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http://creativecommons.org/licenses/by-sa/2.0/
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Design Principles in Systems Molecular Biology
Prof:Rui [email protected]
973702406Dept Ciencies Mediques Basiques,
1st Floor, Room 1.08Website of the Course:http://web.udl.es/usuaris/pg193845/Courses/Bioinformatics_2007/
Course: http://10.100.14.36/Student_Server/
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Outline
What are design principles
How to study design principles
Examples
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What are design principles?
Recurrent qualitative or quantitative rules that are observed in similar types of systems as a solution to a given functional problem
Exist at different levelsNuclear Targeting Sequences
Operon
Gene 1 Gene 2 Gene 3
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Outline
Design Principles in Network Topology Overall Feedback Signal Transduction Gene Circuits
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Regulation by overall feedback
X0 X1
_
+
X2 X3
X4
X0 X1
+
X2 X3
X4
___
Overall feedback
Cascade feedback
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Why overall feedback
Why is overall feedback so prevalent? Hypothesis:
Random thing
Alternative hypothesis: There are functional advantages to this type of
overall feedback that led to its selection and account for its maintenance
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How to test the alternative hypothesis?1 – Identify functional criteria that have physiological relevance
X0 X1
_
X2 X3
+
X4
Appropriate Flux
Flux Responsive to Demand
Low concentrations
Low gains with respect to supply
Low sensitivities to parameter fluctuations
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How to test the alternative hypothesis?1 – Identify functional criteria that have physiological relevance
X0 X1
_
X2 X3
+
X4
Time
[X3]
Change in X4
Fast transient response
Stable steady state
Fluctuation in X3
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Functionality criteria for effectiveness
Low concentrations Appropriate fluxes Sharp flux regulation by demand Low log gains to supply Low sensitivities to parameter changes Fast transient responses Large margins of stability
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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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A model with overall feedback
10 13 111 1 0 3 1 1/ g g hdX dt X X X
11 222 1 1 2 2/ h hdX dt X X
X0 X1
_
+
X2 X3
X4
22 33 343 2 2 3 3 4/ h h hdX dt X X X
Constant
Protein using X3
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A model without overall feedback
'10 111 1 0 1 1/ ' g hdX dt X X
11 222 1 1 2 2/ h hdX dt X X
X0 X1
+
X2 X3
X4
22 33 343 2 2 3 3 4/ h h hdX dt X X X
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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 determined in 1
Comparison must be made appropriately
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Mathematicaly Controlled Comparison10 13 11
1 1 0 3 1 1/ g g hdX dt X X X
11 222 1 1 2 2/ h hdX dt X X
22 33 343 2 2 3 3 4/ h h hdX dt X X X
111 0 1 1
'101'/ g hdX dt X X
11 222 1 1 2 2/ h hdX dt X X
22 33 343 2 2 3 3 4/ h h hdX dt X X X
Internal Constraints:
All processes that are equal must have the same parameter values
External Constraints:
Parameters that are different are degrees of freedom that the system can use to squeeze out differences (e.g. mutation in catalytic power)
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Implementing external constraintsExternal Constraint 1:
Both systems can achieve the same steady state concentrations AND fluxes
Fixes 10’
Both systems can achieve the same Log gains to substrate
Fixes g10’
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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 determined in 1
Use a Mathematically controlled comparison
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Functionality criteria for effectiveness
Low Concentrations → Both Systems = Appropriate Fluxes → Both Systems = Sharp flux regulation by demand → Overall Better Low log gains to supply → Both Systems = Low sensitivities to parameter changes → Overall
Better Fast transient responses → Overall Better Large margins of stability → Overall worst
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Complications to the comparisons
More complicated models Results may depend on parameter values
Smaller models How much better or worst?
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A solution to both problems
Use Statistical mathematically controlled comparisons
Sample parameters exhaustively and use statistical methods to analyze the results
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Functionality criteria for effectiveness
Sharp flux regulation by demand → Overall Better
~5-10% Low sensitivities to parameter changes → Overall
~5-10% Better Fast transient responses → Overall Better
~5-10% Large margins of stability → Overall worst
=<1%Alves & Savageau 2000,a,b; 2001 Bioinformatics; 2000, 2001 Biophysical Journal
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Outline
Design Principles in Network Topology Overall Feedback Signal Transduction Gene Circuits
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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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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 '34 32 33 363 3 4 3 2 3 6
...
'
...
g h h hX X X X X
A
Q
A
Q
A
Q
A
Q
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Physiological Predictions
Bifunctional design lowers Q2 signal amplification prefered when cross-talk is undesirable
Monofunctional design elevates Q2 signal amplification prefered when cross-talk is desirable.
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Predicting Monofunctionality from structure
Alves & Savageau 2003 Mol. Microbiol.
~1000 sequences from genomic data of dozens of bacteria
Bifunctional Sensor
Monofunctional Sensor
Differences in ATP lid
100s predicted structures by modeling
25 monofunctional sensors
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A new design principle
04/20/23 27
Existence of a dead end complex and of a flux channel for the dephosphorylation of the RR that is independent of the sensor allow for TCS that have bistable responses.
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A new design principle
04/20/23 28
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Outline
Design Principles in Network Topology Overall Feedback Signal Transduction Gene Circuits
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Dual Modes of gene control
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Demand theory of gene control
Wall et al, 2004, Nature Genetics Reviews
• High demand for gene expression→ Positive Regulation
• Low demand for gene expression → Negative mode of regulation
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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)