Team:Paris/Analysis/Construction2

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{{Paris/Header|Model Construction}}
{{Paris/Header|Model Construction}}
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== Motivation ==
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== Construction ==
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We are interested to understand the factors that account for an oscillating behaviour at population level. We build two models to study systems that are equipated with quorum sensing capabilities but that relay on different designs principles. The proposed models are:
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We are interested to understand the factors that account for an oscillating behaviour at population level. We build two models to study systems that are equipated with quorum sensing capabilities but that relay on different designs principles.  
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== Modeling Alternatives ==
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The proposed models are:
*In the one model, we use a modular design. We consider that the core system is one of the modules of the system. In adittion, the other module is an two gene oscillator system presented in [[Team:Paris/Modeling/BOB#Bibliography|[Garcia-Ojalvo]]] that accounts for quorum sensing. We call this alternative the 'bimodular system'.
*In the one model, we use a modular design. We consider that the core system is one of the modules of the system. In adittion, the other module is an two gene oscillator system presented in [[Team:Paris/Modeling/BOB#Bibliography|[Garcia-Ojalvo]]] that accounts for quorum sensing. We call this alternative the 'bimodular system'.

Revision as of 11:19, 28 October 2008

Model Construction


Contents

Construction

We are interested to understand the factors that account for an oscillating behaviour at population level. We build two models to study systems that are equipated with quorum sensing capabilities but that relay on different designs principles.

Modeling Alternatives

The proposed models are:

  • In the one model, we use a modular design. We consider that the core system is one of the modules of the system. In adittion, the other module is an two gene oscillator system presented in [Garcia-Ojalvo] that accounts for quorum sensing. We call this alternative the 'bimodular system'.
  • In the other model, namely the 'unimodular system', we rewire the architecture of the core system to have the desired functionality of the system in a single circuit.

Both the bimodular and unimodular models describe events that happend not only at the cellular level (as in the core system) but also at the population level due to interactions needed bettwen a cell and its environment during quorum sensing.

In the following sections, we first describe the population modeling (the common part amoung our two proposed models) to then focus our attention to the characteristics that are exclusive to each of the modeling alternatives.

Description

Common Dynamics: Chemostat
The variation of cells' concentration in the chemostat over time can be expressed in terms of a production (positive) term and degradation (negative) terms:
Final chemostat.png
    For the production term, we use a logistic equation to model cell growth, according to standard assumptions. The behaiviour obtained is the following one: at low population density, the concentration of cells in the chemostat (c) increase exponentialy with a growth rate αcell and at high population density, the population reaches a maximum concentration, cmax.
    For the degradation term, we consider that c decrease proportionaly to both a dilution phenomena cause by the renewal of the medium in the chemostat (Drenewal) and cell death (d).
Common Dynamics: Quorum Sensing
In order to model the quorum sensing dynamics, we consider that:
     1) Inside a cell, the HSL concentration increases proportionaly to the concentration of LasI and decreases according to both a degradation term (proportional to the internal HSL concentration) and a transport term (proportional to the difference beetwen the internal and external concentration of HSL). Thus, the equation for the internal HSL concentration is:
Final HSL.png
     2) Outside the cells, HSL is cumulated with the same transport term tah we use in the previous equation. The degradation of HSL in the external medium and the dilution controled via the chemostat accounts for HSL external decrease. So the external HSL concentration is given by:
HSLext.png
that is equivalent to:
HSLext2.png
where    Hsl average.png    and      Cell number volume.png
Common Network Dynamics: FlhDC and Flia
FlhDC and Flia are regulated in the same way in both systems. FlhDC is produced under the influence of EnvZ via an inhibition. Flia is regulated for its self and FlhDC.
FlhDC-FliaEq.png
Bimodular System Unimodular System
550pxCore system coupled with an oscillator 550pxModified core system that accounts for quorum sensing
     a) The expression of lasI is under the control of the same promotor that used for FlhDC.      a) The expression of lasI is regulated by FlhDC and Flia (as in the core system).
LasIEqInBIMOdularSys.png LasIEqInUNIModularSys.png
     b) The expression of envZ depends on both the activation from FlhDC and Flia (as in the core system) and the concentration of HSL present in the cell.      b) The expression of envZ varies as a function of the concentration of HSL present in the cell.
EnvZInBIMOdularSys.png EnvZInUNIModularSys.png




Parameters Search

The following table sumarize our findings. The parameters' values that are used during the simulations. Most them are found in literature others are obtained from further analysis.

Parameters
 
Chemostat Parameter Meaning Original Value Normalized Value Unit Source
αcell Growth rate 0.0198 1 min-1 wet-lab
cmax Carrying capacity for cell growth 0.1 0.1 µm3 [3]
Drenewal Dilution rate 0.00198 0.1 min-1 wet-lab ([3])
d Death rate 0.0099 0.5 min-1 wet-lab
 
Quorum Sensing Parameter Meaning Original Value Normalized Value Unit Source
γHSL Degradation rate 0.0053 0.2690 min-1 wet-lab
γHSLext

Degradation rate 0.0106 0.5380 min-1 [6]
βHSL Production rate 0.3168 16 min-1
η Diffusion rate 10 505 min-1 [2]
nHSL Hill coefficient   4   [3]
θHSL Hill characteristic concentration for the second operator   0.5 c.u [3]