Table 1.

Selected computational methods and tools that support the bottom-up design in biological engineering.

Part, architecture or context	Description	Reference
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]
Promoter-RBS pairs	Strength prediction tool for sigma70 promoter-RBS pairs, using an artificial neural network.	[8]
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]
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]
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]
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]
Architecture	Protein expression prediction for the first gene of an operon, given the downstream mRNA length, via a linear regression model.	[22]
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]