Team:Paris/Modeling/BOB

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and solving gives:
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thus the conclusion :  
thus the conclusion :  

Revision as of 14:44, 29 August 2008

BOB (Based On Bibliography) Approach


Bob marley.jpg

Contents

Introduction

  • The key problem with a dynamic system consists in the fact that adding a new equation gives a more detailed idea of the overall process, but one looses meaning by doing so since new undefined parameters appear. Hence, it becomes hard to link those parameters with a biological meaning and reality.
  • In that respect we chose to build at first a simplified model, which would enable us to help us understand the processes implied in the system. In that respect, we chose not to introduce the mRNA state between transcription and translation in our model. We presented things as if a protein would act directly upon the following. Furthermore, we assumed that describing routinely every step, would only force us into finding more parameters.
  • This preliminary approach is mainly based on a bibliographical work. In fact, it is important to understand that with this approach, we did not intend to design the most accurate model possible. We found essential to choose only parameters that we could determine or control in the wet lab. The main goal was to get quickly a first idea of the way our system could behave.
  • The goal here is to present the differential equations we used in the model. At each step we shall describe why we chose these precise equations, the drawbacks and possible improvements, the parameters involved and eventually a biologically coherent value.

Model

  • Here is an overall view of the whole system :
Globalmodel.jpg

Steps of the Model

We chose to broke up this system in three sub systems for presentation clarity

  • Population Growth : we wished to model the population evolution in the chemostat, since our experiments will be carried out in these conditions.
  • First Subsystem
    Subsystem1.jpg
    • FliA behind pFliA= f(FlhDC,FliA)
    • CFP behind pFliL = f(FlhDC,FliA)
    • YFP behind pFlgA = f(FlhDC,FliA)
    • RFP and LasI behind pFlhB = f(FlhDC,FliA)

(See also our FIFO module)

  • Second Subsystem
    Subsystem2.jpg
    • HSL = f(LasI) or f(LuxI) depending on which system is being used
    • HSL ⇋ HSLext
    • TetR = f(HSL)

(See also our Synchronisation module)

  • Third Subsystem
    Subsystem3.jpg
    • FlhDC = f(TetR)
  • In each subsystem we will describe the way we decided to normalize each protein resulting from genic expression. In fact, in every case we normalize the concentration by their maximum values, in order to keep the influence relations that rule the system behavior. This will enable us to compare the different concentration evolutions, and interpret the data we get from our simulations.

Population evolution

  • First of all, it seems important at this stage to present the way we interpret the evolution of the population in the chemostat. We want to impose to our model the fact that the rate of production has to be proportional to the existing population and to the amount of available resources. Hence a logistic model of the population, where c denotes the concentration of cells in the medium:


Population evolution 2.jpg


  • Then, we need to add the death term, and a dilution term cause by the renewal of the chemostat. Finally we get :


Population evolution full 2.jpg


where cmax is the carrying capacity for cell growth, and D renewal.jpg the dilution rate, d the death rate constant

First Subsystem

Steps involved

  • FliA behind pFliA= f(FlhDC,FliA)
  • CFP behind pFliL = f(FlhDC,FliA)
  • YFP behind pFlgA = f(FlhDC,FliA)
  • RFP and LasI behind pFlhB = f(FlhDC,FliA)

Mathematical model

  • Classically we use the following equation to model gene interactions :


Classical equation.jpg


where [Y] denotes the concentration of Y protein and γ its degradation rate (which unit is time-1).

  • Yet, the flagella gene network has been thoroughly studied in [1]. We used two major results presented in this study. Firstly, Shiraz Kalir and Uri Alon came up with the fact that the promoters of class 2 genes, among which fliL, flgA and flhB, behaved like SUM-gate functions with flhDC and fliA inputs. Secondly, their experiments proved that these influences could be considered as linear. Thus the following model:


Promoter Activity.jpg


β and β’ represent the relative influence of flhDC and fliA respectively, the units of β and β’ being time-1.

  • Furthermore, they came up with numerical values of β and β’ for each gene, which fitted quite well to their experiments. We then decided that we could use those values as well in our model.



  • Finally, here are our final equations:


FliA dynamics.jpg
CFP.jpg
YFP.jpg
RFP.jpg


Discussion

Normalization

  • First of all, we wanted to have a proper time scale. We the set the degradation rates, γ ,to 1. It is important to note that this degradation rate is represent the influence of the degradation and dilution. We assume that the degradation can be neglected compared to the dilution caused by the cell growth. Then we have:


Gamma Expression.jpg


  • Since we can know easily the value of the real half-time, we may know the real timescale out of our computations. We kept the designation “degradation rate” for convenience, so as not to mix up with the dilution that occurs with the HSL in the synchronisation step.
  • We kept the β and β’ values found by S. Kalir and U. Alon, since they showed the relative influence of flhDC and fliA. To have the same order of magnitude between each specie, we normalized those parameters between 0 and 1 as following. We reasoned independently for each equation, wishing to set the equilibrium values of the concentration to 1 given input values of 1. This gave:
Beta Resize.jpg


Beta p Resize.jpg


  • In fact, if we take CFP for example:
CFP.jpg

The maximum of [CFP] is reached when [fliA] = 1 and [flhDC] = 1 ; when we solve with these condidtions, we obtain :

CFP Solve.jpg

Then setting the equilibrium value of [CFP] to 1 corresponds to setting

Beta Gamma resize.jpg
  • The analysis of fliA is different, but not the result:
FliA Analysis.jpg

With an input of flhDC equal to 1, the solution of the differential equation is:

FliA Solve.jpg

And the condition on the equilibrium imposes

Beta Gamma Resize FliA.jpg
  • To conclude, we see that we always get the same condition:
Final Resize.jpg
  • Finally, since we had imposed γ=1 we resulted with β+β'=1.

Which gene goes were?

  • The key element to understand is that fliA stands as an accelerator. The first influence is caused by flhDC, and fliA gives the last burst. fliA also has a great influence in the fading phase, since it introduces a delay (result found by experiments in [1]). This is what determined our choice in the order of fliL, flgA and flhB. We wanted to put in first the gene that was the least influenced by fliA, and in the end the gene the most influenced by fliA.


Graph No FliA 2.jpg
Graph FliA.jpg


  • In fact, this enabled us to force the curves into crossing themselves. In addition, the delay imposed by fliA in the decreasing phase, would permit us to widen the delays between the first and the last gene expression, thus getting better conditions to observe the FIFO in the wet lab.

Second Subsystem

Steps involved

  • HSL = f(LasI) or f(LuxI) depending on which system is being used
  • HSL ⇋ HSLext
  • TetR = f(HSL)

Mathematical Model

Dynamics of HSL

First and foremost, HSL is the key element in the synchronisation process: we used a diffusive specie in order to balance a value between cells. Thus, the HSL concentration denoted [HSL] become this "shared" value, the outside medium concentration denoted [HSLext] being an intermediate value.

We use the classical osmosis term η x ( [HSL] - [HSLext] ) where η = membrane permeability x ( area 1cell / volume 1cell ) is the diffusion rate expressed in time-1.

The degradation rate is linear: - γ x [HSL].

To model the production, we were at first inspired by [2] where it is described as being linear: β x [LasI] where β is a given constant value. However, the results presented in this study were introduced as only being theoretical so we chose to model the creation of HSL by a Michaelis Menten expression (because there is no known cooperativity).

The final expression where ΘHSL is expressed in concentrations unit and βHSL in (concentration * time) unit is:
HSL final choice 2.jpg

It is important to note that we took into account the fact that lasR/(lasR linked to HSL) was a function of HSL. However, we assumed that the fraction of HSL bound to lasR would not influence HSL concentration; this is the reason why, no “degradation due to binding" term appears in the HSL model.

Dynamics of HSLext

The evolution of HSLext may be described with three terms. First of all, there is a dilution rate, Drenewal, due to the renewal of the chemostat. There is the usual degradation rate,γ[HSL]ext ).

HSLext sum 2.jpg

Which gives

HSLext final 2.jpg

where

Cell number volume.jpg

TetR=f(HSL)

  • Here we had the choice to model the complexation phase of HSL and lasR, and then the binding phase of this complex on the pLas promoter. We assumed that those stages were short, and then decided to model them by a single step.
  • We checked in the registry that the pLas sequence was the same as in [promoter specificity]. It is explained that there are two operators, denoted OP1 and OP2. The Hill parameters were detailed in this article, hence our modeling choice :
TetR final.jpg

Discussion : normalization

  • In this subsystem, we decided not to normalize the HSL concentration, since HSLext is to be understood as a global variable.
  • Nevertheless, it is coherent to normalize [tetR]. Likewise we set out time-scale by setting the degradation rates to the value of 1 in the previous equations, we set γtetR to 1. Following the same train of thought as in the first subsystem, if we consider the equilibrium hypothetical stage when [HSL] concentration is fully expressed, we deduce that :
Beta limit.jpg

and solving gives:

TetR solve.jpg

thus the conclusion :

Beta Tet.jpg

Third Subsystem

Mathematical Model

  • FlhDC = f(TetR)
  • Here we chose to understand aTc as a regulation and control variable, that will help us influence our parameters in the wet lab. Therefore, [aTc] does not appear in this model.
  • For this last step, in addition to the usual degradation term, we simply chose a complete Hill function to describe the dynamics of flhDC, because this is the way it is commonly described in the literature:
FlhDC dynamics final.jpg

Discussion : Normalization

  • Likewise the previous analysis, we set γlflhDC to 1. Then, since FlhDC is fully expressed when TetR is not, we see that when solving under this conditions, we get
FlhDC norm.jpg

hence the need to set

Beta flhDC.jpg

Parameters summary

Parameters Table

(Under Construction)

Parameter Table
Parameter Parameter in code (cell i) Meaning Original Value Normalized Value Unit Source
αcell Growth rate min-1 wet-lab
cmax Carrying capacity for cell growth
Drenewal Dilution rate
d Death rate 0.0198 1 min-1 wet-lab
γFliA g[1 + 8 x (i-1)] Degradation rate 0.0198 1 min-1 wet-lab
βFliA B(1,i) FlhDC activation coefficient 50 0.1429 min-1 [1]
β'FliA b(1,i) FliA activation coefficient 300 0.8571 min-1 [1]
γCFP g[2 + 8 x (i-1)] Degradation rate 0.0198 1 min-1 wet-lab
βCFP B(2,i) FlhDC activation coefficient 1200 0.8276 min-1 [1]
β'CFP b(2,i) FliA activation coefficient 250 0.1724 min-1 [1]
γYFP g[3 + 8 x (i-1)] Degradation rate 0.0198 1 min-1 wet-lab
βYFP B(3,i) FlhDC activation coefficient 150 0.3333 min-1 [1]
β'YFP b(3,i) FliA activation coefficient 300 0.6667 min-1 [1]
γRFP g[4 + 8 x (i-1)] Degradation rate 0.0198 1 min-1 wet-lab
βRFP B(4,i) FlhDC activation coefficient 100 0.2222 min-1 [1]
β'RFP b(4,i) FliA activation coefficient 350 0.7778 min-1 [1]
γHSL g[5 + 8 x (i-1)] Degradation rate 0.0198 1 min-1 wet-lab
βHSL B(5,i) Production rate 0,01 [2]
θHSL Hill characteristic concentration
η D(1,i) Diffusion rate 2 [2]
γHSLext

g[6 + 8 x (i-1)] Degradation rate
V1Cell Cell Volume
Vtot Total Volume
γtetR Degradation rate 0.0198 1 min-1 wet-lab
βtetR-OP1 Maximum production rate for the first operator [4]
ntetR-OP1 Hill coefficient for the first operator 1.0 [4]
θtetR-OP1 Hill characteristic concentration for the first operator 160 [4]
βtetR-OP2 Maximum production rate for the second operator
ntetR-OP2 Hill coefficient for the second operator 1.5 [4]
θtetR-OP2 Hill characteristic concentration for the second operator 8.9 [4]
γflhDC g[8 + 8 x (i-1)] Degradation rate 0.0198 1 min-1 wet-lab
βFlhDC Maximum production rate
nFlhDC Hill coefficient
θFlhDC Hill characteristic concentration

Remarks

  • We evaluated in the wet lab the half life time for our cells, and then calculated the degradation constants using :
Gamma Expression.jpg

The value for half-life time we found and used is 35min.

  • Drenewal // wetlab...

Bibliography

Much of our inspiration comes from four articles to which we shall refer in the next subsections :

  • [1] Shiraz Kalir, Uri Alon. Using quantitative blueprint to reprogram the dynamics of the flagella network. Cell, June 11, 2004, Vol.117, 713-720.
  • [2]Jordi Garcia-Ojalvo, Michael B. Elowitz, Steven H. Strogratz. Modeling a synthetic multicellular clock : repressilators coupled by quorum sensing. PNAS, July 27, 2004, Vol. 101, no. 30.
  • [3]Frederick K. Balagaddé, Hao Song, Jun Ozaki, Cynthia H. Collins, Matthew Barnet, Frances H. Arnold, Stephen R. Quake, Lingchong You. A Synthetic Escherichia Coli predator prey ecosystem. Molecular Systems Biology. 2008.
  • [4]M. Schuster, M. L Urbanowski, E. P. Greenberg. Promoter Specificity in Pseudomonas aeruginosa quorum sensing revealed by DNA binding of purified LasR. PNAS, November 9, 2004, vol. 101, no. 45.

A VOIR


  • [3]Nitzan Rosenfeld, Uri Alon. Response delays and the structure of transcription networks. JMB, 2003, 329, 645-654.
  • [4]Nitzan Rosenfeld, Michael B. Elowitz, Uri Alon. Negative autoregulation speeds the response times of transcription networks. JMB, 2003, 323, 785-793.
  • [5]S.Kalir, J. McClure, K. Pabbaraju, C. Southward, M. Ronen, S. Leibler, M. G. Surette, U. Alon. Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria. Science, June 2001, Vol 292.