Difference between revisions of "Template:Groningen/Model"
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| − | <h1> | + | <img src="https://static.igem.org/mediawiki/2018/a/a5/T--Groningen--banner_modeling.png" class="responsive-img"> |
| + | <h1>Overview</h1> | ||
| + | <p> | ||
| + | The IGEM team Groningen has invested a lot of effort into developing <u>sophisticated models</u> that simulates all parts of our project. In our quest for producing styrene from the polysaccharide cellulose, the first step is to get our enzymes to the place they need to go; cellulose. As the cellulose binding domain of our mini-cellulosome is responsible for this task, we characterized its cellulose binding properties by creating a <u>cutting edge</u> coarse grained molecular dynamics simulation and running it on our <b>6652 core supercomputer cluster</b> peregrine. The simulation shows the cellulose binding domain as an affinity for cellulose several <b>orders of magnitude higher</b> than the enzymes alone and draw novel insights from this. However by restraining the enzymes together in a scaffold protein, the added rigidity might prove detrimental to enzyme activity. We used an <u>advanced mathematical model</u> to work out the <b>complex system of differential equations</b> that describe this restrained situation, and compared the results to the solubilized enzymes. Luckily, the model shows that restraining the enzymes only impacts their performance negligibly. Finally, we once more harnessed the <b>supercomputing power</b> at our disposal to simulate our synthetic styrene production pathway in the metabolism of <i>S. cerevisiae</i> using a <u>flux based model</u>. We confirmed that yeast is indeed capable of simultaneous growth and high theoretical styrene production. Most strikingly however, we discovered several <b>important metabolic engineering targets</b>, some of which are corroborated by empirical evidence, while others are <b>entirely novel discoveries</b>. Overall all our models have provided us with <u>key insights</u> to aid us in reaching our goal: <b>a sustainable future</b>. | ||
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| + | <br> | ||
| + | <div class="row_m" align="middle"> | ||
| + | <div class="column_m col_box"> | ||
| + | <a href="https://2018.igem.org/Team:Groningen/Model/Molecular_Dynamics"> | ||
| + | <h5>Proximity by affinity</h5> | ||
| + | <img src="https://static.igem.org/mediawiki/2018/9/96/T--Groningen--MD_thumb.png"> | ||
| + | </a> | ||
| + | <br> | ||
| + | <p style="text-align: justify; padding: 8%">Our cutting edge molecular dynamics simulation uses the in-house developed MARTINI coarse grained force field to effectively model the thermodynamic properties of a reductionist view of our system. </p> | ||
| + | </div> | ||
| + | <div class="column_m col_box"> | ||
| + | <a href="https://2018.igem.org/Team:Groningen/Model/Mathematical_Modeling"> | ||
| + | <h5>Cellulose degradation</h5> | ||
| + | |||
| + | <img src="https://static.igem.org/mediawiki/2018/0/03/T--Groningen--math_thumb.png"> | ||
| + | </a> | ||
| + | <p style="text-align: justify; padding: 8%">We were able to model the kinetic behaviour of a reduced enzyme scaffold. This system was described by a system of differential equations that describe the behaviour of the enzyme complex.</p> | ||
| + | </div> | ||
| + | <div class="column_m col_box"> | ||
| + | <a href="https://2018.igem.org/Team:Groningen/Model/Flux_Based_Analysis"> | ||
| + | <h5>Optimizing styrene production</h5> | ||
| + | <img src="https://static.igem.org/mediawiki/2018/4/4e/T--Groningen--fba_thumb.png"> | ||
| + | </a> | ||
| + | <p style="text-align: justify; padding: 8%">By using flux based analysis, the complex network of reactions in the metabolism of <i>S. cerevisiae</i> could be modeled. We used this approach to simulate styrene production and to find metabolic engineering targets.</p> | ||
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<p>Our integrated styrene production pathway in Saccharomyces cerevisiae relies on several key properties to make the process the most efficient in ideal conditions. Our enzyme scaffold which supposedly enhances enzyme activity by bringing the enzymes in closer proximity to each other, and closer to cellulose by a cellulose binding domain. Furthermore our yeast strain needs several metabolic optimizations to maximize styrene production levels. </p> | <p>Our integrated styrene production pathway in Saccharomyces cerevisiae relies on several key properties to make the process the most efficient in ideal conditions. Our enzyme scaffold which supposedly enhances enzyme activity by bringing the enzymes in closer proximity to each other, and closer to cellulose by a cellulose binding domain. Furthermore our yeast strain needs several metabolic optimizations to maximize styrene production levels. </p> | ||
| − | <p>To verify whether an enzyme scaffold indeed enhances enzyme activity, a mathematical model was created to describe catalyzed degradation of cellulose in the presence of our enzymes. The model, based on work by Levine et al. [1], includes an exoglucanase and an endoglucanase, both in bound together and separately in solution. | + | |
| − | + | <p>To verify whether an enzyme scaffold indeed enhances enzyme activity, a <a href="https://2018.igem.org/Team:Groningen/Model/Mathematical_Modeling">mathematical model</a> was created to describe catalyzed degradation of cellulose in the presence of our enzymes. The model, based on work by Levine et al. [1], includes an exoglucanase and an endoglucanase, both in bound together and separately in solution. | |
| − | <p>As it is likely that substrate availability is higher when the scaffold is bound to cellulose, we explored the binding characteristics of | + | The model provides a justification for the use of scaffolding and insight into how to choose enzymes for a scaffold</p> |
| − | + | ||
| − | <p>Finally flux balance modeling was used to model the metabolism of S. cerevisiae after introduction of our heterologous pathway enabling styrene production. Yeast GEM 7.6 [4] was used to model the metabolic network of yeast. Furthermore, the OptForce algorithm [5], implemented in the COBRA toolbox [6], was used to find compatible sets of up- or down-regulated genes. | + | <p>As it is likely that substrate availability is higher when the scaffold is strongly bound to cellulose, we explored the binding characteristics of the cellulose binding domain that is attached to our minicellulosome scaffold using <a href="https://2018.igem.org/Team:Groningen/Model/Molecular_Dynamics">coarse grained molecular dynamics</a>. The cellulose binding domain (CBD) of Cip3A from Clostridium thermocellum (PDB 1NBC by Tormo et al. [2]) was modeled in the presence of a single cellulose fiber using coarse grained molecular dynamics simulations [3]. The simulations affirm the hypothesized binding mechanism of CBD to cellulose, and show that CBD from C. thermocellum has a very high affinity for cellulose fibers. From these simulations, the potential of mean force of the binding interaction could be computed, from which the binding free energy could be derived. The results show that our CBD binds even stronger to cellulose than some other cellulose binding domains known from literature. These results justify including the CBD in the enzyme scaffold as it will likely increase the activity of the complex by substrate proximity.</p> |
| + | |||
| + | <p>Finally <a href="https://2018.igem.org/Team:Groningen/Model/Flux_Based_Analysis">flux balance modeling</a> was used to model the metabolism of S. cerevisiae after introduction of our heterologous pathway enabling styrene production. Yeast GEM 7.6 [4] was used to model the metabolic network of yeast. Furthermore, the OptForce algorithm [5], implemented in the COBRA toolbox [6], was used to find compatible sets of up- or down-regulated genes. | ||
When optimizing for biomass, it was shown that 65% of maximum styrene production is still compatible with normal growth of yeast, justifying our choice for yeast as our host organism.</p> | When optimizing for biomass, it was shown that 65% of maximum styrene production is still compatible with normal growth of yeast, justifying our choice for yeast as our host organism.</p> | ||
<p>To speed up simulations, our models were run using the Groningen University’s state of the art 4000+ core Peregrine cluster.</p> | <p>To speed up simulations, our models were run using the Groningen University’s state of the art 4000+ core Peregrine cluster.</p> | ||
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<h4>References</h4> | <h4>References</h4> | ||
<p>[1] Levine, S. E., Fox, J. M., Blanch, H. W., & Clark, D. S. (2010). A mechanistic model of the enzymatic hydrolysis of cellulose. Biotechnology and Bioengineering. <a href="https://doi.org/10.1002/bit.22789" target="_blank">https://doi.org/10.1002/bit.22789</a></p> | <p>[1] Levine, S. E., Fox, J. M., Blanch, H. W., & Clark, D. S. (2010). A mechanistic model of the enzymatic hydrolysis of cellulose. Biotechnology and Bioengineering. <a href="https://doi.org/10.1002/bit.22789" target="_blank">https://doi.org/10.1002/bit.22789</a></p> | ||
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<p>[6] Heirendt, L., Arreckx, S., Pfau, T., Mendoza, S. N., Richelle, A., Heinken, A., … Fleming, R. M. T. (2017). Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0. ArXiv. <a href="https://doi.org/10.1038/protex.2011.234" target="_blank">https://doi.org/10.1038/protex.2011.234</a></p> | <p>[6] Heirendt, L., Arreckx, S., Pfau, T., Mendoza, S. N., Richelle, A., Heinken, A., … Fleming, R. M. T. (2017). Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0. ArXiv. <a href="https://doi.org/10.1038/protex.2011.234" target="_blank">https://doi.org/10.1038/protex.2011.234</a></p> | ||
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Latest revision as of 18:30, 7 December 2018
Overview
The IGEM team Groningen has invested a lot of effort into developing sophisticated models that simulates all parts of our project. In our quest for producing styrene from the polysaccharide cellulose, the first step is to get our enzymes to the place they need to go; cellulose. As the cellulose binding domain of our mini-cellulosome is responsible for this task, we characterized its cellulose binding properties by creating a cutting edge coarse grained molecular dynamics simulation and running it on our 6652 core supercomputer cluster peregrine. The simulation shows the cellulose binding domain as an affinity for cellulose several orders of magnitude higher than the enzymes alone and draw novel insights from this. However by restraining the enzymes together in a scaffold protein, the added rigidity might prove detrimental to enzyme activity. We used an advanced mathematical model to work out the complex system of differential equations that describe this restrained situation, and compared the results to the solubilized enzymes. Luckily, the model shows that restraining the enzymes only impacts their performance negligibly. Finally, we once more harnessed the supercomputing power at our disposal to simulate our synthetic styrene production pathway in the metabolism of S. cerevisiae using a flux based model. We confirmed that yeast is indeed capable of simultaneous growth and high theoretical styrene production. Most strikingly however, we discovered several important metabolic engineering targets, some of which are corroborated by empirical evidence, while others are entirely novel discoveries. Overall all our models have provided us with key insights to aid us in reaching our goal: a sustainable future.
Proximity by affinity
Our cutting edge molecular dynamics simulation uses the in-house developed MARTINI coarse grained force field to effectively model the thermodynamic properties of a reductionist view of our system.
Cellulose degradation
We were able to model the kinetic behaviour of a reduced enzyme scaffold. This system was described by a system of differential equations that describe the behaviour of the enzyme complex.
Optimizing styrene production
By using flux based analysis, the complex network of reactions in the metabolism of S. cerevisiae could be modeled. We used this approach to simulate styrene production and to find metabolic engineering targets.