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http://creativecommons.org/licenses/by-sa/2.0/
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/
Outline
What are design principles
How to study design principles
Examples
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
Outline
Design Principles in Network Topology Overall Feedback Signal Transduction Gene Circuits
Regulation by overall feedback
X0 X1
_
+
X2 X3
X4
X0 X1
+
X2 X3
X4
___
Overall feedback
Cascade feedback
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
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
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
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
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
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
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
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
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)
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’
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
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
Complications to the comparisons
More complicated models Results may depend on parameter values
Smaller models How much better or worst?
A solution to both problems
Use Statistical mathematically controlled comparisons
Sample parameters exhaustively and use statistical methods to analyze the results
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
Outline
Design Principles in Network Topology Overall Feedback Signal Transduction Gene Circuits
Alternative sensor design in Two Component Systems
S
S*
R*
R
Q1 Q2
Monofunctional Sensor Bifunctional Sensor
S
S*
R*
R
Q1 Q2
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
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.
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
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.
A new design principle
04/20/23 28
Outline
Design Principles in Network Topology Overall Feedback Signal Transduction Gene Circuits
Dual Modes of gene control
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
Acknowledgments
Mike Savageau Albert Sorribas Armindo Salvador
PGDBM JNICT FCT Spanish Government Portuguese Government NIH (Mike Savageau) DOD (ONR) (Mike Savageau)