Difference between revisions of "Team:Aalto-Helsinki/Modeling synergy"
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<p>One big concern in our project was the efficiency of propane production. To solve this problem we wanted to use micelles to hold enzymes together and speed up the reactions. By having two of our most inefficient enzymes close together we try to increase the propane yield. Our only question regarding this is: Does it actually work?</p> | <p>One big concern in our project was the efficiency of propane production. To solve this problem we wanted to use micelles to hold enzymes together and speed up the reactions. By having two of our most inefficient enzymes close together we try to increase the propane yield. Our only question regarding this is: Does it actually work?</p> | ||
− | <p>The idea behind this approach is to get the majority of butyraldehyde to ADO and propane production than to other butyraldehyde- | + | <p>The idea behind this approach is to get the majority of butyraldehyde to ADO and propane production than to other butyraldehyde-consuming enzymes. Intuitively this should happen if CAR and ADO are close together.</p> |
<p>To address this not so trivial question of feasibility of this approach, the modeling team assembled and thought about the problem. This problem couldn’t be described easily with simple differential equations since then notions of distance, proximity and their relation with enzyme reaction rates would have to be thoroughly researched as well.</p> | <p>To address this not so trivial question of feasibility of this approach, the modeling team assembled and thought about the problem. This problem couldn’t be described easily with simple differential equations since then notions of distance, proximity and their relation with enzyme reaction rates would have to be thoroughly researched as well.</p> | ||
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<p>Instead, the modeling team wondered: Is it possible to model the enzyme reactions like they happen in a cell? In the real world, there are no simple numbers inside the cell: Notions like reaction rate and enzyme kinetics arise from the chaotic fluctuations between molecules inside the cell. Enzyme reactions between enzymes and substrates are dependent on many factors, such as molecules having the right energy and orientation.</p> | <p>Instead, the modeling team wondered: Is it possible to model the enzyme reactions like they happen in a cell? In the real world, there are no simple numbers inside the cell: Notions like reaction rate and enzyme kinetics arise from the chaotic fluctuations between molecules inside the cell. Enzyme reactions between enzymes and substrates are dependent on many factors, such as molecules having the right energy and orientation.</p> | ||
− | <p>As such, this problem is quite | + | <p>As such, this problem is quite difficult and would undoubtedly need a lot of research. Instead, we decided to simplify the situation: Could we simulate enzymes and substrates as particles in a cell and model enzyme reactions by simulating their interactions with each other?</p> |
</section> | </section> | ||
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<p>Now, let’s go through the different phases of the simulation:</p> | <p>Now, let’s go through the different phases of the simulation:</p> | ||
− | <p><b>Initialization:</b> The program loads a settings file filled with information concerning the simulation, and creates a simulation according these specifications. With this file the user can, for example, specify the length of the simulation, the different substrates with their amounts and masses, as well as the different enzymes and the types of substrates and products they either consume or produce.</p> | + | <p><b>Initialization:</b> The program loads a settings file filled with information concerning the simulation, and creates a simulation according to these specifications. With this file the user can, for example, specify the length of the simulation, the different substrates with their amounts and masses, as well as the different enzymes and the types of substrates and products they either consume or produce.</p> |
<p><b>Particle movement:</b> The model moves particles according to Brownian motion in water. With particles of this size the governing attribute these particles have is their radius.</p> | <p><b>Particle movement:</b> The model moves particles according to Brownian motion in water. With particles of this size the governing attribute these particles have is their radius.</p> | ||
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− | <p><b>Reactions:</b> The simulation considers that a reaction is possible only when a right type of substrate and a right type of enzyme are close enough to react. When this happens, the simulation randomly decides if the reaction actually | + | <p><b>Reactions:</b> The simulation considers that a reaction is possible only when a right type of substrate and a right type of enzyme are close enough to react. When this happens, the simulation randomly decides if the reaction actually takes place with user-defined probability. If the reaction happened, the simulation changes the substrate to the enzyme’s product and makes both the substrate and enzyme unable to react for a short amount of time.</p> |
<p><b>Data logging:</b> the program collects only one type of data, and that is the particle numbers of each substrate at each point in time that the simulation runs. This makes it possible to make figures of the reactions and to determine the different reaction rates.</p> | <p><b>Data logging:</b> the program collects only one type of data, and that is the particle numbers of each substrate at each point in time that the simulation runs. This makes it possible to make figures of the reactions and to determine the different reaction rates.</p> | ||
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<p>Enzymes and substrates only react when they are in close enough proximity with each other. In addition to this, enzymes and substrates only react with the correct type of substrate and enzyme, respectively.</p> | <p>Enzymes and substrates only react when they are in close enough proximity with each other. In addition to this, enzymes and substrates only react with the correct type of substrate and enzyme, respectively.</p> | ||
− | <p> | + | <p>Reactions between enzymes and substrates are difficult to model. Since we don’t have a good way of predicting when a reaction should happen given that an enzyme and a substrate meet, we decide this by giving a reaction a probability to succeed and polling this probability every time a substrate and enzyme meet.</p> |
<p>Enzyme concentration and therefore particle number stays constant during the simulation. | <p>Enzyme concentration and therefore particle number stays constant during the simulation. | ||
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<p>To get some concrete results from our model, we ran it using different starting settings and different starting conditions, such as the amount of enzymes or substrates in the cell, different amounts of competing enzymes as well as different reaction parameters for the enzymes.</p> | <p>To get some concrete results from our model, we ran it using different starting settings and different starting conditions, such as the amount of enzymes or substrates in the cell, different amounts of competing enzymes as well as different reaction parameters for the enzymes.</p> | ||
− | <p>Settings with both joined enzymes and no joined enzymes were used to get the results presented. In addition to this, the model was tested with settings with competing enzymes to better represent the real-world scenario inside | + | <p>Settings with both joined enzymes and no joined enzymes were used to get the results presented. In addition to this, the model was tested with settings with competing enzymes to better represent the real-world scenario inside <i>E. coli</i>, where there are multiple competing enzymes consuming butyraldehyde.</p> |
− | <p>The simulation space was set to represent a cross section of an | + | <p>The simulation space was set to represent a cross section of an <i>E. coli</i> bacteria. Time resolution of the simulations was set to 1 µs. The simulations considered the beginning substrate’s concentration to be constant. The reaction probabilities for different enzymes were set at 15%. Total elapsed simulation time was 0.1 s. The amount of competing enzymes varied from 0% to 750% of the other enzymes’ amounts between simulations. The other enzymes’ amounts were not varied between simulations. The simulation setting files are available _here_.(add download link)</p> |
<p>The simulation results indicate that simulations with joined enzymes as opposed to unjoined enzymes achieved higher product creation rates. When more competing enzymes were present in the simulation, the difference between joined and unjoined enzymes became more apparent. In some simulations, joined enzymes got up to 400% reaction rate as opposed to non-joined enzymes. In most simulations, joined enzyme reaction rate changed between 150% and 400% of unjoined enzyme reaction rate, depending on how much competing enzymes there were. In all of the simulations joined enzymes performed as well or better than unjoined enzymes.</p> | <p>The simulation results indicate that simulations with joined enzymes as opposed to unjoined enzymes achieved higher product creation rates. When more competing enzymes were present in the simulation, the difference between joined and unjoined enzymes became more apparent. In some simulations, joined enzymes got up to 400% reaction rate as opposed to non-joined enzymes. In most simulations, joined enzyme reaction rate changed between 150% and 400% of unjoined enzyme reaction rate, depending on how much competing enzymes there were. In all of the simulations joined enzymes performed as well or better than unjoined enzymes.</p> |
Revision as of 06:56, 7 September 2015