Team:Montreal/Project

From 2008.igem.org

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===Biological Drug Delivery===
===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.•
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.•
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==Modelling==
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[[Image:Theoteam1.jpg|right|frame|Theorists: Vincent Quenneville-Bélair and Alexandra Ortan]]
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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.
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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.
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{|align="justify"
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|[[Image:visualisation.png|right|frame|Although the cells are shown using a Voronoi graph, the cells are represented internally by points.]]
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|[[Image:px.png|right|frame|Simulation using Mathematica of the Repressilator Network with four cells shown on the left. The plot shows the concentration of AI against time (in arbitrary units).]]
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|}

Revision as of 18:42, 31 July 2008

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Contents

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

PlasmidA: λcI + TetR + LacI (Elowitz Repressilator)

  • Sent by Dr. Michael Elowitz (Caltech).

PlasmidB: 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

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.•

Modelling

Theorists: Vincent Quenneville-Bélair and Alexandra Ortan

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.

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.


Although the cells are shown using a Voronoi graph, the cells are represented internally by points.
Simulation using Mathematica of the Repressilator Network with four cells shown on the left. The plot shows the concentration of AI against time (in arbitrary units).