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Transcript of creativecommons/licenses/by-sa/2.0
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Mathematically Controlled Comparisons
Rui Alves
Ciencies Mediques Basiques
Universitat de Lleida
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Outline
Design Principles
Classical Mathematically Controlled Comparisons
Statistical Mathematically Controlled Comparisons
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What are design principles?
Qualitative or quantitative rules that explain why certain designs are recurrently observed in similar types of systems as a solution to a given functional problem
Exist at different levels Nuclear Targeting Sequences
Operon
Gene 1 Gene 2 Gene 3
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Alternative sensor design in two component systems
S
S*
R*
R
Q1 Q2
Monofunctional Sensor Bifunctional Sensor
S
S*
R*
R
Q1 Q2
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Alternative sensor design in two component systems
X3
X1
X2
X4
X5 X6
Monofunctional Sensor Bifunctional Sensor
X3
X1
X2
X4
X5 X6
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Why two types of sensor?
Why do two types of sensor exist?
Hypothesis:Random thing
Alternative hypothesis:There are physiological characteristics in the
systemic response that are specific to each type of sensor and that offer selective advantages under different functionality requirements
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X3
X1
X2
X4
X5 X6
How do we test the alternative hypothesis?1 – Identify functional criteria that have physiological relevance
i) Appropriate fluxes & concentrations
ii) High signal amplification
iii) Appropriate response to cross-talk
iv) Low parameter sensitivity
v) Fast responses
vi) Large stability margins X5
X2
Time
[X2]
Decrease in X5Fluctuation
in X2
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Functionality criteria for effectiveness
Appropriate fluxes & concentrations High signal amplification Appropriate response to cross-talk Low parameter sensitivity Fast responses Large stability margins
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How to test the alternative hypothesis?1 – Identify functional criteria that have physiological relevance
2 – Create Mathematical models for the alternativesS-system has analytical steady state solutionAnalytical solutions → General features of the model that
are independent of parameter values
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X3
X1
X2
X4
X5 X6
A model with a monofunctional sensor
3/ 1/
4 / 2 /
dX dt dX dt
dX dt dX dt
13 15 11 141 11/ 3 5 1 4g g h hdX dt X X X X
Monofunctional Sensor
21 26 242
2222 / 21 6 4g g g hdX dt X X X X
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X3
X1
X2
X4
X5 X6
A model with a bifunctional sensor
3/ 1/
4 / 2 /
dX dt dX dt
dX dt dX dt
13 15 11 121 11/ 3 5 1 2g g h hdX dt X X X X
Bifunctional Sensor
21 26 224 22
2322 / 1 6 4 2 3g h hg g XdX dt X X X X
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Approximating the conserved variables
3 4
2 7
3 42 0 7 0
3 7 0 4 0 0
3 1
3 / 1
/ 3 1 * / 3
f f
f f
X X X
X X X
f X X f X X
13 15 11 141 11/ 3 5 1 4g g h hdX dt X X X X
21 26 242
2224 / 21 6 4g g g hdX dt X X X X
Monofunctional Sensor 1 2
1 8
1 21 0 8 0
1 8 0 2 0 0
4 2
4 / 2
/ 4 2 / 4
f f
f f
X X X
X X X
f X X f X X
X3
X1
X2
X4
X5 X6
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The S-system equations
13 143 4 15 11 1 21 7 1 1 81/ 1 5 1 2
g hf f g h f fdX dt X X X X X X
2421 26 1 22 1
22282 6 2 2/ 1
gg g f hfdX dt X X X X X
Monofunctional Sensor
Bifunctional Sensor 13 143 4 15 11 1 2
1 7 1 1 81/ 1 5 1 2g hf f g h f fdX dt X X X X X X
2421 26 1 22 1 8
233 4 222 7 1 22 / 1 6 2
gg g f f hf f hdX dt X X X X X X X
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S-systems have analytical solutions
22 1 12 2 22 15 12 26
22 17 12 27 7 22 18 12 28 8
11 22 12 21
11 2 21 1 21 15 11 26
21 17 11 27 7 21 18 11 28 8
[ 5] [ 6]
[ ] [ ][ 1]
[ 5] [ 6]
[ ] [ ][ 2]
a b h b a g Log X h g Log X
a g h h Log X a h h g Log XLog X
a a h a
a b a b a g Log X a g Log X
a g a h Log X a h a g Log XLog X
11 22 12 21
11 11 11 21 21 21 22 22 22
a a h a
a g h a g h a g h
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Analytical solutions are nice!!
Calculating analytical expressions for the gains of the dependent variables with respect to independent variables (Signal amplification) is possible
The same for sensitivity to parameters
The same for other magnitudes
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Calculating gains is taking derivatives
11 2 21 1 21 15 11 26
21 17 11 27 7 21 18 11 28 8
11 22 12 21
21 15
11 22 12 21
[ 5] [ 6]
[ ] [ ]
[ 2, 5][ 5]
a b a b a g Log X a g Log X
a g a h Log X a h a g Log Xd
a a h a
L X XdLog X
a g
a a h a
11 2 21 1 21 15 11 26
21 17 11 27 7 21 18 11 28 8
11 22 12 21
11 26
11 22 12 21
[ 5] [ 6]
[ ] [ ]
[ 2, 6][ 6]
a b a b a g Log X a g Log X
a g a h Log X a h a g Log Xd
a a h a
L X XdLog X
a g
a a h a
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Functionality criteria for effectiveness
Appropriate fluxes & concentrations High signal amplification Appropriate response to cross-talk Low parameter sensitivity Large stability margins Fast responses
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Outline
Design Principles
Classical Mathematically Controlled Comparisons
Statistical Mathematically Controlled Comparisons
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How to test the alternative hypothesis?1 – Identify functional criteria that have physiological relevance
2 – Create Mathematical models for the alternatives S-system has analytical steady state solutionAnalytical solutions → General features of the model that
are independent of parameter values
3 – Compare the behavior of the two models with respect to the functional criteria defined in 1
Comparison must be made appropriately, using Mathematically Controlled Comparisons
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How to compare the inherent differences between designs?
X3
X1
X2
X4
X5 X6
X3
X1
X2
X4
X5 X6
13 15 11 141 11/ 3 5 1 4g g h hdX dt X X X X
21 26 242
2222 / 21 6 4g g g hdX dt X X X X
13 15 11 141 11/ 3 5 1 4g g h hdX dt X X X X
21 26 224 22
2322 / 1 6 4 2 3g h hg g XdX dt X X X X
Internal Contraints: Corresponding parameters in processes that are identical have the same values in both designs
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How to compare the inherent differences between designs?
X3
X1
X2
X4
X5 X6
X3
X1
X2
X4
X5 X6
21 26 2 '2242 22 / 1 6 4 ' 2g g hgdX dt X X X X 21 26 224 2
223
22 / 1 6 4 2 3g h hg g XdX dt X X X X
External constraints:’2 and h’22 are degrees of freedom that the system can use to overcome the loss of bifunctionality.
Reference System
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How do we implement external contraints?
Identify variables that are important for the physiology of the system
Choose one of those variables Equal it between the reference system and the
alternative system Calculate what the value that leads to such
equivalence is for the primed parameter
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Partial controlled comparisons There can be situations where the physiology is
not sufficiently known → Not enough external contraints for all parameters
There can be interest in determining the effect of different sets of physiological contrainst upon parameter values→ Alternative sets of external constraints
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Implementing external constraintsChoose Functional Criteria so that the value of the primed parameters can be fixed.
External Constraint 1:
Both systems can achieve the same steady state concentrations AND fluxes
Fixes 2’
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Implementing external constraintsChoose Functional Criteria so that the value of the primed parameters can be fixed.
External Constraint 2:
Both systems can achieve the same total signal amplification
Fixes h22’
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Studying physiological differences of alternative designs
31 34 32 33 363 3 1 4 3 2 3 6
...
...
g g h h hX X X X X X
04/20/23 27
'34 32 33 363 3 4 3 2 3 6
...
'
...
g h h hX X X X X
AM
Q
AB
Q
AB
AM
Q
1
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Comparing concentrations and fluxes
Concentrations and fluxes can be the same in the presence of a bifunctional sensor or of a monofunctional sensor
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Comparing signal amplification
Signal amplification is larger in the system with bifunctional sensor
+ - - ++
+ + + - + - ++ Property in Reference system
Property in Alternative system
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Comparing cross-talk
Sensitivity to cross talk is higher in the system with monofunctional sensor
+
+ + -
-
+ +-
-
+ Property in Reference system
Property in Alternative system
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Comparing sensitivities
Sensitivities can be larger in either system, depending on which sensitivity and on parameter values.
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Comparing stability margins
The system with a monofunctional sensor is absolutely stable and has larger stability margins than the system with a bifunctional sensor
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Comparing transient times
Undecided
31 34 32 33 363 3 1 4 3 2 3 6
...
...
g g h h hX X X X X X '34 32 33 363 3 4 3 2 3 6
...
'
...
g h h hX X X X X
LinearizeLinearize
Calculate analytical solution
Calculate analytical solution
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Comparing transient times
Undecided
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Functionality criteria for effectiveness
Appropriate Concentrations → Both Systems = Appropriate Fluxes → Both Systems = Signal amplification → Bifunctional larger Cross-talk amplification → Bifunctional smaller Margins of stability → Bifunctional smaller Sensitivities to parameter changes → Undecided Fast transient responses → Undecided
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Physiological predictions
Bifunctional design lowers X6 signal amplification prefered when cross-talk is undesirable.
Monofunctional design elevates X6 signal amplification prefered when cross-talk is desirable.
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Questions
What happens when ratios depend on parameter values to be larger or smaller than one?
When the ratios are always larger or smaller than one, independent of parameter values, how much larger or smaller are they, on average?
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A solution to both problems
Statistical Mathematically Controlled Comparisons
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Outline
Design Principles
Classical Mathematically Controlled Comparisons
Statistical Mathematically Controlled Comparisons
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Alternative sensor design in Two Component Systems
X3
X1
X2
X4
X5 X6
Monofunctional Sensor Bifunctional Sensor
X3
X1
X2
X4
X5 X6
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Functionality criteria for effectiveness
Appropriate Concentrations → Both Systems = Appropriate Fluxes → Both Systems = Signal amplification → Bifunctional larger Cross-talk amplification → Bifunctional smaller Margins of stability → Bifunctional smaller Sensitivities to parameter changes → Undecided Fast transient responses → Undecided
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Quantifying the differences
To find out how much bigger or smaller or to decide whether an undecided ratio is bigger or smaller than one we have to plug in numbers into the equations
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Statistical controlled comparisons
Interested in a specific system from a specific organism: Plug in values and calculate the quantitative
differences Interested in large scale analysis
Large scale sampling of parameter and independent variable space followed by calculation of properties and statistical comparison
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Statistical controlled comparisons
Parameters: s, s gs, hs
Independent Variables X5, X6, X7, X8
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Basic sampling
Random number generator
L1L’1
Sample in Log space
X5X6Random number generator
[-L’’1,X5,L’’’1], ...
Sample in Log space
gg2Random number generator
[-5,g1,0], [0,g1,5] ...Sample
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Importance sampling
Random number generator
Sample1 [-L1,1,L’1]
Normal, Bessel,…Uniform
Filters:
Positive Signal Amplification
Stable Steady State
Fast Response Times
Calculate Values for systemic properties
YesKeep set
NoDiscard set
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Warnings about the filters in sampling
Make sure that both the reference and the alternative systems fullfil the filters
Make sure that the sign for the kinetic orders calculated through the external constraints is as it should be
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Problems with the sampling
Systems with bifurcations in flux
Systems with conservation relationships
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Systems with bifurcations in flux
13 12 11 121 1 3 5 1 1 4/ g g h hdX dt X X X X X3
X1
X2
X4
X5 X6
1 211
1 2
1 2v v
ss
v g v gh
v v
v1 v2
The measure of the set of parameter values within
parameter space that is consistent (generates a steady state that is consistent with v1 and v2) is 0
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Systems with moiety conservation
11 123 4 15 11 1 21 1 7 1 5 1 1 1 8 2/
g hf f g h f fdX dt X X X X X X X3
X1
X2
X4
X5 X6
3 4
3 2 7 1
3 42 30 7 10
3 7 30 4 10 30
/
/ /
f f
f f
X X X
X X X
f X X f X X
The measure of the set of parameter values within
parameter space that is consistent (generates a steady state that is consistent with v1 and v2) is 0
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Consistent sampling
Sampling Result Space
Sampling without approximating moiety relationships or aggregating fluxes (AMRAF)
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Sampling result space
i-2i-n
Random number generator
L1L’1
Sample in Log space
X5X6, X1,X2,X3,X4
Random number generator
[-L’’1,X5,L’’’1], ...
Sample in Log space
gg2Random number generator
[-5,g1,0], [0,g1,5] ...Sample
N rate constants are left to be calculated from the values of the remaining sampled parameters
and variable
N is the number of equations in the ODE system
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Sampling GMA systems
13 121
114112 11112 11
1/ 7 1 5
1 1 8 2
g g
hh h
dX dt X X X
X X X X
Using GMA form/Don’t
approximate moeity
Sample & Solve Steady State Numericaly
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Effects of constraints on parameter values
Using this type of filters allows
Studying which physiological contrainst are important in selecting the range of values for a given parameter
Studying how different contrainst interact with each other to generate a given parameter value distribution
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Effect of filters on output parameter distribution
Parameter
High gains
Parameter
Stable SS
Bothf f
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Effect of input ditributions on output distributions
Parameter Parameter
Filters
Parameter
Filters
Parameter
f
f f
f
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Effects on parameter distributions
Uncontrained SamplingFully Contrained Sampling
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Analyzing the results
Set of parametervalues
Set of Steady State properties
Reference
Set of Ratios
Property
Rat
io
1
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Using point measures
Property
Rat
io
1
Compare Means, Medians, sd,
quantiles
Alternative System Reference System
Reference system
has higher values
Reference system
has lower values
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High Threshold
Using distributions
Property
Rat
io
1
Property, R
f
Property, A
f
Property, A
f
Property, R
f
Low Threshold
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Moving median plots
Property
Rat
io1
Property
Rat
io
1
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Effect of input ditributions on properties and ratios
Parameter
fCalculation
Parameter
f Calculation
1
Property
Rat
io
1
Property
Rat
io
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Sensor logarithmic gains
Y-Axis: Property in Reference system
Property in Alternative system
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Regulator logarithmic gains
Y-Axis: Property in Reference system
Property in Alternative system
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Sensitivities
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Stability
Y-Axis: Property in Reference system
Property in Alternative system
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Comparing transient times
Compare
31 34 32 33 363 3 1 4 3 2 3 6
...
...
g g h h hX X X X X X '34 32 33 363 3 4 3 2 3 6
...
'
...
g h h hX X X X X
Numerically Solve ODEs
Numerically Solve ODEs
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Response times
Y-Axis: Property in Alternative system
Property in Reference system
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Quantifying decided criteria
Average signal amplification → Bifunctional larger (up to 10%)
Average cross-talk amplification → Bifunctional smaller (up to 4%)
Average margins of stability → Bifunctional smaller (up to 4%)
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Quantifying undecided criteria
Average Sensitivities → Difference smaller than 0.5%
Average transient responses → Bifunctional faster up to 10%
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Summary
Control ComparisonsAnalyticalStatistical
Two component systemsBifunctional sensor better at buffering against
cross talkMonofunctional sensor absolutely stable and
better integrator of cross talk.
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Bibliography
Alves & Savageau 2000, 2001, Bioinformatics. Alves & Savageau 2003, Mol Microbiol. Schwacke & Voit 2004 Theor Biol. Med.
Modelling
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A note on hysteresis
Signal
Res
pons
e
Unstable steady state
At least three steady states must coexist for hysteresis to be a possibility
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Hysteresis in classical TCS
The module with a monofunctional sensor has a steady state that is absolutely stable
The module with a bifunctional sensor has unstable steady states→ Hysterisis?
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m=1
n=1
1 5 7 1 11 1 8 2 12 1
26 2211 1 8 2 22 6 8 2 21 2 7 1 22 2
8 2 4
7 1 3
0
0
m
ng h
X X X X X X X
X X X X X X X X X X
X X X
X X X
1 5 71
1 5 11 8 2 12
261 5 711 8 2 22 6 8 2
1 5 11 8 2 12
1 5 721 2 7 22 2
1 5 11 8 2 12
8 2 4
7 1 3
0 g
X XX
X X X
X XX X X X X
X X X
X XX X X
X X X
X X X
X X X
21 1 0a X bX c
At most 2 steady states
Hysteresis requires 3 steady states
Therefore, no hysteresis
Finding the steady state
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1 5 7 1 11 1 8 2 12 1
2611 1 8 2 22 6 8 2 21 2 7 1 2 22 2
8 2 4
7 1 3
0
0 /
m
g
X X X X X X X
X X X X X X X X X K X X
X X X
X X X
Finding the steady state
1 5 71
11 8 2 12 1 5
261 5 711 8 2 22 6 8 2
11 8 2 12 1 5
1 5 721 2 7 2 22 2
11 8 2 12 1 5
8 2 4
7 1 3
0
/
g
X XX
X X X
X XX X X X X
X X X
X XX X K X X
X X X
X X X
X X X
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Finding the steady state
3 22 2 2 0a X bX cX d
Three positive non-multiple roots must exist if hysteresis exists
261 5 711 8 2 22 6 8 2
11 8 2 12 1 5
1 5 721 2 7 2 22 2
11 8 2 12 1 5
/ 0
gX XX X X X X
X X X
X XX X K X X
X X X
a, b, c and d are sums and differences of products of positive parameters and independent variables
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Analysis of the roots
1 2 3
3 23 2 1 1 2 2 3 1 3 1 2 3( ) ( )
X r X r X r
X X r r r X r r r r r r r r r
If all roots are real and positive, the coefficients have alternating signs
Necessary but not sufficient condition (2 negative roots can have the same pattern, depending on their
values)
_+_+
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g268 22 6 1 5 12 11 8 1 5 11 7d = KX X X + X + X X
Finding the steady state
0 ?
? 0
Sign a Sign c
Sign c Sign d
g2611 22 6 22a = - X + 1Sign a
b = BIG MESS Sign b depends on parameter values
c = BIG MESS Sign c dependson parameter values
No alternating signs No three steady states
No hysteresis
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No hysteresis in TCS
Thus, neither the monofunctional nor the bifunctional module can, in principle exhibit hysteresis
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Summary
Control ComparisonsAnalyticalStatistical
Two component systemsBifunctional sensor better at buffering against
cross talkMonofunctional sensor absolutely stable and
better integrator of cross talk.
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Acknowledgments
Mike Savageau Albert Sorribas Armindo Salvador
PGDBM JNICT FCT Spanish Government Portuguese Government NIH (Mike Savageau) DOD (ONR) (Mike Savageau)
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Sampling without AMRAF
13 151
11 141
1/ 3 5
1 4
g g
h h
dX dt X X
X X
13 121
112 111 11412 11
1/ 3 5
1 1 4
g g
h h h
dX dt X X
X X X
Sample & Solve Steady State Numericaly
approximating moiety relationships or aggregating fluxes
S-system form without
approximating Moiety conservation
relationships
Using GMA form/Don’t approximate moeity
Sample & Solve Steady State Numericaly