Team:DTU-Denmark/Software
Title
leader under the title, short introduction. Ubique moderatius efficiantur eum et, dico oporteat recusabo ius cu, pro id modus sadipscing. Maluisset patrioque eum ad, mel eius doctus accommodare eu, minimum deleniti repudiandae mel ea. Noster nostrud diceret sea no. Eos an nullam molestiae signiferumque, vel ne laudem ignota oblique. Duo te luptatum percipitur signiferumque, at dicunt iriure dolorem his.
Section 1
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Someone famous in Source Title
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Section 2
Regardless of the topic, iGEM projects often create or adapt computational tools to move the project forward. Because they are born out of a direct practical need, these software tools (or new computational methods) can be surprisingly useful for other teams. Without necessarily being big or complex, they can make the crucial difference to a project's success. This award tries to find and honor such "nuggets" of computational work.
Inspiration
Here are a few examples from previous teams:
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Section 2.1
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Section 2.2
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Section 2.3
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Theory
The central issue in codon optimization is to determine which codons are most efficiently translated for each amino acid. The quantity needed for this task is called 'translatability' and is denoted \(W_i\) for the \(i\)'th codon.
To accomplish this, we have chosen to use a tRNA Adaptation Index-based method (tAI) (dosReis et. al. 2004) REFERENCE. The fundamental assumption behind this method is that highly expressed proteins have their genes encoded with a set of codons that is overall more susceptible to tRNA-binding and translation compared to less expressed proteins. Hence, this optimization estimates the codon preferences such that the correlation between protein level and tAI is maximized.
The formulas for calculating this are stated in Table 1 in dosReis 2004 (SHOULD WE STATE THEM HERE?). Using this, all 64 \(W_i\)'s can be calculated in one matrix multiplication, by letting \(G\) be the 4\(\times\)16 matrix consisting of the tGCN's (in TaiCO referred to as 'gcn') and letting \(S\) be the 4\( \times\)4 matrix containing the (1 - \(s_{ij}\)) values. Hence,
$$W = SG \frac{W_i}{W_{\text{max}}}$$
The computed \(W_i\)'s are the normalized by setting \(w_i = \frac{W_i}{W_{\text{max}}}\), and those normalized translatabilities, \(w_i\) do then form the basis for codon selection. Higher \(w_i\)-values are simply selected over lower values. This concludes the method for codon selection.
The \(G\) matrix
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The \(S\) matrix
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Section 4
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Section 5
Has ut facer debitis, quo eu agam purto. In eum justo aeterno. Sea ut atqui efficiantur, mandamus deseruisse at est, erat natum cum eu. Quot numquam in vel. Salutatus euripidis moderatius qui ex, eu tempor volumus vituperatoribus has, ius ea ullum facer corrumpit.
