Difference between revisions of "Team:UCSF/Description"
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| − | "<p class='headerProjectSub'>BASIC CIRCUIT</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Induction: Doxycycline</b></br></br><img style='float:right;margin-right:0px;margin-top:-80px' src='https://static.igem.org/mediawiki/2015/b/bf/UCSF_Basic_Circuit_Diagram_Part_1-01.png' alt='Circuit Part 1' height='210' width='350'><b>Activate cell through the use of an induction factor and inducible promoters.</b></br></br><b style='color:#368E8C'>Design:</b> | + | "<p class='headerProjectSub'>BASIC CIRCUIT</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Induction: Doxycycline</b></br></br><img style='float:right;margin-right:0px;margin-top:-80px' src='https://static.igem.org/mediawiki/2015/b/bf/UCSF_Basic_Circuit_Diagram_Part_1-01.png' alt='Circuit Part 1' height='210' width='350'><b>Activate cell through the use of an induction factor and inducible promoters.</b></br></br><b style='color:#368E8C'>Design:</b> Exogenous doxycycline binds with rtTA produced by a constitutive promoter (pTEF1) to drive individual/community response signaling cascades and amplify the three signalling modification proteins (for positive feedback, signal degradation, and clustering).</br><b style='color:#368E8C'>Purpose:</b> The transcription factor rtTA is driven by a constitutive pTEF1 mutant promoter. When combined with doxycycline, rtTA drives the pTET promoter that regulates the expression of both our community signal and individual response.</br><b style='color:#368E8C'>BioBricks:</b> pTEF1-m10 promoter (from UCSF 2014, BBa_K1346003).</br></p></br>"); |
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| − | "<p class='headerProjectSub'>BASIC CIRCUIT</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Induction: Doxycycline</b></br></br><img style='float:right;margin-right:0px;margin-top:-80px' src='https://static.igem.org/mediawiki/2015/b/bf/UCSF_Basic_Circuit_Diagram_Part_1-01.png' alt='Circuit Part 1' height='210' width='350'><b>Activate cell through the use of an induction factor and inducible promoters.</b></br></br><b style='color:#368E8C'>Design:</b> | + | "<p class='headerProjectSub'>BASIC CIRCUIT</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Induction: Doxycycline</b></br></br><img style='float:right;margin-right:0px;margin-top:-80px' src='https://static.igem.org/mediawiki/2015/b/bf/UCSF_Basic_Circuit_Diagram_Part_1-01.png' alt='Circuit Part 1' height='210' width='350'><b>Activate cell through the use of an induction factor and inducible promoters.</b></br></br><b style='color:#368E8C'>Design:</b> Exogenous doxycycline binds with rtTA produced by a constitutive promoter (pTEF1) to drive individual/community response signaling cascades and amplify the three signalling modification proteins (for positive feedback, signal degradation, and clustering).</br><b style='color:#368E8C'>Purpose:</b> The transcription factor rtTA is driven by a constitutive pTEF1 mutant promoter. When combined with doxycycline, rtTA drives the pTET promoter that regulates the expression of both our community signal and individual response.</br><b style='color:#368E8C'>BioBricks:</b> pTEF1-m10 promoter (from UCSF 2014, BBa_K1346003).</br></p></br>"); |
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| − | "<p class='headerProjectSub'>BASIC CIRCUIT</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Individual Response: GFP</b></br></br><img style='float:right;margin-right:0px;margin-top:-80px' src='https://static.igem.org/mediawiki/2015/2/27/UCSF_Basic_Circuit_Diagram_Part_2-01.png' alt='Circuit Part 2' height='210' width='350'><b>Cells produce GFP in response to | + | "<p class='headerProjectSub'>BASIC CIRCUIT</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Individual Response: GFP</b></br></br><img style='float:right;margin-right:0px;margin-top:-80px' src='https://static.igem.org/mediawiki/2015/2/27/UCSF_Basic_Circuit_Diagram_Part_2-01.png' alt='Circuit Part 2' height='210' width='350'><b>Cells produce GFP in response to doxycycline.</b></br></br><b style='color:#368E8C'>Design:</b> pTET promoter (induced by Doxycycline + rtTA) drives production of GFP</br><b style='color:#368E8C'>Purpose:</b> This is our individual response. The GFP output is directly correlated with the concentration of doxycycline used to induce the cells (see data), allowing us to see how each cell in the population is responding to the stimulus.</br></p></br><div class='seeData'><a href='https://2015.igem.org/Team:UCSF/Results#pageLeakyGraph' target='_blank'>See Our Data <img class='readMoreArrow' src='https://static.igem.org/mediawiki/2015/6/68/UCSF_Read_More_Arrow.png' alt='Next Arrow' height='35' width='35'></a></div></br>"); |
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| − | "<p class='headerProjectSub'>BASIC CIRCUIT</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Communication Signal: mFα</b></br></br><img style='float:right;margin-right:0px;margin-top:-80px' src='https://static.igem.org/mediawiki/2015/a/aa/UCSF_Basic_Circuit_Diagram_Part_3-01.png' alt='Circuit Part 3' height='210' width='350'><b>Cells produce mFα in response to doxcycline.</b></br></br><b style='color:#368E8C'>Design:</b> pTET | + | "<p class='headerProjectSub'>BASIC CIRCUIT</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Communication Signal: mating factor α (mFα)</b></br></br><img style='float:right;margin-right:0px;margin-top:-80px' src='https://static.igem.org/mediawiki/2015/a/aa/UCSF_Basic_Circuit_Diagram_Part_3-01.png' alt='Circuit Part 3' height='210' width='350'><b>Cells produce mFα in response to doxcycline.</b></br></br><b style='color:#368E8C'>Design:</b> pTET promoter (induced by Doxycycline+rtTA) drives production of mFα.</br><b style='color:#368E8C'>Purpose:</b> mFα is our communication signal. All cells in the population will be able to both sense and secrete this small peptide. This allows them to process their original, and individual, responses to the stimulus through communication and community coordination.</br></p></br>"); |
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| − | "<p class='headerProjectSub'>BASIC CIRCUIT</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Community Response: RFP</b></br></br><img style='float:right;margin-right:0px;margin-top:- | + | "<p class='headerProjectSub'>BASIC CIRCUIT</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Community Response: RFP (and transcription factor production)</b></br></br><img style='float:right;margin-right:0px;margin-top:-30px' src='https://static.igem.org/mediawiki/2015/8/8a/UCSF_Basic_Circuit_Diagram_Part_5_New-01.png' alt='Circuit Part 3' height='210' width='350'><b>Cells produce RFP and LexADBD in response to mFα.</b></br></br><b style='color:#368E8C'>Design:</b> mFα-inducible promoter drives production of RFP-LexADBD fusion protein.</br><b style='color:#368E8C'>Purpose:</b> The community response is driven by local concentrations of alpha factor in the culture. Alpha factor is sensed by a cell-surface receptor called Ste2, which turns on a signaling cascade that drives certain alpha-factor responsive promoters -- in our case pAga1. </br> We have coupled this RFP output to the production of a transcription factor (TF) by creating a TF-RFP fusion. Our transcription factor itself is a synthetic fusion of the DNA-binding domain of the repressor protein LexA with transcription activation domains from VP64 (referred to here as LexADBD). When sensing alpha factor, these cells produce both a community response in the form of RFP as well as a transcription factor which can be used to control downstream genes.</br><b style='color:#368E8C'>BioBricks:</b> pAga1 promoter (BBa_K1829005). </br></p></br><div class='seeData'><a href='https://2015.igem.org/Team:UCSF/Results#pageAlphaGraph' target='_blank'>See Our Data <img class='readMoreArrow' src='https://static.igem.org/mediawiki/2015/6/68/UCSF_Read_More_Arrow.png' alt='Next Arrow' height='35' width='35'></a></div></br>"); |
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| − | "<p class='headerProjectSub'>BASIC CIRCUIT</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Feedback: Increased Activation</b></br></br><img style='float:right;margin-right:0px;margin-top:-80px' src='https://static.igem.org/mediawiki/2015/4/45/UCSF_Basic_Circuit_Diagram_Part_6-01.png' alt='Circuit Part 3' height='210' width='350'><b> | + | "<p class='headerProjectSub'>BASIC CIRCUIT</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Feedback: Increased Activation</b></br></br><img style='float:right;margin-right:0px;margin-top:-80px' src='https://static.igem.org/mediawiki/2015/4/45/UCSF_Basic_Circuit_Diagram_Part_6-01.png' alt='Circuit Part 3' height='210' width='350'><b>Feedback driven by downstream promoter.</b></br></br><b style='color:#368E8C'>Design:</b> LexADBD-inducible promoter drives different genes that can affect our communication parameters.</br><b style='color:#368E8C'>Purpose:</b> The LexADBD transcription factor binds to and activates genes downstream of the LexA operator sequences (LexAOps). This drives the genes used for positive feedback, specifically the alpha factor and Ste2 genes. This creates a positive feedback loop that increases secretion and reception as more alpha factor is sensed. This is one method used to create divergence. To see more on these altered communication parameters, see “Can You Sense Me Now” and “Cellular Hotspots” above. </br><b style='color:#368E8C'>BioBricks:</b> Ste2 receptor, improved by 2015 UCSF by submitting a sample of the existing part (BBa_I766204) and building a BioBrick compatible version to add to the Registry (BBa_K1829007). </br></p></br>"); |
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| − | "<p class='headerProjectSub'>CAN YOU SENSE ME NOW?</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Positive Feedback</b></br></br><img style='float:right;margin-right:100px;margin-top:0px' src='https://static.igem.org/mediawiki/2015/9/9d/UCSF_Increased_Secretion.png' alt='Increased Secretion' height='250' width='250'><b>Increased Secretion: Generating more alpha factor signal to increase communication range and amplify local concentrations.</b></br></br><b style='color:#368E8C'>Design:</b> LexA transcription factor inducible promoter driving production of mFα.</br><b style='color:#368E8C'>Purpose:</b> Activated cells will up-regulate mFα, which will produce and secrete more alpha factor signal to the community. This will generate a positive feedback loop that will amplify both communication range and local concentrations of signal, essentially generating local communities of activated cells. We believe that this motif is essential for community decision making because increased secretion will allow cells to “talk” to individuals farther away in the community. However, through diffusion mechanisms, alpha factor will create a concentration gradient, allowing local cells to sense the community signal more strongly.</br><b style='color:#368E8C'>Inspiration:</b> T | + | "<p class='headerProjectSub'>CAN YOU SENSE ME NOW?</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Positive Feedback</b></br></br><img style='float:right;margin-right:100px;margin-top:0px' src='https://static.igem.org/mediawiki/2015/9/9d/UCSF_Increased_Secretion.png' alt='Increased Secretion' height='250' width='250'><b>Increased Secretion: Generating more alpha factor signal to increase communication range and amplify local concentrations.</b></br></br><b style='color:#368E8C'>Design:</b> LexA transcription factor inducible promoter driving production of mFα.</br><b style='color:#368E8C'>Purpose:</b> Activated cells will up-regulate mFα, which will produce and secrete more alpha factor signal to the community. This will generate a positive feedback loop that will amplify both communication range and local concentrations of signal, essentially generating local communities of activated cells. We believe that this motif is essential for community decision making because increased secretion will allow cells to “talk” to individuals farther away in the community. However, through diffusion mechanisms, alpha factor will create a concentration gradient, allowing local cells to sense the community signal more strongly.</br><b style='color:#368E8C'>Inspiration:</b> T cells, through sensing and secreting the community signal IL2, will upregulate secretion of IL2 and “talk” to neighboring T cells better <sup>[6, 11]</sup>.</br></p></br><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><img style='float:right;margin-right:100px;margin-top:0px' src='https://static.igem.org/mediawiki/2015/8/8c/UCSF_Increased_Receptor.png' alt='Increased Receptor' height='250' width='250'><b>Increased Reception: Production of more alpha factor receptor to sense more alpha factor and polarize communities.</b></br></br><b style='color:#368E8C'>Design:</b> LexA transcription factor inducible promoter driving production of Ste2.</br><b style='color:#368E8C'>Purpose:</b> Activated cells will up-regulate Ste2, an integral ligand receptor of alpha factor in the mating pathway. This will allow cells to sense more alpha factor, producing a selfish feedback loop and amplifying the gap between “ON” and “OFF” states. Due to proximity of signal and receptor, cells with high Ste2 expression will engage in asocial behavior and sense signal they produced themselves. This selfish feedback will sharpen concentration gradients and create stronger local concentrations of alpha factor.</br><b style='color:#368E8C'>Inspiration:</b> Similar to the positive feedback of IL2 secretion in T cells, sensing IL2 will upregulate the production of IL2 receptor. This allows cells to “listen” to their neighbors better <sup>[4, 10, 11]</sup>.</br></p></br><div class='seeData'><a href='https://2015.igem.org/Team:UCSF/Results#pageFeedbackGraph' target='_blank'>See Our Data <img class='readMoreArrow' src='https://static.igem.org/mediawiki/2015/6/68/UCSF_Read_More_Arrow.png' alt='Next Arrow' height='35' width='35'></a></div></br>"); |
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| − | "<p class='headerProjectSub'>RAISING THE BAR1</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Signal Degradation</b></br></br><img style='float:right;margin-right:100px;margin-top:0px' src='https://static.igem.org/mediawiki/2015/d/da/UCSF_Signal_Degradation_2.png' alt='Signal Degradation' height='250' width='250'><b>Using an alpha factor protease to set a threshold for cellular activation.</b></br></br><b style='color:#368E8C'>Design:</b> pTEF1 constitutive promoter (and mutants) driving production of Bar1.</br><b style='color:#368E8C'>Purpose:</b> We have engineered constitutive expression of Bar1, an endogenous alpha factor protease, with varying activity levels. Degradation of mFα signal will sharpen concentration gradients and “raise the bar” for cells to sense the community and activate a RFP community response. This will set | + | "<p class='headerProjectSub'>RAISING THE BAR1</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Signal Degradation</b></br></br><img style='float:right;margin-right:100px;margin-top:0px' src='https://static.igem.org/mediawiki/2015/d/da/UCSF_Signal_Degradation_2.png' alt='Signal Degradation' height='250' width='250'><b>Using an alpha factor protease to set a threshold for cellular activation.</b></br></br><b style='color:#368E8C'>Design:</b> pTEF1 constitutive promoter (and mutants) driving production of Bar1.</br><b style='color:#368E8C'>Purpose:</b> We have engineered constitutive expression of Bar1, an endogenous alpha factor protease, with varying activity levels. Degradation of mFα signal will sharpen concentration gradients and “raise the bar” for cells to sense the community and activate a RFP community response. This will set the threshold for cells to activate and thus increase the gap between “ON” and “OFF” cells.</br><b style='color:#368E8C'>Inspiration:</b> The immune system has a subset of T-regulatory cells that will sponge up the IL2 signal secreted by T cells. This prevents all cells from proliferating and attacking, and selects for the cells best suited for the response. T-regulatory cells have been shown to be essential in preventing autoimmune diseases <sup>[3, 5, 6]</sup>.</br></p></br><div class='seeData'><a href='https://2015.igem.org/Team:UCSF/Results#pageDegradationGraph' target='_blank'>See Our Data <img class='readMoreArrow' src='https://static.igem.org/mediawiki/2015/6/68/UCSF_Read_More_Arrow.png' alt='Next Arrow' height='35' width='35'></a></div></br>"); |
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| − | "<p class='headerProjectSub'>CELLULAR HOTSPOTS</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Feedback: Activated Clustering</b></br></br><img style='float:right;margin-right:100px;margin-top:0px' src='https://static.igem.org/mediawiki/2015/e/e7/UCSF_Activated_Clustering.png' alt='Clustering' height='250' width='423'><b>Maintaining activated cells in local clusters to strengthen communication.</b></br></br><b style='color:#368E8C'>Design:</b> LexA transcription factor inducible promoter driving production of Aga-Mgfp5.</br><b style='color:#368E8C'>Purpose:</b> Repurposing parts from UCSF 2011, we are using modified surface display proteins in yeast to induce cell adhesion through gene specific heterodimers. Particularly, we are | + | "<p class='headerProjectSub'>CELLULAR HOTSPOTS</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Feedback: Activated Clustering</b></br></br><img style='float:right;margin-right:100px;margin-top:0px' src='https://static.igem.org/mediawiki/2015/e/e7/UCSF_Activated_Clustering.png' alt='Clustering' height='250' width='423'><b>Maintaining activated cells in local clusters to strengthen communication.</b></br></br><b style='color:#368E8C'>Design:</b> LexA transcription factor inducible promoter driving production of Aga-Mgfp5.</br><b style='color:#368E8C'>Purpose:</b> Repurposing parts from UCSF 2011, we are using modified surface display proteins in yeast to induce cell adhesion through gene specific heterodimers. Particularly, we are using a yeast surface display system that utilizes a fusion of two proteins: (1) a cell wall anchoring protein (Aga1 and Aga2) and (2) a bioadhesive endogenously found in mussels (Mgfp5). In close proximity, cells should be able to communicate with their local community more efficiently. This will essentially polarize cellular communities into their local clusters.</br><b style='color:#368E8C'>Inspiration:</b> We know from natural systems that distance between individuals in a community is a crucial factor in how well they communicate with one another. T cells, after being activated by an antigen, will be maintained in lymph nodes by a protein, CD69. This spatial retention of activated cells will allow them to talk better to one another since they are all within a small range of communication <sup>[9]</sup>.</br>This motif is also found in quorum sensing <i>V. fischeri</i>, who sense and secrete a species specific autoinducer that allows individuals to measure population size. These organisms are found in the light organs of the Hawaiian Bobtail Squid, and, when enclosed at high cell density, are able to communicate with another about their decision to bioluminesce. This is due to the limited diffusion of the signaling molecule, and thus, more effective communication <sup>[12]</sup>.</br></p></br><div class='seeData'><a href='https://2015.igem.org/Team:UCSF/Results#pageClusteringGraph' target='_blank'>See Our Data <img class='readMoreArrow' src='https://static.igem.org/mediawiki/2015/6/68/UCSF_Read_More_Arrow.png' alt='Next Arrow' height='35' width='35'></a></div></br>"); |
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| − | "<p class='headerProjectSub'>INTRODUCTION</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Inspiration</b></br></br>We drew inspiration for this sense and secrete signaling motif by its ubiquity in natural systems <sup>[1]</sup>. By sensing a self-generated signal, cells participate in social and asocial behaviors that allow them to communicate decisions effectively with their neighbors <sup>[13]</sup>. This process is generally stochastic, due to the random local concentration makeups and molecular proximities of cells and signal <sup>[2]</sup>. However, sense and secrete signaling motifs allow for powerful mechanisms to alter community phenotypes. Cellular populations use this signaling molecule to create robust decisions for the whole population such as convergence, divergence, cell differentiation, or other community responses.</br></br>Sense and secrete circuits are found in most bacterial communities through species specific quorum sensing <sup>[12]</sup>. V. fischeri, aquatic bacteria that bioluminesce at certain cell densities, sense and secrete an autoinducer to keep a census of their population. Low signal concentrations are correlated with low cell density and high signal concentrations are correlated with high cell density. However, when confined to a small area, these signal concentrations are dramatically amplified and all bacteria activate a signaling cascade to bioluminesce. This is an example of cellular populations averaging out individual variation through communication.</br></br>Sense and secrete systems are also found in the adaptive immune system, which is comprised of T | + | "<p class='headerProjectSub'>INTRODUCTION</p><div class='headerBreakSub' style='width:100%'></div></br><p class='content1'><b style='color:#368E8C;font-size:1.5em'>Inspiration</b></br></br>We drew inspiration for this sense and secrete signaling motif by its ubiquity in natural systems <sup>[1]</sup>. By sensing a self-generated signal, cells participate in social and asocial behaviors that allow them to communicate decisions effectively with their neighbors <sup>[13]</sup>. This process is generally stochastic, due to the random local concentration makeups and molecular proximities of cells and signal <sup>[2]</sup>. However, sense and secrete signaling motifs allow for powerful mechanisms to alter community phenotypes. Cellular populations use this signaling molecule to create robust decisions for the whole population such as convergence, divergence, cell differentiation, or other community responses.</br></br>Sense and secrete circuits are found in most bacterial communities through species specific quorum sensing <sup>[12]</sup>. <i>V. fischeri</i>, aquatic bacteria that bioluminesce at certain cell densities, sense and secrete an autoinducer to keep a census of their population. Low signal concentrations are correlated with low cell density and high signal concentrations are correlated with high cell density. However, when confined to a small area, these signal concentrations are dramatically amplified and all bacteria activate a signaling cascade to bioluminesce. This is an example of cellular populations averaging out individual variation through communication.</br></br>Sense and secrete systems are also found in the adaptive immune system, which is comprised of T cells with varied responses to a specific antigen <sup>[4, 10, 11]</sup>. When a T cell senses an antigen, it initiates a signaling cascade in which a signaling cytokine, IL2, is secreted and a receptor for IL2 is produced <sup>[11]</sup>. After communicating to neighboring T cells with IL2, the T cell population coordinates its efforts to proliferate only the cells best suited to fight off the antigen. Thus, we see varied individual responses leading way to divergent community responses in T cell populations after communication.</br></br>Community decision making can also be found in bacterial communities, such as B. subtilis <sup>[8]</sup>. These cellular populations are made of genetically identical cells that utilize extracellular signals to differentiate into phenotypically different cells that play specific roles in the survival of the community. B. subtilis utilizes a sense-and-secrete motif, similar to quorum sensing autoinducers, that couple with local environmental factors to assign “jobs” to individual cells in the community, and thus different molecular makeups. By communicating with members of their community, B. subtilis communities are able to amplify individual responses to differentiate cells into multiple distinct cell fates.</br></p></br>"); |
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| − | Cells in a population can have varied responses to a stimulus | + | Cells in a population can have varied responses to a stimulus but are able to coordinate their responses through communication motifs. Chemical signaling to neighbors in a community can allow populations to make more robust and effective decisions as a collective whole. T cells, for instance, need to know whether or not to proliferate to attack a given antigen. If too many proliferate, an autoimmune disorder is generated. If too few proliferate, the antigen continues to attack the body. By sensing the antigen at varying levels and communicating with the population, each T cell knows whether or not it should activate and what level to activate at, in order to carry on their function properly. |
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| − | + | Nevertheless, how do these cells communicate and how do they understand their role as part of the collective? How do these genetically identical cells in the same population differentiate themselves from others? What motifs are necessary to elicit a bimodal response, in which high activating cells stay ON and low activating cells stay OFF? Our goal this year is to understand these questions and to take advantage of the natural variation found within cells of the same population in order to amplify that difference and create two divergent responses. | |
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| − | <p class="headerProject" style="font-size:5em">SYNTHETIC | + | <p class="headerProject" style="font-size:5em">SYNTHETIC COMMUNITY</p> |
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Latest revision as of 03:15, 19 September 2015
BACKGROUND
Cells in a population can have varied responses to a stimulus but are able to coordinate their responses through communication motifs. Chemical signaling to neighbors in a community can allow populations to make more robust and effective decisions as a collective whole. T cells, for instance, need to know whether or not to proliferate to attack a given antigen. If too many proliferate, an autoimmune disorder is generated. If too few proliferate, the antigen continues to attack the body. By sensing the antigen at varying levels and communicating with the population, each T cell knows whether or not it should activate and what level to activate at, in order to carry on their function properly. Nevertheless, how do these cells communicate and how do they understand their role as part of the collective? How do these genetically identical cells in the same population differentiate themselves from others? What motifs are necessary to elicit a bimodal response, in which high activating cells stay ON and low activating cells stay OFF? Our goal this year is to understand these questions and to take advantage of the natural variation found within cells of the same population in order to amplify that difference and create two divergent responses. Our genetic circuit will utilize a stimulus that activates a fluorescent readout for individual response (GFP) and the secretion of a communication signal that is sensed and secreted by all members of the community. This community signal will in turn activate a fluorescent readout for community response (RFP).
SYNTHETIC COMMUNITY
Click on the part of our circuit you are interested in learning about in the image above.
REFERENCES
- Balázsi, Gábor, Alexander Van Oudenaarden, and James J. Collins. "Cellular Decision Making and Biological Noise: From Microbes to Mammals." Cell 144.6 (2011): 910-25.
- Cotari, Jesse W., Guillaume Voisinne, and Grégoire Altan-Bonnet. "Diversity Training for Signal Transduction: Leveraging Cell-to-cell Variability to Dissect Cellular Signaling, Differentiation and Death." Current Opinion in Biotechnology 24.4 (2013): 760-66.
- Diener, Christian, Gabriele Schreiber, Wolfgang Giese, Gabriel Del Rio, Andreas Schröder, and Edda Klipp. "Yeast Mating and Image-Based Quantification of Spatial Pattern Formation." PLoS Comput Biol PLoS Computational Biology 10.6 (2014).
- Feinerman, O., J. Veiga, J. R. Dorfman, R. N. Germain, and G. Altan-Bonnet. "Variability and Robustness in T Cell Activation from Regulated Heterogeneity in Protein Levels." Science 321.5892 (2008): 1081-084.
- Höfer, Thomas, Oleg Krichevsky, and Grégoire Altan-Bonnet. "Competition for IL-2 between Regulatory and Effector T Cells to Chisel Immune Responses." Front. Immun. Frontiers in Immunology 3 (2012).
- Huberman, L. B., and A. W. Murray. "Genetically Engineered Transvestites Reveal Novel Mating Genes in Budding Yeast." Genetics 195.4 (2013): 1277-290.
- Jahn, Michael, Annett Mölle, Gerhard Rödel, and Kai Ostermann. "Temporal and Spatial Properties of a Yeast Multi-Cellular Amplification System Based on Signal Molecule Diffusion." Sensors 13.11 (2013): 14511-4522.
- López, Daniel, and Roberto Kolter. "Extracellular Signals That Define Distinct and Coexisting Cell Fates in Bacillus Subtilis." FEMS Microbiology Reviews FEMS Microbiol Rev 34.2 (2010): 134-49.
- Shiow, Lawrence R., David B. Rosen, Naděžda Brdičková, Ying Xu, Jinping An, Lewis L. Lanier, Jason G. Cyster, and Mehrdad Matloubian. "CD69 Acts Downstream of Interferon-α/β to Inhibit S1P1 and Lymphocyte Egress from Lymphoid Organs." Nature 440.7083 (2006): 540-44.
- Tkach, Karen, and Grégoire Altan-Bonnet. "T Cell Responses to Antigen: Hasty Proposals Resolved through Long Engagements." Current Opinion in Immunology 25.1 (2013): 120-25.
- Tkach, Karen E., Debashis Barik, Guillaume Voisinne, Nicole Malandro, Matthew M. Hathorn, Jesse W. Cotari, Robert Vogel, Taha Merghoub, Jedd Wolchok, Oleg Krichevsky, and Grégoire Altan-Bonnet. "T Cells Translate Individual, Quantal Activation into Collective, Analog Cytokine Responses via Time-integrated Feedbacks." ELife 3 (2014).
- Waters, Christopher M., and Bonnie L. Bassler. "QUORUM SENSING: Cell-to-Cell Communication in Bacteria." Annual Review of Cell and Developmental Biology Annu. Rev. Cell Dev. Biol. 21.1 (2005): 319-46.
- Youk, H., and W. A. Lim. "Secreting and Sensing the Same Molecule Allows Cells to Achieve Versatile Social Behaviors." Science 343.6171 (2014): 1242782.
