Team:IIT Bombay India/DDM

From 2009.igem.org

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Detailed Molecular Model
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'''Objective'''
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Here we wish to show how the dynamics of the cellular material (proteins and plasmids) changes with time and IPTG and also how the specific growth rate of the four constructs on lactose is controlled and maximized by use of multiple feedbacks.  In this model quantification by simulation was done and later results were verified by experimental data.
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'''Primary Kinetics and Equations'''
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In our system we have the key components being plasmid copy number, fusion protein, yfp, lactose, IPTG and growth associated enzyme β-galactosidase. The E. coli genome inherently consists of β gal gene which has plac promoter. LacI interacts with lactose and IPTG and also with plac promoter.
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[[Image:p1.jpg]]
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Assuming these 3 equilibrium reactions, we can now write differential equations for the components relating their concentrations with time. The total amount of plac promoter present in any strain could be given by the equation:
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[[Image:p2.jpg]]
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Where ‘a’ is an integer which depends on the strain for which differential equation has been used to describe.(Total plac promoter, is the sum of concentration of free plac(fp) promoter and plac-LacI complex.
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[[Image:p3.jpg]]
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LacI total equals cfp (because they are a fusion protein). LacI refers to unbounded free lacI in the medium.
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[[Image:p4.jpg]]
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Note: here plac1, plac2, plac3 are the free plac associated with β-gal production, plasmid number and cfp-LacI protein.
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The differential equations are solved for two different conditions. Equations were first solved for 24 hours on other medium with different IPTG and no lactose.  After 24 hours the equations were solved for the same value of IPTG but on different values of lactose.
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Equations for growth on no Lactose:
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[[Image:p5.jpg]]
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Equations for growth on Lactose:
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[[Image:p6.jpg]]
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[[Image:p7.jpg]]
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'''Results'''
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The simulated results for the 4 strains are discussed below. The results are characterized in three parts; growth on non lactose media, subsequent growth on lactose and then we show how the multiple feedback helps in increasing growth rate of cells with reduced burden for production of proteins
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Thus, we see that the strain with multiple feedback shows greater degree of control with reduced noise.
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| Established in 1958, [http://www.iitb.ac.in IIT Bombay] is one of the most recognized centers of academic excellence in the country today. The excellence of its academic programs, a robust  research and development program with  parallel improvement in facilities and infrastructure have kept it at par with the best institutions in the world. The ideas on which such institutes are built evolve and change with national aspirations, national perspectives, and global trends. At IIT Bombay we are continuously seeking to extend the boundaries of our research in a sustained manner with clear cut executable goals, grounded solidly in national realities.
 
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This is our first year of participation and as such, we are pretty excited about the prospects. We are a group of chemical engineering and bioschool students. The most exciting aspect that we found about this competition was the interdisciplinary learning. A chemical reactor system invariably involves the design of control structures, and it is the design of these structures in a biological system that we wish to attain via our project.
 
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A major objective of synthetic biology is to unveil the inherent design principles prevailing in biological circuits. Multiple feedback loops (having both positive and negative regulation) are highly prevalent in biological systems. The relevance of such a design in biological systems is unclear. Our team will use synthetic biology approaches to answer these questions. Our team comprises of nine undergraduates, 3 graduate students as student mentor and two faculty mentors, one each from biology and engineering background. The project specifically deals with the analysis of effect of single and multiple feedback loops on gene expression. This project will involve theoretical and experimental studies. We have designed synthetic constructs to mimic multiple feedbacks. The focus of our experimental work will be to visualize the effect of multiple feedback loops on the synthetic construct using single cell analysis. The project will provide insights into the roles of multiple feedback loops in biological systems.
 
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Revision as of 20:01, 21 October 2009

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Detailed Deterministic Model

Detailed Molecular Model

Objective Here we wish to show how the dynamics of the cellular material (proteins and plasmids) changes with time and IPTG and also how the specific growth rate of the four constructs on lactose is controlled and maximized by use of multiple feedbacks. In this model quantification by simulation was done and later results were verified by experimental data.


Primary Kinetics and Equations In our system we have the key components being plasmid copy number, fusion protein, yfp, lactose, IPTG and growth associated enzyme β-galactosidase. The E. coli genome inherently consists of β gal gene which has plac promoter. LacI interacts with lactose and IPTG and also with plac promoter.


P1.jpg

Assuming these 3 equilibrium reactions, we can now write differential equations for the components relating their concentrations with time. The total amount of plac promoter present in any strain could be given by the equation:

File:P2.jpg

Where ‘a’ is an integer which depends on the strain for which differential equation has been used to describe.(Total plac promoter, is the sum of concentration of free plac(fp) promoter and plac-LacI complex.


File:P3.jpg

LacI total equals cfp (because they are a fusion protein). LacI refers to unbounded free lacI in the medium.



File:P4.jpg

Note: here plac1, plac2, plac3 are the free plac associated with β-gal production, plasmid number and cfp-LacI protein. The differential equations are solved for two different conditions. Equations were first solved for 24 hours on other medium with different IPTG and no lactose. After 24 hours the equations were solved for the same value of IPTG but on different values of lactose. Equations for growth on no Lactose:


File:P5.jpg

Equations for growth on Lactose:


File:P6.jpg File:P7.jpg


Results

The simulated results for the 4 strains are discussed below. The results are characterized in three parts; growth on non lactose media, subsequent growth on lactose and then we show how the multiple feedback helps in increasing growth rate of cells with reduced burden for production of proteins


Thus, we see that the strain with multiple feedback shows greater degree of control with reduced noise.



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