Team:IIT Bombay India/PSM

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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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Stochastic Modelling for the system  
Stochastic Modelling for the system  
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Objective:- To characterize the intrinsic noise present in the system for all the 4 strains.
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              To compare the lac I expression levels and plasmid concentrations and the errors associated with them for each of the 4 strains, using a simplified phenomenological model.
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Model
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'''Objective'''
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To characterize the intrinsic noise present in the system for all the 4 strains.
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To compare the lac I expression levels and plasmid concentrations and the errors associated with them for each of the 4 strains, using a simplified phenomenological model.
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'''Model'''
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A simplified model for lacI expression and copy number regulation is developed.
A simplified model for lacI expression and copy number regulation is developed.
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dlacI/dt=K_1*Cn*C1-( μ+β)Cn-K_4*C3
 
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dCn/dt=K_2*(Cn*C2)/(K_c+ 〖Cn〗^2 )-(μ+β_2 )*Cn
 
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(d(lacI-IPTG))/dt=K_4*C3
 
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[[Image:Eq-1.jpg]]
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Where the terms C1 and C2 and C3 representing control action are:
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[[Image:Eq-2.jpg]]
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For open loop, none of the control actions exist, and hence C1=C2=1 and C3=0;
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For the strain with lacI regulation, C2=1, C1 and C3 are obtained from the equations above.
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For strain with plasmid  regulation, C1=1,C2 and C3 are obtained from the equations above.
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For strain with multiple feedback, all the three terms, C1, C2 and C3 are obtained from equations above.
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'''Methodology'''
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Stochasticity is introduced in the system by randomly perturbing the kinetic parameters, K1,K2, K3 and K4 and k1, k2 and k4 from their mean values to a maximum limit of 30 % and carrying out numerous simulations to obtain the various trajectories possible. Hence the distributions so obtained for lacI and plasmid concentrations are characterized . The errors in these distributions are then compared for the 4 strains.
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'''Results'''
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[[Image:Graph-1.jpg]]
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The qualitative behavior for all the 4 strains is similar.
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The strain with multiple feedback shows least expression while strain with no feedback shows maximum expression.
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The error bars are plotted above. The mean values for 100 runs and the errors associated with them are summarized below.
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[[Image:Table-1.jpg]]
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Thus, error decreases almost 6 times for the strain with multiple feedback as compared to the open loop strain.
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The error is almost similar for strains with only a single feedback, which is less than that for the open loop strain.
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[[Image:Graph-2.jpg]]
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[[Image:Table-2.jpg]]
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The curves for plasmid concentration in the open loop strain and strain with lacI regulation are the same, since plasmid replication is unaffected by lacI regulation.
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The error in plasmid concentration is least in the strain with plasmid regulation, it is 1/3rd of the error in open loop strain.
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This can be attributed to the fact that the lacI feeds back to two control loops in strain with multiple feedback, and hence it does not regulate the plasmid concentration as effectively.
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'''Effect of IPTG on system:'''
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[[Image:Graph-3.jpg]]
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Increasing IPTG causes all systems to resemble open loop in their behavior, which is confirmed by their steady state concentration.
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The error values are summarized.
 +
 +
[[Image:Table-3.jpg]]
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 +
At high IPTG, the error and mean is almost similar for all strains.
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Note that we plot the total IPTG present in the system(free as well as complexed with IPTG). Hence the high values observed in the  3 strains.
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[[Image:Graph-4.jpg]]
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[[Image:Table-4.jpg]]
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Here again, the resemblance of the system with open loop is observed at high IPTG values.
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This is confirmed by the same mean and errors obtained at high IPTG values.
 +
 +
Thus, we see that the strain with multiple feedback shows greater degree of control with reduced noise.
 +
 +
Further we are attempting to study the differences on growth on lactose in the 4 strains by introducing stochasticity on the reduced model.
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The detailed methodology, system equations, results and discussion can be seen [[Media:Stochastic modelling.pdf|here]].
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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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Latest revision as of 01:02, 22 October 2009

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Phenomenological Stochastic Model

Stochastic Modelling for the system


Objective To characterize the intrinsic noise present in the system for all the 4 strains. To compare the lac I expression levels and plasmid concentrations and the errors associated with them for each of the 4 strains, using a simplified phenomenological model.

Model

A simplified model for lacI expression and copy number regulation is developed.

Eq-1.jpg

Where the terms C1 and C2 and C3 representing control action are:

Eq-2.jpg

For open loop, none of the control actions exist, and hence C1=C2=1 and C3=0;

For the strain with lacI regulation, C2=1, C1 and C3 are obtained from the equations above.

For strain with plasmid regulation, C1=1,C2 and C3 are obtained from the equations above.

For strain with multiple feedback, all the three terms, C1, C2 and C3 are obtained from equations above.


Methodology

Stochasticity is introduced in the system by randomly perturbing the kinetic parameters, K1,K2, K3 and K4 and k1, k2 and k4 from their mean values to a maximum limit of 30 % and carrying out numerous simulations to obtain the various trajectories possible. Hence the distributions so obtained for lacI and plasmid concentrations are characterized . The errors in these distributions are then compared for the 4 strains.


Results

Graph-1.jpg

The qualitative behavior for all the 4 strains is similar.

The strain with multiple feedback shows least expression while strain with no feedback shows maximum expression.

The error bars are plotted above. The mean values for 100 runs and the errors associated with them are summarized below.

Table-1.jpg

Thus, error decreases almost 6 times for the strain with multiple feedback as compared to the open loop strain.

The error is almost similar for strains with only a single feedback, which is less than that for the open loop strain.

Graph-2.jpg

Table-2.jpg

The curves for plasmid concentration in the open loop strain and strain with lacI regulation are the same, since plasmid replication is unaffected by lacI regulation.

The error in plasmid concentration is least in the strain with plasmid regulation, it is 1/3rd of the error in open loop strain. This can be attributed to the fact that the lacI feeds back to two control loops in strain with multiple feedback, and hence it does not regulate the plasmid concentration as effectively.

Effect of IPTG on system:

Graph-3.jpg

Increasing IPTG causes all systems to resemble open loop in their behavior, which is confirmed by their steady state concentration. The error values are summarized.

Table-3.jpg

At high IPTG, the error and mean is almost similar for all strains. Note that we plot the total IPTG present in the system(free as well as complexed with IPTG). Hence the high values observed in the 3 strains.

Graph-4.jpg

Table-4.jpg

Here again, the resemblance of the system with open loop is observed at high IPTG values.

This is confirmed by the same mean and errors obtained at high IPTG values.

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

Further we are attempting to study the differences on growth on lactose in the 4 strains by introducing stochasticity on the reduced model.

The detailed methodology, system equations, results and discussion can be seen here.


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