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Part, architecture or context | Description | Reference |
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Promoters | Strength prediction tool for sigmaE promoters, using a position weight matrix-based core promoter model and the length and frequency of A- and T-tracts of UP elements. | [6] |
Strength prediction tool for sigma70 promoters, using partial least squares regression. | [7] |
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Promoter-RBS pairs | Strength prediction tool for sigma70 promoter-RBS pairs, using an artificial neural network. | [8] |
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RBSs | RBS Calculator: a web-based tool for RBS strength prediction and forward engineering, frequently updated and able to design RBS libraries. | [9] |
RBS Designer: a stand-alone tool for RBS strength prediction and forward engineering, it considers long-range interactions within RNA and it can predict the translation efficiency of mRNAs that may potentially fold into more than one structure. | [10] |
UTR Designer: a web-based tool for RBS strength prediction and forward engineering, able to design RBS libraries and with the codon editing option to change RNA secondary structures. | [11] |
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Genes | GeMS: web-based tool for gene design, using a codon optimization strategy based on codon randomization via frequency tables. | [12] |
Optimizer: web-based tool for gene design using three possible codon optimization strategies: “one amino acid-one codon”, randomization (called “guided random”) and a hybrid method (called “customized one amino acid-one codon”). | [13] |
Synthetic Gene Designer: web-based tool for gene design with expanded range of codon optimization methods: full (“one amino acid-one codon”), selective (rare codon replacement) and probabilistic (randomization-based) optimization. | [14] |
Gene Designer: stand-alone tool for gene design using a codon randomization method based on frequency tables and with the possibility to filter out secondary structures and Shine-Dalgarno internal motifs. | [15] |
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Terminators | Termination efficiency prediction tool based on a linear regression model using a set of sequence-specific features identified via stepwise regression. | [16] |
Termination efficiency prediction tool based on a biophysical model using a set of free energies, previously identified as important features. | [17] |
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Interconnected networks | A range of empirical or mechanistic ODE or steady-state models can be used to predict complex systems behaviour from the knowledge of individual parts/devices parameters. | [5, 18–21] |
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Architecture | Protein expression prediction for the first gene of an operon, given the downstream mRNA length, via a linear regression model. | [22] |
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Context | Mechanistic ODE models where the DNA copy number is explicitly represented. | [23] |
Protein expression prediction tool, based on linear regression model, given the chromosomal position of the gene and its orientation. |
[24] |
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