Team:Montreal/Project

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Revision as of 18:31, 2 August 2008

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Experimental

To create our functional bacteria, we intend to transform a line of MC4100 E. coli cells used by Elowitz with two plasmids. The first plasmid will contain the Repressilator (λcI, TetR and LacI), described in his 2000 paper. The second plasmid will be a triple-ligation of the I₁₄₀₀₁ bio-brick, the J₄₀₀₀₁ bio-brick and the Elowitz reporter (TetR, YFP).

If successful, we intend to observe and experiment with our cells with fluorescence microscopy and spectrophotometry. Using three individual filters, we can microscopically observe the oscillations of individual components by the three fluorescent proteins (ECFP, GFP and RFP) embedded in the system.

Project Objectives

Cloning Plan

Plasmid_A: λcI + TetR + LacI (Elowitz Repressilator)

Sent by Dr. Michael Elowitz (Caltech).

Plasmid_B: I₁₄₀₀₁ + J₄₀₀₀₁ + TetR/YFP (Elowitz Reporter) + kan⁺

1. Cut J-brick genes out of plasmid with XbaI/SpeI

2. Ligate to J-brick genes to I-brick plasmid cut with XbaI (+dephosphorylation)

3. Select on amp⁺/kan⁺ plates (I-brick plasmid carries resistance for both ampicillin and kanomycin)

4. Cut of I and J-brick genes with EcoRI

5. Ligate genes to Elowitz reporter plasmid cut with EcoRI (3-way ligation)

6. Select on kan⁺ LB plates

Modeling

The modeling effort attempts to simulate a repressilator network using a system of coupled differential equations. The repressilator itself is modelled as a loop of three proteins which inhibit each other's growth. Each cell in the network contains a repressilator, and the cells communicate amongst themselves by exchanging an auto-inducer molecule, which feeds back the repressilator loop.

The model is assuming continuous levels of the different proteins, as well as a continuous spatial distribution. The model attempts to take into account a sparse, heterogeneous distribution of cells with depletion of the autoinducer molecule and leakage.

For this purpose, the model is currently being coded up in Mathematica. The simulations, based on that continuous model, are generated using xCellerator and NDelayDSolve for different cell configurations; the results obtained are graphs of the concentration of molecules in the system versus time. That is, our interest lies in the phase difference between clusters of cells. As of now, a low number of cells is being used for testing, but a higher one will be reached later.

For the simulation, we are currently using Mathematica, along with the xCellerator and NDelayDSolve packages.

Future Applications

Pacemaking Technology

Once of the better known sinusoidal oscillators in the human body are the cells of the sinoatrial node of the heart that establish a regular rhythm of action potentials that propagate throughout the atria and ventricles to generate beats. While current artificial pacemakers focus primarily on re-establishing this rhythm by generating electrical potentials, a biological alternative could prove more effective and less invasive than its mechanical counterpart with further research.

Continuous Cultures in Industrial Bio-Reactors

A common problem in bio-reactors used by pharmaceutical and biotechnology companies results from difficulties in growing cells in continuous cultures due to various complications in recycling nutrients and draining metabolites. An effectively oscillating system could reduce the reliance on current fed-batch systems by allowing more effective cycles of cell growth and protein expression.

Biological Drug Delivery

With biological alternatives now being increasingly explored as mechanisms of delivering therapeutics, a functionally oscillating system could prove invaluable to tailoring drug regimes to specific systems. Innumerable biological processes function in rhythmic on/off switches and being able to control the release of certain cellular components to such a schedule may permit more effective treatment.•

@@ Line 38: / Line 38: @@
-==Modelling==
+==Modeling==
-Using coupled differential equations, we are modeling the repressilator, which is a network of three genes, whose product proteins are repressing each other's growth. This cycle is taking place in each of a colony of cells, who communicate amongst themselves by exchanging an autoinducer molecule. The model attempts to take into account a sparse, heterogeneous distribution of cells with depletion of the autoinducer molecule and leakage.
+The modeling effort attempts to simulate a repressilator network using a system of coupled differential equations. The repressilator itself is modelled as a loop of three proteins which inhibit each other's growth. Each cell in the network contains a repressilator, and the cells communicate amongst themselves by exchanging an auto-inducer molecule, which feeds back the repressilator loop.
+The model is assuming continuous levels of the different proteins, as well as a continuous spatial distribution. The model attempts to take into account a sparse, heterogeneous distribution of cells with depletion of the autoinducer molecule and leakage.
 For this purpose, the model is currently being coded up in Mathematica. The simulations, based on that continuous model, are generated using xCellerator and NDelayDSolve for different cell configurations; the results obtained are graphs of the concentration of molecules in the system versus time. That is, our interest lies in the phase difference between clusters of cells. As of now, a low number of cells is being used for testing, but a higher one will be reached later.
+For the simulation, we are currently using Mathematica, along with the xCellerator and NDelayDSolve packages.
 ==Future Applications==