We have developed a user-friendly website featuring a publicly accessible web tool that serves as the interface for our dry-lab model and optimizer. Upon accessing the website, users are presented with a choice between two distinct tools: the Relative Expression Prediction Tool and the RBS Optimization Tool.

The Relative Expression Prediction Tool accepts the following input parameters: RBS Sequence, Coding Sequence, Temperature, Gram Stain of Chassis, 16S rRNA Sequence. It subsequently provides the relative expression level in terms of the RBS rate, represented on a logarithmic scale.

On the other hand, the RBS Optimization Tool also receives the following inputs: RBS Sequence, Coding Sequence, Target Relative Expression, Temperature, Gram Stain of Chassis, and 16S rRNA Sequence. This tool then furnishes the optimized RBS sequence along with its corresponding predicted (logarithmically scaled) relative expression level.

We rigorously evaluated our tool by subjecting it to a comprehensive benchmarking exercise against cutting-edge rate prediction tools, including the acclaimed RBS Calculator v2.1 by Salis et al., RBS Designer, UTR Designer, EMOPEC, and others. This evaluation was conducted on a meticulously curated dataset comprising 16,779 mRNA sequences with quantified expression levels, as assembled by Reis and Salis.

The outcomes of our evaluation unequivocally establish that our tool has decisively surpassed the accuracy of the foremost calculators worldwide, doing so by a substantial margin.

Our model has the best performance among those compared, despite the inherent noise present in a FlowSeq experiment.

BioBrick parts

We have created the following BioBrick parts generated by our RBS Optimization tool which have been characterized by conducting fluorescence assays in the wet lab.

Modelling engineered Lactococcus lactis for homocystinuria treatment
We developed a novel kinetic model for engineered Lactococcus lactis to be used as a probiotic for the treatment of homocystinuria. Apart from the addition of the genes required to metabolize methionine to cysteine, the model incorporated the MazEF toxin-antitoxin system as a kill-switch.

Kushi Athreya, a third-year BSc. Biotechnology student of Shiv Nadar Institute of Imminence has collaborated with IITM iGEM 2023 to continue the ChassiDex project. The ChassiDex project was started by Team iGEM IIT-Madras as an online database of host organisms and related information. The resource also includes host specific software tools and lists guidelines to help start with new host organisms.

Kushi played a pivotal role in compiling data related to various Model and GRAS organisms, including animal models (Zebrafish, Drosophila, C. elegans ), plant models (Tobacco, Arabidopsis), and other model organisms (Agrobacterium, Bacillus licheniformis, Trichoderma spp) that we wished to include in the ChassiDex website. This was followed by uploading the collated data onto a publically accessible GitHub platform as a part of the ChassiDex project.

The database can be accessed through the following link:

https://chassidex.github.io/

She has been very determined and optimistic, fostering collaboration throughout our exchange. This experience has been very positive and enriching at both ends. We look forward to future collaborations.