Team:Imperial College/Growth Curve

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=== Modelling the Growth Curve ===
=== Modelling the Growth Curve ===
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{{Imperial/Box2||
{{Imperial/Box2||
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Part of our dry lab team concentrated on modelling the growth curve of ''B. subtilis''. This is important to characterise the chassis; particularly, the growth of ''subtilis'' is a vital parameter for planning experiments for future projects. Characterisation increases the predictability of the growth of  B. subtilis by determining, for example, its growth rate and the duration of its distinctive growth phases. In order to model the growth of ''B. subtilis'', the process was broken down into three main steps, where a separate submodel is produced in MATLAB for each step. Each submodel is an ODE model, which can be simulated using MATLAB. The variables in each submodel can be adjusted according to the boundary conditions (from experimental results).
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The first task of our dry lab team was to construct a simple yet effective model for the growth curve of ''B. subtilis''. The inspiration for our model came from the work done by the Imperial College team on the characterisation of Cell Free Systems (<html><a target="_blank" href="https://2007.igem.org/Imperial/Cell-Free/Characterisation">here</a></html>) and the elegant manner they found to couple the production terms in the model with the resources available in the medium.|}}
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In the final step, a combination of Submodels 1 and 2 are superimposed with Submodel 3, resulting in a more complex model which enhances the accuracy of illustrating bacterial growth.  For more details about the submodels, please see the [[Team:Imperial_College/Appendices | Dry Lab Appendices page]].
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The graph on the right shows the log graph used to determine the growth rate.
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|[[Image:log_graph2.JPG|300px|Log-Graph used to determine the growth rate]]}}
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{{Imperial/Box1|The Model|
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Our model is based on the crucial role played by nutrients, and other resources, during the main growth phases that B. subtilis undergoes - the '''lag phase''', the '''exponential phase''' and the '''stationary phase'''. To keep the model simple, we omit the fourth phase of growth - the '''death phase'''.
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{{Imperial/Box1|Key Phases of Bacterial Growth|
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[[Image:Lag_phase.JPG|300px|left]]
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[[Image:Lag_phase.JPG|left]]
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====== Lag Phase ======
====== Lag Phase ======
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During the lag phase, the rate of growth is slow.  All nutrients are situated outside the cell initially.  Some time is needed for an adequate amount of nutrients to move from the outside of the cell into the interior of the cell.  This is vital as the cell requires the nutrients for growth.
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During the lag phase, the rate of growth is slow.  All nutrients are situated outside the cell initially.  Some time is needed for an adequate amount of nutrients to move from the outside of the cell into the interior of the cell.  This is vital, as the cell requires the nutrients for growth.
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<html><br clear="all"></html>
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[[Image:Exponential_phase.JPG|left]]
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[[Image:Exponential_phase.JPG|300px|left]]
====== Exponential Phase ======
====== Exponential Phase ======
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Nutrients are consumed during the cell growth and the growth is exponential as long as there are enough nutrients available.  The exchange of nutrients ensures that the intra- and extracellular nutrient concentration are the same.
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Nutrients are consumed during cell growth and the growth is exponential as long as there are enough nutrients available.  The exchange of nutrients ensures that the intra- and extracellular nutrient concentrations are the same.
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<html><br clear="all"></html>
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[[Image:Stationary_phase.JPG‎|left]]
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[[Image:Stationary_phase2.JPG‎|300px|left]]
====== Stationary Phase ======
====== Stationary Phase ======
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The growth of the colony ceases in number and in volume due to a finite concentration of nutrients, hence it does not have a gradient.  Other causes may be death and cell division.
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The growth of the colony ceases in number and in volume. This happens when the colony has consumed all available nutrients.  Other contributing factors may be death and cell division.
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<html><br clear="all"></html>
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To further enhance the accuracy of the model, the following information will be extracted from experimental data, resulting in a model that will produce a closer fit to the trend given by the experimental data.
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We chose to represent the growth in terms of overall volume rather than number of cells.
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For small volumes (lag phase and early exponential phase) the volume is a smoother variable (the overall volume grows a little in the lag phase) while in later phases, the volume and the number of cells are proportional. <br>
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Considering the overall volume also regularises the process since it allows us, to some extent, to overlook the process of cell division during the bacteria growth. We can assume the internal concentration of nutrients is reset almost instantaneously to the external concentration. During this time, the total volume does not change.
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<br><br>
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In order to couple growth with resources, our model curbs the optimal growth rate of the volume (A) as a function f of the resources R. We used a Hill function of exponent n, as it models with only one parameter the fact that production is unhindered when resources are plentiful and minimal when resources have run out. Finally we assume that in term of resources the cost of an extra unit of volume is alpha.
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<br>
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*Time span of lag phase, stationary phase and exponential phase
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The growth of the overall volume V is therefore modelled as:
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*The growth rate}}
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[[Image:Growth_equ.JPG|150px]]
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{{Imperial/Box1|The Model|
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f(R) is the Hill function, where
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The model illustrates the main growth phases the ''B. subtilis'' undergoes.  These are identified as the '''lag phase''', the '''exponential phase''' and the '''stationary phase'''.  The death phase is a constitutive event and it is possible that it exerts an influence on the three phases discussed below.  However, to simplify a complicated model, it is less relevant in this case and therefore is not included in this model.
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[[Image:Hill_fn_eq.jpg‎]]
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The M-file used to generate the model below is located in the Appendices section of the Dry Lab hub.
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Its switch point is 1.
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<center>[[Image:nutrient_ft.JPG|350px]][[Image:Label_model.JPG‎|350px]]</center>}}
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The M-file we wrote for the growth model can be found in the Appendices section of the Dry Lab hub.
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|}}
{{Imperial/Box1|Results|
{{Imperial/Box1|Results|
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The model for the growth curve was fitted to the experimental results as shown below. The experimental results is depicted by the red curve, while our model is shown by the green curve. The resource curve was also plotted as a function of time and is shown below. Based on our experimental results from the Wet Lab, a log graph was plotted to determine the growth rateThe growth rate was then determined from the gradient of the log graph.  This value was included when simulating the growth model using MATLAB.
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As the simulation below shows, in a rich medium the nutrients are taken up quickly, resulting in a 'nutrient crash'.  The transition from exponential to stationary phase is very sudden.
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<html><table border="0" cellpadding="5" cellspacing="0" align="center"><tr><td width=33%></html>[[Image:Experimental_Result.JPG|thumb|center]]<html>
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In the case of our experimental data, the transition is much smoother and the growth far less explosive even in the exponential phase, thus suggesting that the growth medium was not very rich in nutrients and that even at the beginning of the exponential phase the growth was hampered.
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<br><br>
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<center>[[Image:nutrient_ft.JPG|350px]][[Image:Label_model.JPG‎|350px]]</center>
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<br><br>
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The model for the growth curve was fitted to the experimental results. The results are shown below. The experimental results are depicted by the red curve, while our model is shown by the green curve. The resource curve was also plotted as a function of time and is shown below. It clearly confirms our initial guess regarding the poorness of the medium in term of nutrients.
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It is worth noting that the model 'fit' is very good in the exponential and stationary phasesHowever, the fit is poorer in the lag phase.  This suggests that the model should be expanded in order to take into account phenomena such as the movement of the nutrients into the cells.
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<br>
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<html><table border="0" cellpadding="0" cellspacing="0" align="center" style="background-color:transparent;"><tr><td></html>[[Image:Experimental_Result.JPG|thumb|center]]<html>
</td><td></html>[[Image:Fitted_Curve.JPG‎|thumb|center]]<html>
</td><td></html>[[Image:Fitted_Curve.JPG‎|thumb|center]]<html>
</td><td></html>[[Image:Resource_Curve.JPG‎|thumb|center]]<html>
</td><td></html>[[Image:Resource_Curve.JPG‎|thumb|center]]<html>
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</td><td><center>Resource Curve</center>
</td><td><center>Resource Curve</center>
</td></tr></table></html>
</td></tr></table></html>
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<br><br>
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<br>
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The following constants used to generate the model were found to yield the best fit to experimental results:
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GROWTH CONSTANT (A): 1.3494
 
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INITIAL NUTRIENT CONCENTRATION (R<sub>0</sub>): 2
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The search for the optimal model parameters was done by brute force. But instead of browsing the whole space of parameters, we reduced the search volume with a simple pre-processing of the experimental data.
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The initial volume was estimated from the data - likewise the volume in the stationary phase which is directly related to the model parameters R0 and alpha.
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The model parameter A being related to the apparent growth rate of the experimental data,
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We plotted the log graph to determine the growth rate of our data.
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HILL COEFFICIENT (n): 1.25
 
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INITIAL OD: 0.4
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''Log-Graph used to determine the growth rate''
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CONSTANT (α): 0.64516
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[[Image:log_graph2.JPG|300px|Log-Graph used to determine the growth rate|left]]
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The model 'fit' is very good in exponential and stationary phases. However, this does not apply to the lag phaseThis suggests that the model should be expanded in order to take into account phenomena such as the movement of the nutrients into the cells.
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The following constants were found to yield the best fit to experimental results:
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<br>- GROWTH CONSTANT (A): 1.3494
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<br>- INITIAL NUTRIENT CONCENTRATION (R<sub>0</sub>): 2
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<br>- HILL COEFFICIENT (n): 1.25
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<br>- INITIAL OD: 0.4
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<br>- CONSTANT (α): 0.64516
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}}
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{{Imperial/EndPage|Dry_Lab|Genetic_Circuit}}
{{Imperial/EndPage|Dry_Lab|Genetic_Circuit}}

Latest revision as of 04:00, 27 July 2009


Modelling the Growth Curve

The first task of our dry lab team was to construct a simple yet effective model for the growth curve of B. subtilis. The inspiration for our model came from the work done by the Imperial College team on the characterisation of Cell Free Systems (here) and the elegant manner they found to couple the production terms in the model with the resources available in the medium.


The Model

Our model is based on the crucial role played by nutrients, and other resources, during the main growth phases that B. subtilis undergoes - the lag phase, the exponential phase and the stationary phase. To keep the model simple, we omit the fourth phase of growth - the death phase.

Lag phase.JPG
Lag Phase

During the lag phase, the rate of growth is slow. All nutrients are situated outside the cell initially. Some time is needed for an adequate amount of nutrients to move from the outside of the cell into the interior of the cell. This is vital, as the cell requires the nutrients for growth.

Exponential phase.JPG
Exponential Phase

Nutrients are consumed during cell growth and the growth is exponential as long as there are enough nutrients available. The exchange of nutrients ensures that the intra- and extracellular nutrient concentrations are the same.

Stationary phase2.JPG
Stationary Phase

The growth of the colony ceases in number and in volume. This happens when the colony has consumed all available nutrients. Other contributing factors may be death and cell division.

We chose to represent the growth in terms of overall volume rather than number of cells. For small volumes (lag phase and early exponential phase) the volume is a smoother variable (the overall volume grows a little in the lag phase) while in later phases, the volume and the number of cells are proportional.
Considering the overall volume also regularises the process since it allows us, to some extent, to overlook the process of cell division during the bacteria growth. We can assume the internal concentration of nutrients is reset almost instantaneously to the external concentration. During this time, the total volume does not change.

In order to couple growth with resources, our model curbs the optimal growth rate of the volume (A) as a function f of the resources R. We used a Hill function of exponent n, as it models with only one parameter the fact that production is unhindered when resources are plentiful and minimal when resources have run out. Finally we assume that in term of resources the cost of an extra unit of volume is alpha.

The growth of the overall volume V is therefore modelled as:

Growth equ.JPG

f(R) is the Hill function, where

Hill fn eq.jpg

Its switch point is 1.

The M-file we wrote for the growth model can be found in the Appendices section of the Dry Lab hub.


Results

As the simulation below shows, in a rich medium the nutrients are taken up quickly, resulting in a 'nutrient crash'. The transition from exponential to stationary phase is very sudden. In the case of our experimental data, the transition is much smoother and the growth far less explosive even in the exponential phase, thus suggesting that the growth medium was not very rich in nutrients and that even at the beginning of the exponential phase the growth was hampered.

Nutrient ft.JPGLabel model.JPG



The model for the growth curve was fitted to the experimental results. The results are shown below. The experimental results are depicted by the red curve, while our model is shown by the green curve. The resource curve was also plotted as a function of time and is shown below. It clearly confirms our initial guess regarding the poorness of the medium in term of nutrients.

It is worth noting that the model 'fit' is very good in the exponential and stationary phases. However, the fit is poorer in the lag phase. This suggests that the model should be expanded in order to take into account phenomena such as the movement of the nutrients into the cells.


Experimental Result.JPG
Fitted Curve.JPG
Resource Curve.JPG
Experimental Results
Fitted Curve
Resource Curve



The search for the optimal model parameters was done by brute force. But instead of browsing the whole space of parameters, we reduced the search volume with a simple pre-processing of the experimental data. The initial volume was estimated from the data - likewise the volume in the stationary phase which is directly related to the model parameters R0 and alpha. The model parameter A being related to the apparent growth rate of the experimental data, We plotted the log graph to determine the growth rate of our data.


Log-Graph used to determine the growth rate

Log-Graph used to determine the growth rate

The following constants were found to yield the best fit to experimental results:
- GROWTH CONSTANT (A): 1.3494
- INITIAL NUTRIENT CONCENTRATION (R0): 2
- HILL COEFFICIENT (n): 1.25
- INITIAL OD: 0.4
- CONSTANT (α): 0.64516