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Team:UC Davis/Projects
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| - | var crosstalkContent = "<br/><p class='header'>THE PROBLEM</p>In synthetic biology, the issue of crosstalk acts as a substantial barrier against developing fully-controlled biological systems. Much like in the development of electrical systems where crosstalk causes harmful interference and unpredictable behavior, crosstalk prevents us from completely understanding how our biological constructs function, and quite often can affect the efficacy of these systems. As such, it is clear that a method to computationally predict crosstalk in a given biological system would be a valuable scientific resource, and would effectively help minimize the negative effects of crosstalk. This is where our computational tool, CPOTATo, comes in.<br/><br/><div style='text-align: center;'><img src='https://static.igem.org/mediawiki/2010/c/c3/CPPaperScreen1.jpg'></div><br/>Crosstalk can be attributed to several aspects of biological systems, one of which is the interaction between proteins from the synthetic circuit and proteins from the host organism. CPOTATo takes advantage of this fact in an attempt to predict protein combinations that may cause crosstalk in various biological systems.<br/><br/>The primary reason for the crosstalk between proteins is usually the homology between them. Consider the following abstract example: In the chassis' system, Protein A naturally interacts with Protein B; however, Protein C, a protein produced by the synthetic circuit we wish to implant, is very homologous to Protein A. Since Protein A and Protein C are very similar, there is a certain degree of probability that Protein C will interact with Protein B. Therefore, unless Protein C's original purpose was to interact with Protein B, this is an undesirable interaction that may lead to unpredictable crosstalk.<br/><br/><p class='header'>APPROACH</p>CPOTATo executes several consecutive database queries to different existing databases of genomic information in order to infer any instances of crosstalk based on protein-protein relationships. The tool relies on two inputs: the amino acid sequence of the target protein, and the target organism or chassis. We query 3 individual databases in order to obtain the information we need: Interpro, UniprotKB and String.<br/><br/>From the Interpro database, we obtain one or more Interpro entries, each of which consists of a group of proteins homologous to our target protein. We then use these Interpro entries as the query parameter to the UniprotKB database in order to filter out all proteins that are not produced within the chassis. And finally, we query the String database with | + | var crosstalkContent = "<br/><p class='header'>THE PROBLEM</p>In synthetic biology, the issue of crosstalk acts as a substantial barrier against developing fully-controlled biological systems. Much like in the development of electrical systems where crosstalk causes harmful interference and unpredictable behavior, crosstalk prevents us from completely understanding how our biological constructs function, and quite often can affect the efficacy of these systems. As such, it is clear that a method to computationally predict crosstalk in a given biological system would be a valuable scientific resource, and would effectively help minimize the negative effects of crosstalk. This is where our computational tool, CPOTATo, comes in.<br/><br/><div style='text-align: center;'><img src='https://static.igem.org/mediawiki/2010/c/c3/CPPaperScreen1.jpg'></div><br/>Crosstalk can be attributed to several aspects of biological systems, one of which is the interaction between proteins from the synthetic circuit and proteins from the host organism. CPOTATo (Crosstalk Predictive Organism-Targeted Analysis Tool) takes advantage of this fact in an attempt to predict protein combinations that may cause crosstalk in various biological systems, and furthermore, to derive a numerical representation of the strength of this prediction, whether that be in the form of a probability or some other value on a specifically designed scoring scale.<br/><br/>The primary reason for the crosstalk between proteins is usually the homology between them. Consider the following abstract example: In the chassis' system, Protein A naturally interacts with Protein B; however, Protein C, a protein produced by the synthetic circuit we wish to implant, is very homologous to Protein A. Since Protein A and Protein C are very similar, there is a certain degree of probability that Protein C will interact with Protein B. Therefore, unless Protein C's original purpose was to interact with Protein B, this is an undesirable interaction that may lead to unpredictable crosstalk.<br/><br/><p class='header'>APPROACH</p>CPOTATo executes several consecutive database queries to different existing databases of genomic information in order to infer any instances of crosstalk based on protein-protein relationships. The tool relies on two inputs: the amino acid sequence of the target protein, and the target organism or chassis. We query 3 individual databases in order to obtain the information we need: Interpro, UniprotKB and String.<br/><br/>From the <a href='http://www.ebi.ac.uk/interpro/'>Interpro</a> database, we obtain one or more Interpro entries, each of which consists of a group of proteins homologous to our target protein. Interpro contains entries that correspond to various \"families\" of proteins based on whether they share some conserved protein sequence signature; these conserved signatures imply that the proteins within a given entry are homologously similar in some way (i.e. functionally, structurally etc.) Although Interpro is a metadatabase that takes information from several other databases, each of which has their own way of evaluating protein similarity, for our purposes, we only take into account entries from PFAM and TIGRFAM, two databases that deal exclusively with protein signature similarity related to protein function. We do hope to incorporate more databases as the project progresses, however, in the hopes that it will make our results more accurate.<br/><br/>After querying Interpro, we then use these Interpro entries as the query parameter to the <a href='http://www.uniprot.org/help/uniprotkb'>UniprotKB</a> database in order to filter out all proteins that are not produced within the chassis. The UniprotKB database contains information on individual proteins, and as such, we can acquire a complete list of all proteins within a given Interpro entry, and furthermore, we can ignore all proteins not produced within the target organism.<br/><br/>And finally, we query the <a href='http://string-db.org/'>String</a> database. This particular database contains documented protein-protein interaction information and when queried with a given protein, will return a list of all documented interactions that protein participates in within a given organism. For our purposes, this is the perfect source of interaction information, as we simply query the String database (many, many, MANY times) to obtain all the known interactions involving any of our homologous proteins.<br/><br/>The following is a screenshot of the results page of the tool.<div style='text-align:center'><img src='https://static.igem.org/mediawiki/2010/c/c6/CPPaperScreen2.jpg'></img></div><br/><br/>The results screen has several important fields. The first field is a combobox at the top of the form that contains all associated Interpro entries. Upon selecting an Interpro entry, the Homologues list will change to reflect what homologous proteins (to the target protein) exist in the selected Interpro entry. The user can also select one of these homologues to dynamically change the list of Interactions as well to those that are associated with the selected homologue.<br/><br/>For example: in this screenshot, we see that protein phoQ is produced in E. Coli, is within Interpro entry IPR003594, and is documented to interact with phoP, crcA, ugd, rcsF etc.<br/><br/>The final function to take note of is the Interactor Search function. This allows the user to interactively search for a given Interactor (say, \"phoB\"), and the tool will automatically cite all instances of these interactors within all Interpro entries in the box below.<br/><br/><p class='header'>SCORES</p>In order to derive the final score/probability that will be reported by the tool, we must take into account the various scores already reported by the databases we are querying. The score reported by Interpro is a probabilistic significance score describing how well the target protein \"fits\" in that Interpro group (an e-value). The score reported by UniprotKB (after consulting with a UniprotKB representative), will probably not be included in the final calculation because it simply represents an Apache Lucene full body text match score for the search string (in other words, it really gives no insight to the biological relationships of the proteins whatsoever). And finally, the score reported by String is a combination of several statistical values calculated by the database in the background that describes the overall probability of interaction. The finalization of the numerical score analysis is still pending. Please see the Changelog for the latest developments. <br/><br/><p class='header'>In Development:</p><div class='version'>v0.2.0</div><ul><li>Designing scoring models that will take into account all homologues' Interpro e-values and String interaction probabilities to generate the final reaction probability.</li><li>Creating statistical significance models in order to evaluate accuracy of current results</li><li>Hoping to utilize Flash to create a graphical network view of all related interactions for the current query</li></ul><br/><br/><p class='header'>Changelog</p><div class='version'>v0.1.0:</div><ul><li>Queries completed. Some documented cases of crosstalk have been run through the tool, and the results are consistent with what's in the literature.</li><li>Score system is still in development. Currently trying to devise a \"ranking\" system for each Interpro entry to rank each individual protein according to their individual e-values. Unfortunately, Interpro does not provide an easy way to acquire these numbers through one query. The only feasible alternative seems to be to re-query all homologues into the Interpro database to acquire their individual e-value. Asynchronous queries, anyone?</li></ul>"; |
| - | var pHContent = "<br/><p class='header'>THE PROBLEM</p>Cellular machines are not isolated systems. They have an intimate relationship with their surroundings and must change with varying environmental conditions. To do this, they rely on sensory machinery to trigger internal | + | var pHContent = "<br/><p class='header'>THE PROBLEM</p>Cellular machines are not isolated systems. They have an intimate relationship with their surroundings and must change with varying environmental conditions. To do this, they rely on sensory machinery to trigger internal responses based on external stimuli such as light, chemical concentrations, etc. Sensors have been engineered for the E. coli chassis but one stimulus has been neglected: pH.<br/><br/> Cells thrive in a limited pH range; the optimal range for E. coli being 6-7. If the pH is much different than this, the cell must take action to survive. This needs to be taken into account when designing cellular machines which are reactive to pH changes. A pH sensitive system from another organism would help keep the engineered response independent. This would allow the desired response to be separate from a native stress response. <br/><br/><div style='text-align:center'><img src='https://static.igem.org/mediawiki/2010/a/ac/Mes_photo.jpg'></div><br/><br/><p class='header'>APPROACH</p>In the native host, Agrobacterium tumefaciens, the pH sensing machinery is a two component system consisting of the ChvG and ChvI genes. ChvG is the membrane bound histidine kinase and ChvI is the chromosomal response regulatory gene. The pH sensing machinery is flawlessly intertwined in the vast number of other simultaneously occurring processes. Ideally, it could be transplanted into E. coli without affecting any other pathway, although the possibility of this happening is next to none. We do not know how the construct will behave in it's new environment, but we do expect some unwanted crosstalk caused by differing pH and phosphate levels within the new host.<br/><br/>Four promoters are likely to be activated by the ChvG/ChvI in A. tumefaciens: KatA, ImpA, ChvI, and AopB. We took these sequences and placed them upstream of RFP to measure the extent to which they are activated in E. coli. Another promoter, PhoA from the PhoB/PhoR two component system was also chosen to be tested with the ChvG/ChvI construct since both two-component systems are similar. We expect some activation of the PhoA promoter at high phosphate levels when the PhoB/PhoR system is deactivated.<br/><br/><p class='header'>CONSTRUCT</p><br/><br/><div style='text-align: center;'><img src='https://static.igem.org/mediawiki/2010/f/fd/Phsensorconstructs.jpg'></div><br/> The figure shows the construct for the ChvG/ChvI two component system activating 5 possible promoters.<br/><br/><p class='header'>RESULTS: KatA, ImpA, ChvI, AopB</p>Control tests of promoters KatA,Impa, ChvI, and AopB showed relatively low to no fluorescence over the background. Graphs are shown below.<br/><br/><div style='text-align:center'><img src='https://static.igem.org/mediawiki/2010/d/dc/ImpA_KatA_graph.JPG'></div><br/><br/>It is clear that there is virtually no activity of ImpA and KatA in the absence of ChvG/ChvI. This test was carried out at varying pH levels from 5.0-7.3 and at two different phosphate concentrations of 0.05mM and 5mM. All the data looks similar to this graph which means there is no activation of the promoters from anything native to E. coli.<br/><br/><div style='text-align:center'><img src='https://static.igem.org/mediawiki/2010/7/76/AopB_ChvI_graphs.JPG'></div><br/><br/>Similar curves were seen in tests of AopB and ChvI promoter constructs. There is substantially less fluorescence than the induced RFP. It may appear as if ChvI has some minimal fluorescence over background but this is merely because the tests were done on separate occasions where the gain played a part in differing the actual fluorescence. AopB and ChvI were also tested at pH levels from 5.0-7.3 and in phosphate concentrations of 0.05mM and 5mM.<br/><br/>In all tests, the induced RFP is part Bba_J04450 and was induced with 0.1-1mM IPTG in Xl1 blue cells.<br/><br/><p class='header'>RESULTS: PhoA</p><br/><br/><div style='text-align:center'><img src='https://static.igem.org/mediawiki/2010/b/b7/PhoA_graph.JPG'></div><br/><br/>We did see some fluorescence from the E. coli native promoter PhoA at low pH and low phosphate (0.05mM) which we were not expecting. We were expecting some crosstalk from the ChvG/ChvI two component system with the PhoB/PhoR system but weren't expecting the promoter construct alone to show any fluorescence. We are still looking for a possible explanation of why this is happening and have more tests planned using different reagents.<br/><br/><p class='header'>FUTURE TESTING</p>We have many more tests planned including testing the ChvG/ChvI construct with each promoter construct and further PhoA tests. We would like to find out why we see expression in the absence of the ChvG/ChvI system and hope to see and characterize expression for the other four promoters when coupled with the ChvG/ChvI system."; |
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| + | var spatialContent = "<br/><p class='header'>MOTIVATION</p>Patterns are everywhere in biology. Some, like zebra stripes, are easy to see, while other patterns like those that appear during animal development may be more subtle. Underneath the expression of these patterns are complex genetic networks that interpret specific cues from the environment and use this data to direct cells, or even populations of cells, to self organize and act.<br/><p class='header'>THE PROJECT</p>Given the importance of pattern generation in biology, we wanted to see if we could construct a synthetic circuit that would allow us to generate patterns in a community of inter-communicating cells in response to a simple stimuli; in our case, this stimuli would be light.<br/><br/>In order to mimic the process by which groups of cells can communicate to ultimately form a pattern, we will be using a lawn of E. Coli cells as our multicellular model system. These cells would be designed to communicate with each other through quorum sensing, and based on the small signaling molecules that each cell \"patch\" produces, the subsequent cell patch will know whether to activate or remain inactive.<br/><br/>In our system, the activated cells would produce colored pigment in order to indicate that they have been activated, and the cells that remain inactive will produce no color. In this way, the lawn of E. Coli cells would produce an oscillatory pattern of active and inactive \"bands\" of color and no color.<br/><br/><p class='header'>THE CONSTRUCT</p><img src='https://static.igem.org/mediawiki/2010/a/a8/IgemConstruct.jpg' height='600px' width='675px'><br/><br/><p class='header'>HOW IT WORKS: LET THERE BE LIGHT</p>The two main components of this system are two quorum sensing constructs that detect the products Lux and Las; the Lux being the signal for a cell to turn off, and the Las being the signal for a cell to turn on.<br/><br/>The following flash animation depicts the initiation of the system; as light hits the initiating construct's light promoter, Lambda activator is produced which activates the \"On Promoter\" which promotes the production of color (the cell is now on), additional Lambda activator and Lux. However, in the presence of Lambda, the Lux activator is unable to bind to the Lux Promoter, thereby keeping the cell on. The final thing to notice about the flash is that some of the Lux activator will diffuse out of this cell into adjacent cells, where the next step of the cycle will take place.<br/><br/><object width='690' height='600'><param name='movie' value='https://static.igem.org/mediawiki/2010/6/60/InitiatorUCD.swf'><embed src='https://static.igem.org/mediawiki/2010/6/60/InitiatorUCD.swf' width='690' height='600'></embed></object><br/><br/><p class='header'>HOW IT WORKS: HOW TO TURN CELLS OFF (besides telling bad jokes...)</p>The next step in the cycle is to turn off adjacent cells with the diffused Lux activator. Upon entering a new cell, the Lux will activate the Lux Promoter which triggers the production of Delta activator, 434 repressor and TetR repressor. The Delta repressor is in charge of stopping Las activator (the on signal) from activating the Las Promoter which would otherwise ultimately turn the cell on via the 'On Promoter' construct. The 434 repressor is yet another system that will prevent transcription of the 'On Promoter'. This is a necessary component of the system because there is a very high probability that some Lambda activator will also diffuse from the neighboring cells, which could activate the 'On Promoter' to a certain degree.<br/><br/>Since the Las activator is unable to bind to the Las Promoter, it will find its way out of this cell and diffuse to neighboring cells. The following flash animation depicts this secondary process.<br/><br/><object width='690' height='600'><param name='movie' value='https://static.igem.org/mediawiki/2010/d/d7/LuxAnimation.swf'><embed src='https://static.igem.org/mediawiki/2010/d/d7/LuxAnimation.swf' width='690' height='600'></embed></object><br/><br/><p class='header'>HOW IT WORKS: THE POWER OF LAS</p>The final step in our system's cycle is activating the next adjacent batch of cells to turn on and produce color. This can be achieved by taking advantage of the diffused Las activators produced by the cells in the previous section. The following flash depicts this process.<br/><br/><object width='690' height='600'><param name='movie' value='https://static.igem.org/mediawiki/2010/1/12/FinalflashUCD.swf'><embed src='https://static.igem.org/mediawiki/2010/1/12/FinalflashUCD.swf' width='690' height='600'></embed></object><br/>First, the leftover Las activators from the previous patch of cells makes their way into the new patch of cells. This activates the Las Promoter which promotes the transcription of Lambda activator. From this point on, it is as if the Las activators are acting in place of the light stimulus from the first step: the produced Lambda will activate the 'On Promoter', and the cycle repeats from the beginning. In this way, an oscillating pattern of colored/non-colored bands can be produced on a lawn of our modified cells.<br/><br/> For more details about the construct itself and its construction, please visit the <a href='/Team:UC_Davis/Notebook'>Notebook</a> section."; | ||
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| - | $("#projectContent").fadeOut("slow", function() { document.getElementById("projectContent").innerHTML = | + | $("#projectContent").stop(true, true).fadeOut("slow", function() { document.getElementById("projectContent").innerHTML =spatialContent;}).fadeIn("slow"); |
| - | $("#projectBanner").fadeOut("slow", function() { document.getElementById("projectBanner").innerHTML = "<img src='https://static.igem.org/mediawiki/2010/9/9d/SObanner2.jpg' width='689px'>";}).fadeIn("slow"); | + | $("#projectBanner").stop(true, true).fadeOut("slow", function() { document.getElementById("projectBanner").innerHTML = "<img src='https://static.igem.org/mediawiki/2010/9/9d/SObanner2.jpg' width='689px' height='100px'>";}).fadeIn("slow"); |
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| - | $("#projectContent").fadeOut("slow", function() { document.getElementById("projectContent").innerHTML = pHContent;}).fadeIn("slow"); | + | $("#projectContent").stop(true, true).fadeOut("slow", function() { document.getElementById("projectContent").innerHTML = pHContent;}).fadeIn("slow"); |
| - | $("#projectBanner").fadeOut("slow", function() { document.getElementById("projectBanner").innerHTML = "<img src='https://static.igem.org/mediawiki/2010/a/a8/PhBanner.jpg' width='689px'>";}).fadeIn("slow"); | + | $("#projectBanner").stop(true, true).fadeOut("slow", function() { document.getElementById("projectBanner").innerHTML = "<img src='https://static.igem.org/mediawiki/2010/a/a8/PhBanner.jpg' width='689px'' height='100px'>";}).fadeIn("slow"); |
break; | break; | ||
case 3: | case 3: | ||
| - | $("#projectContent").fadeOut("slow", function() { document.getElementById("projectContent").innerHTML = crosstalkContent;}).fadeIn("slow"); | + | $("#projectContent").stop(true, true).fadeOut("slow", function() { document.getElementById("projectContent").innerHTML = crosstalkContent;}).fadeIn("slow"); |
| - | $("#projectBanner").fadeOut("slow", function() { document.getElementById("projectBanner").innerHTML = "<img src='https://static.igem.org/mediawiki/2010/b/b1/CrosstalkBanner.jpg' width='689px'>";}).fadeIn("slow"); | + | $("#projectBanner").stop(true, true).fadeOut("slow", function() { document.getElementById("projectBanner").innerHTML = "<img src='https://static.igem.org/mediawiki/2010/b/b1/CrosstalkBanner.jpg' width='689px' height='100px'>";}).fadeIn("slow"); |
| - | + | ||
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| + | document.getElementById("projectContent").innerHTML = spatialContent; | ||
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<table class="pikachu"> | <table class="pikachu"> | ||
<tr> | <tr> | ||
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<table class="projects"> | <table class="projects"> | ||
<tr> | <tr> | ||
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| - | <td><a onclick="tabFunction(2)"> | + | <td width='33%'><a onmouseout="changePic('phButton', 'https://static.igem.org/mediawiki/2010/6/6f/PhSensorButton.png')" onmouseover="changePic('phButton', '/wiki/images/e/e7/PhSensorButtonOn.png')" onclick="tabFunction(2)" style="cursor:pointer"><img name='phButton' src='https://static.igem.org/mediawiki/2010/6/6f/PhSensorButton.png' height='100%' width='100%'></a></td> |
| - | <td><a onclick="tabFunction(3)"> | + | <td width='33%'><a onmouseout="changePic('potatoButton', '/wiki/images/f/f3/CpotatoButton.png')" onmouseover="changePic('potatoButton', '/wiki/images/6/6b/CpotatoButtonOn.png')" onclick="tabFunction(3)" style="cursor:pointer"><img name='potatoButton' src='https://static.igem.org/mediawiki/2010/f/f3/CpotatoButton.png' height='100%' width='100%'></a></td> |
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</table> | </table> | ||
Latest revision as of 03:30, 28 October 2010
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