Team:Michigan/Modeling

From 2013.igem.org

(Difference between revisions)
(SimBiology)
(Analytical modeling)
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     • naturally standardized
     • naturally standardized
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ERSESCO algorithm
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=ERSESCO algorithm=
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The ERSESCO algorithm the Michigan team developed provides a clearly defined method of using symbolic analysis on systems. The algorithm has seven major steps:
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  1. equation
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1. equation
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  2. reduction
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2. reduction
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  3. solution
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3. solution
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  4. equilibration
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4. equilibration
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  5. stabilization
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5. stabilization
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  6. calibration
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6. calibration
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    7. optimization
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7. optimization
===SimBiology===
===SimBiology===

Revision as of 01:28, 28 September 2013

Xayona Website Template

Best model.jpg

Contents

Introduction

Switch modeling

• produce both states of switch

• use model to optimize switch

Mass action modeling

Definition

• differential equations

• each equation is a sum of rates

• each rate is proportional to each of its reactants

Benefits

• can minimize assumptions

• already used in chemical rate equations

• standardizes equations and their analysis:

Analytical modeling

• benefits

   • given parameters, it precisely predicts behavior
   • accurately predicts the data needed to determine parameters (unlike numerical analysis)
   • allows avoidance of numerical errors like rounding
   • naturally standardized

ERSESCO algorithm

The ERSESCO algorithm the Michigan team developed provides a clearly defined method of using symbolic analysis on systems. The algorithm has seven major steps:

1. equation
2. reduction
3. solution
4. equilibration
5. stabilization
6. calibration
7. optimization

SimBiology

• is a MatLab package provided to iGEM teams

• provides a numerical approximation

• easily creates from a diagram

Model of Recombinase Expression

The Recombinase Expression Model describes the synthesis and degradation of any protein in the cell. Here, the model is used in particular to describe the synthesis and degradation reaction rates ksyn and kdeg of recombinases in the cell. The model also predicts the recombinase concentration.

Model of the Switch

The Switch Model describes how the switch equilibrates when the concentrations of the recombinases FimE and Hbif approach constant values. This model assumes that each recombinase catalyzes a one way reaction. This model predicts the degree of cooperativity n and m in the binding reaction of the recombinase to DNA for FimE and Hbif, respectively. Furthermore, this model predicts the forward catalysis rate constants, kF and KH, for FimE and Hbif.

Model of Inducible Hbif

The Inducible Hbif Model describes how the switch flips when acted upon by the Lux/HSL-controlled expression of Hbif. In this model, [HSL] represents the concentration of the species HSL:pLux:LuxR, the complex that promotes the expression of Hbif. In this model [HSL] is a function of pLux and LuxR expression levels in the cell, the amount of HSL added, and the fractional occupancies at the complexation equilibrium. It can be assumed that this complexation reaction happens instantaneously with respect to the slow, rate-limiting translation step of Hbif. This model predicts the rate k1 of Lux-induced expression of Hbif, the degradation rate kdeg of Hbif, the forward and reverse rates k2 and k-2 of Hbif catalysis on the switch, and the equilibrium constant K2 for the switch when acted upon by Hbif.

Expression Model

ExpressionModelFigure.png

Equation:

ExpressionModel1.png

Reduction: None

Solution:

ExpressionModel2.png

Equilibration:

ExpressionModel3.png

Stabilization:

ExpressionModel3-1.png

Calibration:

ExpressionModel4.png
ExpressionModel5.png

Optimization:

ExpressionModel6.png

SimBiology:

Expression.jpg

Expression Model Derivation

Switch Model

SwitchModelFigure.png

Equation:

SwitchModel1.png
SwitchModel2.png

Reduction:

SwitchModel3.png

Solution:

SwitchModel3-1.png

Equilibration:

SwitchModel4-1.png

Stabilization:

SwitchModel4-2.png

Calibration:

SwitchModel5-1.png
SwitchModel5-2.png

Optimization:

SwitchModel6.png

SimBiology:

Switch.jpg

Switch Model Derivation

Inducible Hbif Model

InducibleHbifModelFigure.png

Equation:

InducibleHbifModel1.png
InducibleHbifModel2.png

Reduction:

InducibleHbifModel3.png

Solution:

InducibleHbifModel4.png

Equilibration:

InducibleHbifModel5.png

Stabilization:

InducibleHbifModel5-2.png

Calibration:

InducibleHbifModel6-1.png
InducibleHbifModel6-2.png
InducibleHbifModel6-3.png

Optimization of Hbif:

InducibleHbifModel7-1.png

Optimization of ON:

InducibleHbifModel7-2.png

SimBiology:

InducedHbif.jpg

Inducible Hbif Model Derivation

Future Directions

Data!

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