Team:Colombia/Modeling/Stochastic

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== '''Stochastic Model''' ==
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== '''Stochastic Model''' ==
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The previous sections showed how to know the mean behavior of the system for one cell, but this is just an average of the total proteins within the cell. All the biological systems are controlled by probability events. The cell is a huge space where there are a lot of small molecules. If we want a biological process to happen, two of these molecules have to find each other among millions in a huge pool. The differential equations do not take into account these uncontrollable events that can change the response dramatically.
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The previous sections showed how to know the mean behavior of the system for one cell, but this is just an average of the total proteins within the cell. All the biological systems are controlled by probability events. The cell is a huge space where there are a lot of small molecules, if we want a biological process to happen, two of this little molecules have to find themselves between million of other molecules in a huge pool. Also there is a possibility that a cell may need more concentration of a promoter’s activator while other may need less if the day is sunny but if is cloudy the second cell may need more. The differential equations don’t take into account these uncontrollable events that can change the response dramatically.
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If we look only one cell, with all the uncontrollable events this may not behave like we want and the system may not respond or even worse the probability of dying exist and our cell may die.  But dealing with one cell is not real, we always work with hundreds of cells. Within this population some cells may no produce the expected response but the others will and the average of cells would be able to respond to the presence of the pest.
 
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The stochastic algorithms are a way to model these probability events within a population.  This simulation are made in order to confirm that the system dynamics are robust and consistent and show us if the response is still behaving like we want after taking probabilities considerations. We use the Gillispie method to develop our model. Here is a brief explanation of how it works:  
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If we look only at one cell, it may not behave like we want and the system may not respond. Even worse, the probability of dying exist and our cell may die.  But dealing with one cell is not real and we always work with hundreds of cells. Within this population, some cells may not behave as expected but others will and the average of cells would be able to respond to the presence of the pest.
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The stochastic algorithms are a way to model these probability events within a population.  This simulation is made in order to confirm that the system dynamics are robust, consistent and show us if the response is still behaving like we want (taking probabilities into consideration). We use the Gillespie algorithm to develop our model. Here is a brief explanation of how it works:  
The complete method consists of eight steps.
The complete method consists of eight steps.
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#Define the number of cells.
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#Define the time of the simulation
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#Define and name all the constants involved.
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#Define creation and destruction expression for each substance involved: The differential equations in this part have to be divided in two, the creation and the destruction expression.  
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#Apply Gilliespie algorithm:
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##Calculate the sample space of the analysed system: This is the sum of all the changes presented at specific time t.  [[File:sampspa.png|center|200x100pxpx]]
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::#Define the number of cells.
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##Calculate time distribution that depends on a random number between 0 and 1. [[File:sampspa1.png|center|600x200pxpx]]
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::#Define the number of steps
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##Calculate which event occurs: The Gillespie algorithm does not consider simultaneous events, it says that each time only one event occurs. In our case the events are the creation or destruction of one protein. Each event has a probability of of occurrence within the sample space  between 0 and 1.  To know which event occurs at the time t+1 we take into account the random number use for the time distribution and look for the event that has this probability of occurrence . For example if you see the sample space figure below, there are 5 possibles events with their probability; if the random number is 0.4 then A is gonna be destructed but if the random number is 0.55 then B will be created.  [[File:sampspa2.png|center|500x300pxpx]]
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::#Define and name all the constants involved.
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#Take the outputs from the simulation and convert them into regular interval vectors.
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::#Define creation and destruction events for each substance involved: The differential equations in this part have to be divided in two, the creation and the destruction expression.  
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#Obtain the Gilliespie function mean values.
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::#Apply [http://www.annualreviews.org/doi/pdf/10.1146/annurev.physchem.58.032806.104637 Gilliespie algorithm:]
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#Plot the obtained functions.
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:::• Calculate the sample space of the analyzed system: This is the sum of all the changes presented at specific time "t".   
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[[File:sampspa.png|center|200x100pxpx]]
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</br>
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</html>
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:::•Calculate time distribution that depends on a random number between 0 and 1.
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<br>
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</br>
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</html>
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[[File:sampspa1.png|center|600x200pxpx]]
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<br>
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:::•Calculate which event occurs: The Gillespie algorithm does not consider simultaneous events, this is that in each time only one event occurs. In this case, the events are the creation or destruction of one protein. Each event has a probability of occurrence within the sample space  between 0 and 1.  To know which event occurs at the time "t+1", we take into account the random number use for the time distribution and look for the event that has this probability of occurrence . For example, if you see the sample space figure below, there are 5 possible events with their probability; if the random number is 0.4 then A is going to be destructed but if the random number is 0.55. then B will be created.   
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[[File:sampspa2.png|center|500x300pxpx]]
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== Results ==
 
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The mathematical model should help the experimental design to optimize the circuit and our case was not the exception. The picture below shows the original design of the circuit.
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::6. Take the outputs from the simulation and convert them into regular interval vectors.
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::7. Obtain the Gillespie function mean values.
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::8. Plot the obtained functions.
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We performed an stochastic simulation that shows that our system does not respond to high but short pulse of chitin, this kind of response is one of the desired characteristics.
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After doing the simulation for the differential equations we have to make little changes to the proposed system:
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#As you can see there was an unknown promoter for LuxR. First we decided that it was a constitutive promoter but the response curves of the system were not consistent with the reality (LuxR had giant concentrations and the salicylic acid never went up). We wanted this protein to interact with LuxI and turn on the response, so we thought that it may need to be in a similar concentration of LuxI. Then we put it under the same promoter, hence the two proteins will be promoted at the same time.
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Latest revision as of 04:04, 27 October 2012

Template:Https://2012.igem.org/User:Tabima

Stochastic Model

The previous sections showed how to know the mean behavior of the system for one cell, but this is just an average of the total proteins within the cell. All the biological systems are controlled by probability events. The cell is a huge space where there are a lot of small molecules. If we want a biological process to happen, two of these molecules have to find each other among millions in a huge pool. The differential equations do not take into account these uncontrollable events that can change the response dramatically.

If we look only at one cell, it may not behave like we want and the system may not respond. Even worse, the probability of dying exist and our cell may die. But dealing with one cell is not real and we always work with hundreds of cells. Within this population, some cells may not behave as expected but others will and the average of cells would be able to respond to the presence of the pest.

The stochastic algorithms are a way to model these probability events within a population. This simulation is made in order to confirm that the system dynamics are robust, consistent and show us if the response is still behaving like we want (taking probabilities into consideration). We use the Gillespie algorithm to develop our model. Here is a brief explanation of how it works: The complete method consists of eight steps.

  1. Define the number of cells.
  2. Define the number of steps
  3. Define and name all the constants involved.
  4. Define creation and destruction events for each substance involved: The differential equations in this part have to be divided in two, the creation and the destruction expression.
  5. Apply [http://www.annualreviews.org/doi/pdf/10.1146/annurev.physchem.58.032806.104637 Gilliespie algorithm:]


• Calculate the sample space of the analyzed system: This is the sum of all the changes presented at specific time "t".
Sampspa.png



•Calculate time distribution that depends on a random number between 0 and 1.




Sampspa1.png



•Calculate which event occurs: The Gillespie algorithm does not consider simultaneous events, this is that in each time only one event occurs. In this case, the events are the creation or destruction of one protein. Each event has a probability of occurrence within the sample space between 0 and 1. To know which event occurs at the time "t+1", we take into account the random number use for the time distribution and look for the event that has this probability of occurrence . For example, if you see the sample space figure below, there are 5 possible events with their probability; if the random number is 0.4 then A is going to be destructed but if the random number is 0.55. then B will be created.




Sampspa2.png





6. Take the outputs from the simulation and convert them into regular interval vectors.
7. Obtain the Gillespie function mean values.
8. Plot the obtained functions.

We performed an stochastic simulation that shows that our system does not respond to high but short pulse of chitin, this kind of response is one of the desired characteristics.

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