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| - | <h2>How does it work</h2>
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| - | <p>In the General View of the program we represent a layer of the cytoplasm of an E.Coli specimen, with a thickness of 5 nm. The actual dimensions of an E.coli have been obtained from the <a href="http://www.ccdb.ualberta.ca/CCDB/" title="CyberCell web" target="_blank">CyberCell Database</a>, a fantastic information source. We have considered the next measures to model the bacterium:</p>
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| - | <img width="300" src="https://static.igem.org/mediawiki/2011/9/92/UPOSevillaEColi_mesurations.png" alt="E. Coli measures" />
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| - | <p>E. Coli schematics measures</p>
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| - | <p>To use these dimensions in the “NetLogo’s patch world” (where the world is made of a grid of stationary agents called patches) we have taken as reference the size of a standard protein (5 nm (Guo,2006)) and adjusted the sizes of the world so that every patch has the same size of a protein. This way, the whole world is made of a grid of 400 x 160. As we have taken a layer with a thickness of only 5nm, we have scaled the typical ammount of several molecules in a E. Coli (like ribosomes or RNAps) accordingly (we do that by dividing the numbers by 120, corresponding with the number of patches that theorically would measure the high of an E.Coli).</p>
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| - | <p>Once we click <strong>setup</strong>, we can see several molecules. To initiate a simulation just click on <strong>go</strong>.</p>
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| - | <p><strong>What can we see?</strong></p>
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| - | <p>On the left we can see several <strong>genes</strong> (one couple of genes by default), represented by lines.</p>
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| - | <p>The longer genes are LacI genes (Repressor 1), while the shorter ones represent c1ts genes (Repressor2). From now on we will use the terms LacI/Repressor1 and c1ts/Repressor2 indistinctly</p>
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| - | <p>Both genes are to scale. In the real bacteria they both would appear in the same plasmid and completely twisted. They both have a promoter at the beginning and a terminator at the end.</p>
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| - | <p>After this, we have <strong>RNAP</strong> (green circles) which can move freely around the cytoplasm until they find a promoter. Once they do it, they have a given chance of starting to transcribe. When they transcribe, they generate a <strong>RNAm molecule</strong>. When they finish transcription, this RNA molecule will move till stop in a randomly place in the cytoplasm. Each RNAm molecule remains a given time in the cell before it “dies” and disappears; this time is given by a normal distribution with mean of 5 minutes and a standard deviation of 1 minute (by default, but you can change this value to experiment with it).</p>
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| - | <p>The big yellow circles represent <strong>ribosomes</strong>, which move freely around the cytoplasm until they find the Shine-Dalgarno of a RNA. At that moment they start the translation. When they finish, they generate a repressor protein. The size of the ribosomes is also to scale, measuring 20 nm of diameter.</p>
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| - | <p><strong>Repressors1</strong>, in red in the plots, represent LacI, which become inactive if they are attached to IPTG. If the user adds IPTG to the cell he will able to inactivate lots of them. The monitors allow you to see online how many repressors are inactive.</p>
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| - | <p><strong>Repressors2</strong>, in blue, represent c1ts repressors, which become unstable and “die” when the temperature is increased. If the user establishes the temperature at 42 degrees, repressors2 will start to disappear. The temperature effect tells us how many times faster the repressors2 disappear if the temperature is 42.</p>
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| - | <p>Repressors1 can attach to kind-2 promoters and repress them, blocking their transcription, and vice-versa. The number of repressor molecules that a promoter can admit can be chosen by the user (0 means no limit)</p>
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| - | <p>The amounts of all molecules (except RNA polymerases and ribosomes to simplify the simulation) decrease sharply to the half once the bacterium passes through a cell cycle. All free molecules move following the rules of the Brownian movement. </p>
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| - | <p><a href="https://2011.igem.org/Team:UPO-Sevilla/Project/Basic_Flip_Flop/Multiagent_System/How_to_use_it" title="How to use it Multiagent System">How to use it multiagent system simulation</a></p>
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| - | <h2>Extending the model</h2>
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| - | <p>This model could become more complicated adding more and more details of the real biological system, but it would increase the complexity of it and so the ability of making simulations in a reasonable time. Some of the improvements that could be implemented without making the program much more complex is the strength of the SD or adding a cycle life to the ribosomes and the RNA polymerases. A more interesting improvement and still plausible could be representing the growth of the bacterium while the time goes by and the sharply decrease of the size to the half after the binary fission.</p>
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| - | <h2>Bibliography</h2>
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| - | <li> Christopher P.Fall, Eric S.Marland, John M. Wagner and John J. Tyson. Computational Cell Biology. United States of America: Springer, 2002.</li>
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| - | <li> Guo, Shan Sundararaj and An Chi. CyberCell Database. 2006. http://redpoll.pharmacy.ualberta.ca/CCDB/index.html.</li>
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| - | <li> Kaczanowska M, Rydén-Aulin M. "Ribosome biogenesis and the translation process in Escherichia coli. ." Microbiol Mol Biol Rev., 2007 : Sep71(3):p.478 right column 1st paragraph.</li>
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| - | <li>Leyton-Browm, Yoav Shoham and Kevin. Multiagent Systems: Algorithmic, Game-theoretic and Logical Foundations. 2009, 2010.</li>
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| - | <li>Timothy S. Gardner, Charles R. Cantor, James J. Collins. "Construction of a genetic toggle switch in Escherichia coli." Nature, 2000: 339-342.</li>
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| - | <li> Wilensky, Uri. NetLogo. 199-2011. http://ccl.northwestern.edu/netlogo/.</li>
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| - | <li> Zoltan Szallasi, Jörg Stelling, and Vipul Periwal. System Modeling in Cellular Biology: From concepts to nuts and bolts. Cambridge, Massachusetts: MIT press, 2010.</li>
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