Team:Groningen/Brainstorm/Growth Control

From 2009.igem.org

(Difference between revisions)
m (Previous contests)
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==Previous contests==
==Previous contests==
<b>Quorum Sensing</b>
<b>Quorum Sensing</b>
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:*[http://2008.igem.org/Team:Calgary_Wetware/Project#SlideFrame_1 Calgary 2008]
 
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:*[http://2008.igem.org/Team:Cambridge/Modelling Cambridge 2008]
 
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:*[http://2008.igem.org/Team:Chiba/Project Chiba 2008]
 
:*[http://parts.mit.edu/igem07/index.php/McGill McGill 2007]
:*[http://parts.mit.edu/igem07/index.php/McGill McGill 2007]
:*[http://parts.mit.edu/igem07/index.php/Chase_Simulator Turkey 2007]
:*[http://parts.mit.edu/igem07/index.php/Chase_Simulator Turkey 2007]
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:*[http://parts.mit.edu/igem07/index.php/Michigan Michigan 2007]
:*[http://parts.mit.edu/igem07/index.php/Michigan Michigan 2007]
:*[http://www.openwetware.org/wiki/IGEM:Peking/2007 Peking 2007]
:*[http://www.openwetware.org/wiki/IGEM:Peking/2007 Peking 2007]
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:*[http://2008.igem.org/Team:Calgary_Wetware/Project#SlideFrame_1 Calgary 2008]
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:*[http://2008.igem.org/Team:Cambridge/Modelling Cambridge 2008]
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:*[http://2008.igem.org/Team:Chiba/Project Chiba 2008]
<b>Cell cycle</b>
<b>Cell cycle</b>
:*[http://parts.mit.edu/wiki/index.php/Synchronization_of_Cell_Cycles Bangalore 2006]
:*[http://parts.mit.edu/wiki/index.php/Synchronization_of_Cell_Cycles Bangalore 2006]
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:*[http://partsregistry.org/Part:BBa_K142040 BBa_K142040]: <i>Ribosome modulation factor (RMF)</i>
:*[http://partsregistry.org/Part:BBa_K142040 BBa_K142040]: <i>Ribosome modulation factor (RMF)</i>
:*[http://partsregistry.org/Part:BBa_K142041 BBa_K142041]: <i>Arabinose controlled RMF generator</i>
:*[http://partsregistry.org/Part:BBa_K142041 BBa_K142041]: <i>Arabinose controlled RMF generator</i>
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<b>Cell death</b>
<b>Cell death</b>
:*[http://partsregistry.org/Part:BBa_I745006 BBa_I745006]
:*[http://partsregistry.org/Part:BBa_I745006 BBa_I745006]
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:*[http://www.ncbi.nlm.nih.gov/pubmed/15107854 Just-in-time transcription program in metabolic pathways] Zaslaver <i>et al.</i>
:*[http://www.ncbi.nlm.nih.gov/pubmed/15107854 Just-in-time transcription program in metabolic pathways] Zaslaver <i>et al.</i>
::<i>A study that showed certain promotors involved in amino acid biosynthesis being downregulated when compounds of the involved pathways were added.</i>
::<i>A study that showed certain promotors involved in amino acid biosynthesis being downregulated when compounds of the involved pathways were added.</i>
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<b>Cell death</b>
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:*[http://2008.igem.org/Team:Edinburgh/Plan#Bacterial_cell_lysis Edinburgh 2008]
<b>Modelling</b>
<b>Modelling</b>
:*[http://bioinformatics.oxfordjournals.org/cgi/content/full/23/18/2415 Robustness analysis and tuning of synthetic gene networks], Batt <i>et al.</i>
:*[http://bioinformatics.oxfordjournals.org/cgi/content/full/23/18/2415 Robustness analysis and tuning of synthetic gene networks], Batt <i>et al.</i>

Revision as of 11:27, 3 May 2009

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Contents

Introduction

Bacteria have a choice between using nutrients for growth or for the production of (commercially) valuable proteins. Being able to control the bacterial growth cycle and inducing a premature stationary phase will create the possibility to spend more time producing, using less nutrients for biomass and more for desired production. Stationary phase is nothing more than a stop in an increase of cell numbers by cell death being equal to cell growth. Normally this is induced by the limitation of available nutrients or by means of quorum sensing in response to high cell density. With this knowledge, a culture can be created that can respond by limited cell death in response to an added molecule that mimics the response to high cell density.

See also Chemostat
One of the most important features of chemostats is that micro-organisms can be grown in a physiological steady state. In steady state, all culture parameters remain constant (culture volume, dissolved oxygen concentration, nutrient and product concentrations, pH, cell density, etc.). Because obtaining a steady state requires at least 5 volume changes, chemostats require large nutrient and waste reservoirs. Creating biological "chemostat" would circumvent these drawbacks.

Previous contests

Quorum Sensing

Cell cycle

Parts in the Registry of Standard Parts:

Quorum sensing

  • BBa_K104001: Sensor for small peptide Subtilin
  • BBa_I13211: Biobricked version of the natural Lux quorum sensing system
  • BBa_T9002: AHL to GFP Converter

Cell cycle

Cell death

Related Literature

Quorum sensing

In this paper, the molecular mechanism underlying regulation of nisin and subtilin production is reviewed.
Addition of N-acylhomoserine lactone in the exponential growth phase, regardless of cell density, induces a repression of cell growth of P. aeruginosa
In this study Competence stimulating peptide is shown to initiates release of DNA from a subfraction of the bacterial population, probably by cell lysis.
In this study they created a synthetic ecosystem with bi-directional communication through quorum sensing which regulate each other's gene expression and survival via engineered gene circuits.
In this study they have built and characterized a 'population control' circuit that autonomously regulates the density of an Escherichia coli population, that is lower than the limits imposed by the environment. The cell density is broadcasted and detected by elements from a bacterial quorum-sensing system, which in turn regulate the death rate
In this study they created two colocalized populations of Escherichia coli that communicate with each other and exhibit a “consensus” gene expression response. Because neither population can respond without the other's signal, this consensus function can be considered a logical AND gate in which the inputs are cell populations.

Cell cycle

A study that showed certain promotors involved in amino acid biosynthesis being downregulated when compounds of the involved pathways were added.

Cell death

Modelling

In this work, they demonstrate the biological relevance of a method specifically developed to support the design of synthetic gene networks.
A review of past and present of computational modeling of cell-cycle regulation