables with similar dimensions are only dimensionless if addition of these variables has a meaning within the model context
• never apply transcendental functions to quantities with a dimension log, exp, sin, … What about pH, and pH1 – pH2?
• don’t replace parameters by their values in model representations y(x) = a x + b, with a = 0.2 M-1, b = 5 y(x) = 0.2 x + 5 What dimensions have y and x? Distinguish dimensions and units!
Models with dimension problems 1.2
• Allometric model: y = a W b
y: some quantity a: proportionality constant W: body weight b: allometric parameter in (2/3, 1) Usual form ln y = ln a + b ln W Alternative form: y = y0 (W/W0 )b, with y0 = a W0
b
Alternative model: y = a L2 + b L3, where L W1/3
• Freundlich’s model: C = k c1/n
C: density of compound in soil k: proportionality constant c: concentration in liquid n: parameter in (1.4, 5) Alternative form: C = C0 (c/c0 )1/n, with C0 = kc0
1/n
Alternative model: C = 2C0 c(c0+c)-1 (Langmuir’s model)
Problem: No natural reference values W0 , c0
Values of y0 , C0 depend on the arbitrary choice
Egg development time 1.2
Bottrell, H. H., Duncan, A., Gliwicz, Z. M. , Grygierek, E., Herzig, A., Hillbricht-Ilkowska, A., Kurasawa, H. Larsson, P., Weglenska, T. 1976 A review of some problems in zooplankton production studies.Norw. J. Zool. 24: 419-456
)))(ln(3414.0)ln(2193.03956.3exp( 2TTD
Kelvinin etemperatur t timedevelopmen egg
TD
2
2
)(ln
ln)dim(
ln
ln)dim(
ln)dim()))(ln()ln(exp(
K
tc
K
tb
taTcTbaD
molecule
cell
individual
population
ecosystem
system earth
time
spac
e
Space-time scales 1.3
When changing the space-time scale, new processes will become important other will become less importantModels with many variables & parameters hardly contribute to insight
Each process has its characteristic domain of space-time scales
Problematic research areas 1.3
Small time scale combined with large spatial scaleLarge time scale combined with small spatial scale
Reason: likely to involve models with large number of variables and parameters
Such models rarely contribute to new insight due to uncertainties in formulation and parameter values
Different models can fit equally well 1.5
Length, mmO2 c
onsu
mpt
ion,
μl/
h
Two curves fitted:
a L2 + b L3
with a = 0.0336 μl h-1 mm-2
b = 0.01845 μl h-1 mm-3
a Lb
with a = 0.0156 μl h-1 mm-2.437
b = 2.437
Plasticity in parameters 1.7
If plasticity of shapes of y(x|a) is large as function of a:
• little problems in estimating value of a from {xi,yi}i
(small confidence intervals)
• little support from data for underlying assumptions
(if data were different: other parameter value results, but still a good fit, so no rejection of assumption) A model can fit data well for wrong reasons
Biodegradation of compounds 1.7
n-th order model Monod modelnkXX
dt
d
1)1(10 )1()(
nn ktnXtX
ktXtXn
0
0
)( kXt /0
}exp{)( 0
1
ktXtXn
n
akXaXt
nn
1
1)(
111
00
XK
XkX
dt
d
ktXtXKXtX }/)(ln{)(0 00
ktXtXXK
0
0
)(
}/exp{)( 0
0
KktXtXXK
aKkakXaXt ln)1()( 1100
; ;
X : conc. of compound, X0 : X at time 0 t : time k : degradation rate n : order K : saturation constant
kXt /0
Biodegradation of compounds 1.7
n-th order model Monod model
scaled time scaled time
scal
ed c
onc.
scal
ed c
onc.
Verification falsification 1.9
Verification cannot work because different models can fit data equally well
Falsification cannot work because models are idealized simplifications of reality “All models are wrong, but some are useful”
Support works to some extend
Usefulness works but depends on context (aim of model) a model without context is meaningless
Model without dimension problem 1.2
Arrhenius model: ln k = a – T0 /Tk: some rate T: absolute temperaturea: parameter T0: Arrhenius temperature
Alternative form: k = k0 exp{1 – T0 /T}, with k0 = exp{a – 1}
Difference with allometric model: no reference value required to solve dimension problem
Central limit theorems 2.6
The sum of n independent identically (i.i.) distributed random variables becomes normally distributed for increasing n.
The sum of n independent point processes tends to behave as a Poisson process for increasing n.
yy
YXZ yYPyzXPzZPdyyfyzfzfYXZ )()()(;)()()(
Number of events in a time interval is i.i. Poisson distributedTime intervals between subsequent events is i.i. exponentially distributed
Sums of random variables 2.6
)λexp()λ()(
λ)(
)λexp(λ)(
1 yyn
yf
xxf
nY
X
)(Var)(Var;1
i
n
ii XnYXY
)λexp(!
λ)()(
)λexp(!
λ)(
ny
nyYP
xxXP
y
x
Exp
onen
tial p
rob
dens
Poi
sson
pro
b
Normal probability density 2.6
2
2 σ
μ
2
1exp
πσ2
1)(
xxf X
μ'μ
2
1exp
π2
1)( 1- xxxf
nX
μ)/σ(x-
σ
σ95%
Dynamic systems 3.2
Defined by simultaneous behaviour of input, state variable, outputSupply systems: input + state variables outputDemand systems: input state variables + outputReal systems: mixtures between supply & demand systemsConstraints: mass, energy balance equationsState variables: span a state space behaviour: usually set of ode’s with parametersTrajectory: map of behaviour state vars in state spaceParameters: constant, functions of time, functions of modifying variables compound parameters: functions of parameters
Statistics 4.1
Deals with• estimation of parameter values, and confidence in these values• tests of hypothesis about parameter values differs a parameter value from a known value? differ parameter values between two samples?
Deals NOT with• does model 1 fit better than model 2 if model 1 is not a special case of model 2
Statistical methods assume that the model is given(Non-parametric methods only use some properties of the given model, rather than its full specification)
Stochastic vs deterministic models 4.1
Only stochastic models can be tested against experimental data
Standard way to extend deterministic model to stochastic one: regression model: y(x| a,b,..) = f(x|a,b,..) + e, with e N(0,2)Originates from physics, where e stands for measurement error
Problem: deviations from model are frequently not measurement errorsAlternatives:• deterministic systems with stochastic inputs• differences in parameter values between individualsProblem: parameter estimation methods become very complex
Stochastic vs deterministic models 4.1
Tossing a die can be modeled in two ways• Stochastically: each possible outcome has the same probability• Deterministically: detailed modelling of take off and bounching, with initial conditions; many parametersImperfect control of process makes deterministic model unpractical
Large scatter 4.1
• complicates parameter estimation
• complicates falsification
Avoid large scatter by
• Standardization of factors that contribute to measurements
• Stratified sampling
Kinds of statistics 4.1
Descriptive statistics sometimes useful, frequently boring
Mathematical statistics beautiful mathematical construct rarely applicable due to assumptions to keep it simple
Scientific statistics still in its childhood due to research workers being specialised upcoming thanks to increase of computational power (Monte Carlo studies)
Tasks of statistics 4.1
Deals with• estimation of parameter values, and confidence of these values• tests of hypothesis about parameter values differs a parameter value from a known value? differ parameter values between two samples?
Deals NOT with• does model 1 fit better than model 2 if model 1 is not a special case of model 2
Statistical methods assume that the model is given(Non-parametric methods only use some properties of the given model, rather than its full specification)
Independent observations 4.1
IIf
If X and Y are independent
Statements to remember 4.1
• “proving” something statistically is absurd
• if you do not know the power of your test, you don’t know what you are doing while testing
• you need to specify the alternative hypothesis to know the power this involves knowledge about the subject (biology, chemistry, ..)
• parameters only have a meaning if the model is “true” this involves knowledge about the subject
Nested models 4.5
2210)( xwxwwxy
xwwxy 10)( 0)( wxy 220)( xwwxy
Venn diagram
02 w 01 w
Error of the first kind: reject null hypothesis while it is true
Error of the second kind: accept null hypothesis while the alternative hypothesis is true
Level of significance of a statistical test: = probability on error of the first kind
Power of a statistical test: = 1 – probability on error of the second kind
Testing of hypothesis 4.5
true false
accept 1 -
reject 1 -
null hypothesis
dec
isio
nNo certainty in statistics
Parameter estimation 4.6
Most frequently used method: Maximization of (log) Likelihood
likelihood: probability of finding observed data (given the model), considered as function of parameter values
If we repeat the collection of data many times (same conditions, same number of data)the resulting ML estimate
Profile likelihood 4.6
large sampleapproximation
95% conf interval
Comparison of models 4.6
Akaike Information Criterion for sample size n and K parameters
12)θ(log2
Kn
nKL
12σlog 2
Kn
nKn
in the case of a regression model
You can compare goodness of fit of different models to the same databut statistics will not help you to choose between the models
Confidence intervals 4.6
parameter
estimate
excluding
point 4
sd
excluding
point 4
estimate
including
point 4
sd
including
point 4
L, mm 6.46 1.08 3.37 0.096
rB,d-1 0.099 0.022 0.277 0.023
time, d
leng
th, m
m
ttrLLLtrLLLtL
B
B
smallfor)()exp()()(
00
0
10 LBr
95% conf intervals
correlations amongparameter estimatescan have big effectson sim conf intervals
excludespoint 4
includespoint 4
L