Difference between revisions of "Team:Heidelberg/Sandbox1025"

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{{Heidelberg/boxopen|Week 34{{#tag:html|<h2>Optopace</h2>
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Word2Vec Embeddings on Proteinsequences
 
Word2Vec Embeddings on Proteinsequences
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<h2>Modeling</h2>
 
<h2>Modeling</h2>
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A first numeric model of PredCel works without oscillations. Graphs look reasonable so far. Modelling was performed on three levels: On the lowest level one Step of Predcel monitoring Phage concentration as well as uninfected, infected and phage producing E. coli concentration [Graph of level 1 Predcel model](170820mod-lvl1.png). One level above all the concentrations were tracked over 100 iterations of Predcel [Graph of level 2 Predcel model](170820mod-lvl2.png). And on the third level different sets of values for starting fitness, starting phage concentration and starting E. coli concentration were tested. In this case we only monitored how long the phage titer at the end of each iteration of Predcel stayed above 1 pfu/mL and below 1e8 pfu/mL.[Graph of level 3 Predcel model](170820mod-lvl3.png)
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A first numeric model of PredCel works without oscillations. Graphs look reasonable so far. Modelling was performed on three levels: On the lowest level one Step of Predcel monitoring Phage concentration as well as uninfected, infected and phage producing E. coli concentration [Graph of level 1 Predcel model](170820mod-lvl1.png). One level above all the concentrations were tracked over 100 iterations of Predcel [Graph of level 2 Predcel model](170820mod-lvl2.png). And on the third level different sets of values for starting fitness, starting phage concentration and starting E. coli concentration were tested. In this case we only monitored how long the phage titer at the end of each iteration of Predcel stayed above 1 pfu/mL and below 1e8 pfu/mL.[Graph of level 3 Predcel model](170820mod-lvl3.png)
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At least the two higher levels probably only work in python, but maybe an interactive version of what happens during one iteration is possible. A final more comfortable version of the script was started.
 
At least the two higher levels probably only work in python, but maybe an interactive version of what happens during one iteration is possible. A final more comfortable version of the script was started.
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Clustering of k-mers based on amino acid content is a first hint, embeddings can be useful for input of neural networks. However their performance can probably only be measured indirectly by comparing the performance of a single architecture on different inputs.
 
Clustering of k-mers based on amino acid content is a first hint, embeddings can be useful for input of neural networks. However their performance can probably only be measured indirectly by comparing the performance of a single architecture on different inputs.
 
<h2>Modeling</h2>
 
<h2>Modeling</h2>
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Calculations of medium consumption for iGEM goes green were performed and made interactive. A heatmap visualizes medium consumption, calculation of ideal turbidostat and lagoon sizes are possible. The user can annotate own experiments and compare them to others.
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Calculations of medium consumption for iGEM goes green were performed and made interactive. A heatmap visualizes medium consumption, calculation of ideal turbidostat and lagoon sizes are possible. The user can annotate own experiments and compare them to others.
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[Heatmap with default values](170827mod-heatmap.png)
 
[Heatmap with default values](170827mod-heatmap.png)
 
Experimentation with elements of a Predcel model based on distributions instead of scalars were started. The idea is that a population of phages does not have one fitness between 0 and 1 but rather has individuals that have different fitness values. In this more complex model the concentrations have to be calculated for each phage fitness value, depending on the amount of phages that have that fitness value. The fitness distribution is changed by mutation and by selection. The first naive approach for mutation was programmed. It simply substracts a given percentage of the difference between the amount of a fitness value and the mean amount from the amount of a fitness value. Obviously this is oversimplified and will therefore be replaced by a model based on the idea that every sequence that mutates gets better or worse with normally distributed changes.
 
Experimentation with elements of a Predcel model based on distributions instead of scalars were started. The idea is that a population of phages does not have one fitness between 0 and 1 but rather has individuals that have different fitness values. In this more complex model the concentrations have to be calculated for each phage fitness value, depending on the amount of phages that have that fitness value. The fitness distribution is changed by mutation and by selection. The first naive approach for mutation was programmed. It simply substracts a given percentage of the difference between the amount of a fitness value and the mean amount from the amount of a fitness value. Obviously this is oversimplified and will therefore be replaced by a model based on the idea that every sequence that mutates gets better or worse with normally distributed changes.
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{{Heidelberg/footer}}
 
{{Heidelberg/footer}}

Revision as of 21:12, 20 October 2017


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