Difference between revisions of "Team:Amazonas Brazil/Model"
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<li><a class="bar_text" href="https://2017.igem.org/Team:Amazonas_Brazil/Demonstrate">Demonstrate</a></li> | <li><a class="bar_text" href="https://2017.igem.org/Team:Amazonas_Brazil/Demonstrate">Demonstrate</a></li> | ||
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| − | <li><a class="bar_text" href="https://2017.igem.org/Team:Amazonas_Brazil/Applied_Design">Design</a></li> | + | <li><a class="bar_text" href="https://2017.igem.org/Team:Amazonas_Brazil/Applied_Design">Applied Design</a></li> |
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| − | Thus, for example, the induction of CRISPeasy machinery starts from its inputs (L-arabinose AND IPTG), which induce Cas9 and RecA expression, respectively. We need to consider, then, the degradation of chemical signals inductors, that influence directly gene expression. After its activation, Cas9 forms a catalytically active ribonucleoprotein (RNP) complex with the sgRNA, therefore, we can model one more variation: allosteric change in Cas9, associating and dissociating with the sgRNA. That and other variables, which compose the complex cell machinery, are addressed in our math modelling, aiming to predict the performance of our <a href="https://static.igem.org/mediawiki/2017/f/f9/Amazonas_Brazil_Modelling.pdf">toolbox<a>. | + | Thus, for example, the induction of CRISPeasy machinery starts from its inputs (L-arabinose AND IPTG), which induce Cas9 and RecA expression, respectively. We need to consider, then, the degradation of chemical signals inductors, that influence directly gene expression. After its activation, Cas9 forms a catalytically active ribonucleoprotein (RNP) complex with the sgRNA, therefore, we can model one more variation: allosteric change in Cas9, associating and dissociating with the sgRNA. That and other variables, which compose the complex cell machinery, are addressed in our math modelling, aiming to predict the performance of our <a class="ex1" href="https://static.igem.org/mediawiki/2017/f/f9/Amazonas_Brazil_Modelling.pdf">toolbox<a>. |
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Latest revision as of 02:05, 2 November 2017
Modelling
Even a chemical reaction to form a water molecule has mathematical mechanisms involved, so why should it be different with cell process? Through the years, many math models were developed to clarify doubts and to predict biochemical phenomenons. Alongside chemical kinetics, models can describe and characterize observed relations between cellular elements, metabolic pathways, cell signaling and gene regulation. To test the toolbox we built, our modelling was performed aiming to understand what could be the possible problems and how to overcome them, in order to promote advances in synthetic biology.
Which circumstances have influence in the activation and execution of CRISPeasy toolbox?
The primal objective of our math modelling was to understand the activation and circumstances of the toolbox we engineered for standardized genome editing, CRISPeasy. Therefore, aiming to predict lab experiments and go one step further to implement our project, we connected math principles to uncover the variables of cell machinery that affect the execution of our softwares (i.e., gene expression) and performance of our hardwares (i.e., proteins).
Thus, for example, the induction of CRISPeasy machinery starts from its inputs (L-arabinose AND IPTG), which induce Cas9 and RecA expression, respectively. We need to consider, then, the degradation of chemical signals inductors, that influence directly gene expression. After its activation, Cas9 forms a catalytically active ribonucleoprotein (RNP) complex with the sgRNA, therefore, we can model one more variation: allosteric change in Cas9, associating and dissociating with the sgRNA. That and other variables, which compose the complex cell machinery, are addressed in our math modelling, aiming to predict the performance of our toolbox.
